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Mayourian J, La Cava WG, Vaid A, Nadkarni GN, Ghelani SJ, Mannix R, Geva T, Dionne A, Alexander ME, Duong SQ, Triedman JK. Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation 2024; 149:917-931. [PMID: 38314583 PMCID: PMC10948312 DOI: 10.1161/circulationaha.123.067750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 12/20/2023] [Indexed: 02/06/2024]
Abstract
BACKGROUND Artificial intelligence-enhanced ECG analysis shows promise to detect ventricular dysfunction and remodeling in adult populations. However, its application to pediatric populations remains underexplored. METHODS A convolutional neural network was trained on paired ECG-echocardiograms (≤2 days apart) from patients ≤18 years of age without major congenital heart disease to detect human expert-classified greater than mild left ventricular (LV) dysfunction, hypertrophy, and dilation (individually and as a composite outcome). Model performance was evaluated on single ECG-echocardiogram pairs per patient at Boston Children's Hospital and externally at Mount Sinai Hospital using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). RESULTS The training cohort comprised 92 377 ECG-echocardiogram pairs (46 261 patients; median age, 8.2 years). Test groups included internal testing (12 631 patients; median age, 8.8 years; 4.6% composite outcomes), emergency department (2830 patients; median age, 7.7 years; 10.0% composite outcomes), and external validation (5088 patients; median age, 4.3 years; 6.1% composite outcomes) cohorts. Model performance was similar on internal test and emergency department cohorts, with model predictions of LV hypertrophy outperforming the pediatric cardiologist expert benchmark. Adding age and sex to the model added no benefit to model performance. When using quantitative outcome cutoffs, model performance was similar between internal testing (composite outcome: AUROC, 0.88, AUPRC, 0.43; LV dysfunction: AUROC, 0.92, AUPRC, 0.23; LV hypertrophy: AUROC, 0.88, AUPRC, 0.28; LV dilation: AUROC, 0.91, AUPRC, 0.47) and external validation (composite outcome: AUROC, 0.86, AUPRC, 0.39; LV dysfunction: AUROC, 0.94, AUPRC, 0.32; LV hypertrophy: AUROC, 0.84, AUPRC, 0.25; LV dilation: AUROC, 0.87, AUPRC, 0.33), with composite outcome negative predictive values of 99.0% and 99.2%, respectively. Saliency mapping highlighted ECG components that influenced model predictions (precordial QRS complexes for all outcomes; T waves for LV dysfunction). High-risk ECG features include lateral T-wave inversion (LV dysfunction), deep S waves in V1 and V2 and tall R waves in V6 (LV hypertrophy), and tall R waves in V4 through V6 (LV dilation). CONCLUSIONS This externally validated algorithm shows promise to inexpensively screen for LV dysfunction and remodeling in children, which may facilitate improved access to care by democratizing the expertise of pediatric cardiologists.
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Affiliation(s)
- Joshua Mayourian
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - William G. La Cava
- Computational Health Informatics Program, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Sunil J. Ghelani
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Rebekah Mannix
- Department of Medicine, Division of Emergency Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Tal Geva
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Audrey Dionne
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Mark E. Alexander
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Son Q. Duong
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - John K. Triedman
- Department of Cardiology, Boston Children’s Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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Khunte A, Sangha V, Oikonomou EK, Dhingra LS, Aminorroaya A, Coppi A, Shankar SV, Mortazavi BJ, Bhatt DL, Krumholz HM, Nadkarni GN, Vaid A, Khera R. Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care. medRxiv 2024:2024.02.17.24302976. [PMID: 38405776 PMCID: PMC10889032 DOI: 10.1101/2024.02.17.24302976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Timely and accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and clinically managing patients. Current workflows rely on a computerized ECG interpretation using rule-based tools built into the ECG signal acquisition systems with limited accuracy and flexibility. In low-resource settings, specialists must review every single ECG for such decisions, as these computerized interpretations are not available. Additionally, high-quality interpretations are even more essential in such low-resource settings as there is a higher burden of accuracy for automated reads when access to experts is limited. Artificial Intelligence (AI)-based systems have the prospect of greater accuracy yet are frequently limited to a narrow range of conditions and do not replicate the full diagnostic range. Moreover, these models often require raw signal data, which are unavailable to physicians and necessitate costly technical integrations that are currently limited. To overcome these challenges, we developed and validated a format-independent vision encoder-decoder model - ECG-GPT - that can generate free-text, expert-level diagnosis statements directly from ECG images. The model shows robust performance, validated on 2.6 million ECGs across 6 geographically distinct health settings: (1) 2 large and diverse US health systems- Yale-New Haven and Mount Sinai Health Systems, (2) a consecutive ECG dataset from a central ECG repository from Minas Gerais, Brazil, (3) the prospective cohort study, UK Biobank, (4) a Germany-based, publicly available repository, PTB-XL, and (5) a community hospital in Missouri. The model demonstrated consistently high performance (AUROC≥0.81) across a wide range of rhythm and conduction disorders. This can be easily accessed via a web-based application capable of receiving ECG images and represents a scalable and accessible strategy for generating accurate, expert-level reports from images of ECGs, enabling accurate triage of patients globally, especially in low-resource settings.
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Affiliation(s)
- Akshay Khunte
- Department of Computer Science, Yale University, New Haven, CT
| | - Veer Sangha
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Engineering Science, Oxford University, Oxford, UK
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Arya Aminorroaya
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Andreas Coppi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX
| | - Sumukh Vasisht Shankar
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
| | - Bobak J Mortazavi
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
| | - Deepak L Bhatt
- Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
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3
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Gulamali F, Jayaraman P, Sawant AS, Desman J, Fox B, Chang A, Soong BY, Arivazaghan N, Reynolds AS, Duong SQ, Vaid A, Kovatch P, Freeman R, Hofer IS, Sakhuja A, Dangayach NS, Reich DS, Charney AW, Nadkarni GN. Derivation, External Validation and Clinical Implications of a deep learning approach for intracranial pressure estimation using non-cranial waveform measurements. medRxiv 2024:2024.01.30.24301974. [PMID: 38352556 PMCID: PMC10863000 DOI: 10.1101/2024.01.30.24301974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Importance Increased intracranial pressure (ICP) is associated with adverse neurological outcomes, but needs invasive monitoring. Objective Development and validation of an AI approach for detecting increased ICP (aICP) using only non-invasive extracranial physiological waveform data. Design Retrospective diagnostic study of AI-assisted detection of increased ICP. We developed an AI model using exclusively extracranial waveforms, externally validated it and assessed associations with clinical outcomes. Setting MIMIC-III Waveform Database (2000-2013), a database derived from patients admitted to an ICU in an academic Boston hospital, was used for development of the aICP model, and to report association with neurologic outcomes. Data from Mount Sinai Hospital (2020-2022) in New York City was used for external validation. Participants Patients were included if they were older than 18 years, and were monitored with electrocardiograms, arterial blood pressure, respiratory impedance plethysmography and pulse oximetry. Patients who additionally had intracranial pressure monitoring were used for development (N=157) and external validation (N=56). Patients without intracranial monitors were used for association with outcomes (N=1694). Exposures Extracranial waveforms including electrocardiogram, arterial blood pressure, plethysmography and SpO2. Main Outcomes and Measures Intracranial pressure > 15 mmHg. Measures were Area under receiver operating characteristic curves (AUROCs), sensitivity, specificity, and accuracy at threshold of 0.5. We calculated odds ratios and p-values for phenotype association. Results The AUROC was 0.91 (95% CI, 0.90-0.91) on testing and 0.80 (95% CI, 0.80-0.80) on external validation. aICP had accuracy, sensitivity, and specificity of 73.8% (95% CI, 72.0%-75.6%), 99.5% (95% CI 99.3%-99.6%), and 76.9% (95% CI, 74.0-79.8%) on external validation. A ten-percentile increment was associated with stroke (OR=2.12; 95% CI, 1.27-3.13), brain malignancy (OR=1.68; 95% CI, 1.09-2.60), subdural hemorrhage (OR=1.66; 95% CI, 1.07-2.57), intracerebral hemorrhage (OR=1.18; 95% CI, 1.07-1.32), and procedures like percutaneous brain biopsy (OR=1.58; 95% CI, 1.15-2.18) and craniotomy (OR = 1.43; 95% CI, 1.12-1.84; P < 0.05 for all). Conclusions and Relevance aICP provides accurate, non-invasive estimation of increased ICP, and is associated with neurological outcomes and neurosurgical procedures in patients without intracranial monitoring.
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Affiliation(s)
- Faris Gulamali
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Pushkala Jayaraman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ashwin S. Sawant
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jacob Desman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Benjamin Fox
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Annie Chang
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Brian Y. Soong
- Department of Medical Education, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Naveen Arivazaghan
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexandra S. Reynolds
- Department of Neurosurgery and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Son Q Duong
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Patricia Kovatch
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Robert Freeman
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ira S. Hofer
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ankit Sakhuja
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Neha S. Dangayach
- Department of Neurosurgery and Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - David S. Reich
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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4
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Duong SQ, Vaid A, My VTH, Butler LR, Lampert J, Pass RH, Charney AW, Narula J, Khera R, Sakhuja A, Greenspan H, Gelb BD, Do R, Nadkarni GN. Quantitative Prediction of Right Ventricular Size and Function From the ECG. J Am Heart Assoc 2024; 13:e031671. [PMID: 38156471 PMCID: PMC10863807 DOI: 10.1161/jaha.123.031671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/20/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored. METHODS AND RESULTS We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m2), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938). We fine-tuned in a multicenter health system (MSHoriginal [Mount Sinai Hospital]; n=3019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance with area under the receiver operating characteristic curve for categorical and mean absolute error for continuous measures overall and in key subgroups. We assessed the association of RVEF prediction with transplant-free survival with Cox proportional hazards models. The prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. The prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model area under the receiver operating characteristic curve for UKBB/MSHoriginal/MSHvalidation cohorts was 0.91/0.81/0.92, respectively. MSHoriginal mean absolute error was RVEF=7.8% and RVEDV=17.6 mL/m2. The performance of the RVEF model was similar in key subgroups including with and without left ventricular dysfunction. Over a median follow-up of 2.3 years, predicted RVEF was associated with adjusted transplant-free survival (hazard ratio, 1.40 for each 10% decrease; P=0.031). CONCLUSIONS Deep learning-ECG analysis can identify significant cardiac magnetic resonance imaging RV dysfunction and dilation with good performance. Predicted RVEF is associated with clinical outcome.
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Affiliation(s)
- Son Q. Duong
- Division of Pediatric Cardiology, Department of PediatricsIcahn School of Medicine at Mount SinaiNew YorkNY
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Vy Thi Ha My
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Liam R. Butler
- Division of Pediatric Cardiology, Department of PediatricsIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Joshua Lampert
- Helmsley Center for Electrophysiology at The Mount Sinai HospitalNew YorkNY
| | - Robert H. Pass
- Division of Pediatric Cardiology, Department of PediatricsIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Alexander W. Charney
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal MedicineYale School of MedicineNew HavenCT
- Section of Health Informatics, Department of BiostatisticsYale School of Public HealthNew HavenCT
- Biomedical Informatics and Data Science, Yale School of MedicineNew HavenCT
- Center for Outcomes Research and Evaluation, Yale‐New Haven HospitalNew HavenCT
| | - Ankit Sakhuja
- Division of Cardiovascular Critical Care, Department of Cardiac and Thoracic SurgeryWest Virginia UniversityMorgantownWV
| | - Hayit Greenspan
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount SinaiNew YorkNY
| | - Bruce D. Gelb
- Division of Pediatric Cardiology, Department of PediatricsIcahn School of Medicine at Mount SinaiNew YorkNY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Ron Do
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNY
- The Division of Data Driven and Digital Medicine (D3M), Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
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5
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Jayaraman P, Rajagopal M, Paranjpe I, Liharska L, Suarez-Farinas M, Thompson R, Del Valle DM, Beckmann N, Oh W, Gulamali FF, Kauffman J, Gonzalez-Kozlova E, Dellepiane S, Vasquez-Rios G, Vaid A, Jiang J, Chen A, Sakhuja A, Chen S, Kenigsberg E, He JC, Coca SG, Chan L, Schadt E, Merad M, Kim-Schulze S, Gnjatic S, Tsalik E, Langley R, Charney AW, Nadkarni GN. Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection. medRxiv 2023:2023.10.25.23297469. [PMID: 37961671 PMCID: PMC10635190 DOI: 10.1101/2023.10.25.23297469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we identified 164/2635 (6.2%) of the significantly differentiated genes associated with overall decrease in long-term kidney function. The strongest associations were 'autophagy', 'renal impairment via fibrosis', and 'cardiac structure and function'. Conclusions We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures, indicating generalizability in therapeutic approaches. SIGNIFICANCE STATEMENT Peripheral transcriptomic findings in acute and long-term kidney dysfunction after hospitalization for SARS-CoV2 infection are unclear. We evaluated peripheral blood molecular signatures in AKI from COVID-19 (COVID-AKI) and their association with long-term kidney dysfunction using the largest hospitalized cohort with transcriptomic data. Analysis of 283 hospitalized patients of whom 37% had AKI, highlighted the contribution of mitochondrial dysfunction driven by endoplasmic reticulum stress in the acute stages. Subsequently, long-term kidney function decline exhibits significant associations with markers of cardiac structure and function and immune mediated dysregulation. There were similar biomolecular signatures in other inflammatory states, such as sepsis. This enhances the potential for repurposing and generalizability in therapeutic approaches.
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6
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Vaid A, Sawant A, Suarez-Farinas M, Lee J, Kaul S, Kovatch P, Freeman R, Jiang J, Jayaraman P, Fayad Z, Argulian E, Lerakis S, Charney AW, Wang F, Levin M, Glicksberg B, Narula J, Hofer I, Singh K, Nadkarni GN. Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings : A Simulation Study. Ann Intern Med 2023; 176:1358-1369. [PMID: 37812781 DOI: 10.7326/m23-0949] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Substantial effort has been directed toward demonstrating uses of predictive models in health care. However, implementation of these models into clinical practice may influence patient outcomes, which in turn are captured in electronic health record data. As a result, deployed models may affect the predictive ability of current and future models. OBJECTIVE To estimate changes in predictive model performance with use through 3 common scenarios: model retraining, sequentially implementing 1 model after another, and intervening in response to a model when 2 are simultaneously implemented. DESIGN Simulation of model implementation and use in critical care settings at various levels of intervention effectiveness and clinician adherence. Models were either trained or retrained after simulated implementation. SETTING Admissions to the intensive care unit (ICU) at Mount Sinai Health System (New York, New York) and Beth Israel Deaconess Medical Center (Boston, Massachusetts). PATIENTS 130 000 critical care admissions across both health systems. INTERVENTION Across 3 scenarios, interventions were simulated at varying levels of clinician adherence and effectiveness. MEASUREMENTS Statistical measures of performance, including threshold-independent (area under the curve) and threshold-dependent measures. RESULTS At fixed 90% sensitivity, in scenario 1 a mortality prediction model lost 9% to 39% specificity after retraining once and in scenario 2 a mortality prediction model lost 8% to 15% specificity when created after the implementation of an acute kidney injury (AKI) prediction model; in scenario 3, models for AKI and mortality prediction implemented simultaneously, each led to reduced effective accuracy of the other by 1% to 28%. LIMITATIONS In real-world practice, the effectiveness of and adherence to model-based recommendations are rarely known in advance. Only binary classifiers for tabular ICU admissions data were simulated. CONCLUSION In simulated ICU settings, a universally effective model-updating approach for maintaining model performance does not seem to exist. Model use may have to be recorded to maintain viability of predictive modeling. PRIMARY FUNDING SOURCE National Center for Advancing Translational Sciences.
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Affiliation(s)
- Akhil Vaid
- Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.)
| | - Ashwin Sawant
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.S.)
| | - Mayte Suarez-Farinas
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York (M.S., J.L.)
| | - Juhee Lee
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York (M.S., J.L.)
| | - Sanjeev Kaul
- Department of Surgery, Hackensack Meridian School of Medicine, Nutley, New Jersey (S.K.)
| | - Patricia Kovatch
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York (P.K., B.G.)
| | - Robert Freeman
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (R.F.)
| | - Joy Jiang
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (J.J.)
| | - Pushkala Jayaraman
- Division of Data-Driven and Digital Medicine, Department of Medicine, and The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (A.V., P.J.)
| | - Zahi Fayad
- BioMedical Engineering and Imaging Institute and Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (Z.F.)
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, and Department of Surgery, Hackensack Meridian School of Medicine, Nutley, New Jersey (A.W.C.)
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York (F.W.)
| | - Matthew Levin
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (M.L.)
| | - Benjamin Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York (P.K., B.G.)
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York (E.A., S.L., J.N.)
| | - Ira Hofer
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York (I.H.)
| | - Karandeep Singh
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan (K.S.)
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine, Department of Medicine; The Charles Bronfman Institute of Personalized Medicine; and Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (G.N.N.)
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7
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Brin D, Sorin V, Vaid A, Soroush A, Glicksberg BS, Charney AW, Nadkarni G, Klang E. Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments. Sci Rep 2023; 13:16492. [PMID: 37779171 PMCID: PMC10543445 DOI: 10.1038/s41598-023-43436-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/23/2023] [Indexed: 10/03/2023] Open
Abstract
The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models' consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT's 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine.
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Affiliation(s)
- Dana Brin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel.
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel.
| | - Vera Sorin
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ali Soroush
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eyal Klang
- Department of Diagnostic Imaging, Chaim Sheba Medical Center, Ramat Gan, Israel
- Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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8
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Vaid A, Takkavatakarn K, Divers J, Charytan DM, Chan L, Nadkarni GN. Deep Learning on Electrocardiograms for Prediction of In-hospital Intradialytic Hypotension in Patients with ESKD. Kidney360 2023; 4:e1293-e1296. [PMID: 37418626 PMCID: PMC10547223 DOI: 10.34067/kid.0000000000000208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023]
Abstract
Intradialytic hypotension is common in patients who are on hemodialysis. We applied deep learning techniques to ECGs to predict patients at risk of IDH. The performance of the model was good with an AUC of 0.763 and AUPRC of 0.35.
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Affiliation(s)
- Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kullaya Takkavatakarn
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jasmin Divers
- Division of Health Services, Department of Medicine, NYU (New York University) Long Island School of Medicine, Mineola, New York
| | - David M. Charytan
- Division of Nephrology, Department of Medicine, NYU (New York University) Grossman School of Medicine and NYU Langone Medical Center, New York, New York
| | - Lili Chan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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9
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Lampert J, Vaid A, Whang W, Koruth J, Miller MA, Langan MN, Musikantow D, Turagam M, Maan A, Kawamura I, Dukkipati S, Nadkarni GN, Reddy VY. A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes. JACC Clin Electrophysiol 2023; 9:1437-1451. [PMID: 37480862 DOI: 10.1016/j.jacep.2023.05.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/17/2023] [Accepted: 05/24/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict left ventricular ejection fraction (LVEF) reduction in patients with PVCs from a 12-lead electrocardiogram (ECG). OBJECTIVES This study aims to assess a deep-learning model to predict cardiomyopathy among patients with PVCs. METHODS We used electronic medical records from 5 hospitals and identified ECGs from adults with documented PVCs. Internal training and testing were performed at one hospital. External validation was performed with the others. The primary outcome was first diagnosis of LVEF ≤40% within 6 months. The dataset included 383,514 ECGs, of which 14,241 remained for analysis. We analyzed area under the receiver operating curves and explainability plots for representative patients, algorithm prediction, PVC burden, and demographics in a multivariable Cox model to assess independent predictors for cardiomyopathy. RESULTS Among the 14,241-patient cohort (age 67.6 ± 14.8 years; female 43.8%; White 29.5%, Black 8.6%, Hispanic 6.5%, Asian 2.2%), 22.9% experienced reductions in LVEF to ≤40% within 6 months. The model predicted reductions in LVEF to ≤40% with area under the receiver operating curve of 0.79 (95% CI: 0.77-0.81). The gradient weighted class activation map explainability framework highlighted the sinus rhythm QRS complex-ST segment. In patients who underwent successful PVC ablation there was a post-ablation improvement in LVEF with resolution of cardiomyopathy in most (89%) patients. CONCLUSIONS Deep-learning on the 12-lead ECG alone can accurately predict new-onset cardiomyopathy in patients with PVCs independent of PVC burden. Model prediction performed well across sex and race, relying on the QRS complex/ST-segment in sinus rhythm, not PVC morphology.
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Affiliation(s)
- Joshua Lampert
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - William Whang
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jacob Koruth
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Marc A Miller
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Marie-Noelle Langan
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Daniel Musikantow
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mohit Turagam
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Abhishek Maan
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Iwanari Kawamura
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Srinivas Dukkipati
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Division of Data Driven and Digital Medicine (D3M), The Charles Bronfman Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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10
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Lampert J, Power D, Havaldar S, Govindarajulu U, Kawamura I, Maan A, Miller MA, Menon K, Koruth J, Whang W, Bagiella E, Bayes-Genis A, Musikantow D, Turagam M, Bayes de Luna A, Halperin J, Dukkipati SR, Vaid A, Nadkarni G, Glicksberg B, Fuster V, Reddy VY. Interatrial Block Association With Adverse Cardiovascular Outcomes in Patients Without a History of Atrial Fibrillation. JACC Clin Electrophysiol 2023; 9:1804-1815. [PMID: 37354170 DOI: 10.1016/j.jacep.2023.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Interatrial block (IAB) is associated with thromboembolism and atrial arrhythmias. However, prior studies included small patient cohorts so it remains unclear whether IAB predicts adverse outcomes particularly in context of atrial fibrillation (AF)/atrial flutter (AFL). OBJECTIVES This study sought to determine whether IAB portends increased stroke risk in a large cohort in the presence or absence of AFAF/AFL. METHODS We performed a 5-center retrospective analysis of 4,837,989 electrocardiograms (ECGs) from 1,228,291 patients. IAB was defined as P-wave duration ≥120 ms in leads II, III, or aVF. Measurements were extracted as .XML files. After excluding patients with prior AF/AFL, 1,825,958 ECGs from 458,994 patients remained. Outcomes were analyzed using restricted mean survival time analysis and restricted mean time lost. RESULTS There were 86,317 patients with IAB and 355,032 patients without IAB. IAB prevalence in the cohort was 19.6% and was most common in Black (26.1%), White (20.9%), and Hispanic (18.5%) patients and least prevalent in Native Americans (9.2%). IAB was independently associated with increased stroke probability (restricted mean time lost ratio coefficient [RMTLRC]: 1.43; 95% CI: 1.35-1.51; tau = 1,895), mortality (RMTLRC: 1.14; 95% CI: 1.07-1.21; tau = 1,924), heart failure (RMTLRC: 1.94; 95% CI: 1.83-2.04; tau = 1,921), systemic thromboembolism (RMTLRC: 1.62; 95% CI: 1.53-1.71; tau = 1,897), and incident AF/AFL (RMTLRC: 1.16; 95% CI: 1.10-1.22; tau = 1,888). IAB was not associated with stroke in patients with pre-existing AF/AFL. CONCLUSIONS IAB is independently associated with stroke in patients with no history of AF/AFL even after adjustment for incident AF/AFL and CHA2DS2-VASc score. Patients are at increased risk of stroke even when AF/AFL is not identified.
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Affiliation(s)
- Joshua Lampert
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA. https://twitter.com/joshuamlampertmd
| | - David Power
- Mount Sinai Heart, Mount Sinai Hospital, New York, New York, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Usha Govindarajulu
- Center for Biostatistics, Department of Population Health, Mount Sinai Hospital, New York, New York, USA
| | - Iwanari Kawamura
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Abhishek Maan
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Marc A Miller
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Kartikeya Menon
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jacob Koruth
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - William Whang
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Emilia Bagiella
- Center for Biostatistics, Department of Population Health, Mount Sinai Hospital, New York, New York, USA
| | - Antoni Bayes-Genis
- Heart Institute, Hospital Universitario Germans trias I Pujol, Badalona, Spain
| | - Daniel Musikantow
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Mohit Turagam
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Antoni Bayes de Luna
- Cardiovascular Research Foundation, Cardiovascular ICCC-Program, Research Institute Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau, Barcelona, Spain
| | | | - Srinivas R Dukkipati
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA
| | - Akhil Vaid
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Valentin Fuster
- Mount Sinai Heart, Mount Sinai Hospital, New York, New York, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Mount Sinai Hospital, New York, New York, USA.
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11
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Paranjpe I, Jayaraman P, Su CY, Zhou S, Chen S, Thompson R, Del Valle DM, Kenigsberg E, Zhao S, Jaladanki S, Chaudhary K, Ascolillo S, Vaid A, Gonzalez-Kozlova E, Kauffman J, Kumar A, Paranjpe M, Hagan RO, Kamat S, Gulamali FF, Xie H, Harris J, Patel M, Argueta K, Batchelor C, Nie K, Dellepiane S, Scott L, Levin MA, He JC, Suarez-Farinas M, Coca SG, Chan L, Azeloglu EU, Schadt E, Beckmann N, Gnjatic S, Merad M, Kim-Schulze S, Richards B, Glicksberg BS, Charney AW, Nadkarni GN. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection. Commun Med (Lond) 2023; 3:81. [PMID: 37308534 PMCID: PMC10258469 DOI: 10.1038/s43856-023-00307-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 05/18/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.
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Affiliation(s)
- Ishan Paranjpe
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Pushkala Jayaraman
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chen-Yang Su
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Computer Science, Quantitative Life Sciences, McGill University, Montreal, QC, Canada
| | - Sirui Zhou
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Steven Chen
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan Thompson
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Diane Marie Del Valle
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ephraim Kenigsberg
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shan Zhao
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suraj Jaladanki
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kumardeep Chaudhary
- Clinical Informatics, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi, India
| | - Steven Ascolillo
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Gonzalez-Kozlova
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Justin Kauffman
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arvind Kumar
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manish Paranjpe
- Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Ross O Hagan
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Kamat
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Faris F Gulamali
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hui Xie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joceyln Harris
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manishkumar Patel
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kimberly Argueta
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Craig Batchelor
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Nie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sergio Dellepiane
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leisha Scott
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew A Levin
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John Cijiang He
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mayte Suarez-Farinas
- Department of Biostatistics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven G Coca
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lili Chan
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Evren U Azeloglu
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Noam Beckmann
- The Mount Sinai Clinical Intelligence Center (MSCIC), The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miram Merad
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Computer Science, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Department of Twin Research, King's College London, London, GB, UK
| | | | - Alexander W Charney
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine (CBIPM), Division of Data Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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12
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Vaid A, Jiang J, Sawant A, Lerakis S, Argulian E, Ahuja Y, Lampert J, Charney A, Greenspan H, Narula J, Glicksberg B, Nadkarni GN. A foundational vision transformer improves diagnostic performance for electrocardiograms. NPJ Digit Med 2023; 6:108. [PMID: 37280346 PMCID: PMC10242218 DOI: 10.1038/s41746-023-00840-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023] Open
Abstract
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create a vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We find that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. We also find that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Domain specific pre-trained transformer models may exceed the classification performance of models trained on natural images especially in very low data regimes. The combination of the architecture and such pre-training allows for more accurate, granular explainability of model predictions.
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Affiliation(s)
- Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA.
| | - Joy Jiang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ashwin Sawant
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuri Ahuja
- Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Joshua Lampert
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander Charney
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hayit Greenspan
- Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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13
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Duong SQ, Vaid A, Vy HMT, Butler LR, Lampert J, Pass RH, Charney AW, Narula J, Khera R, Greenspan H, Gelb BD, Do R, Nadkarni G. Quantitative prediction of right ventricular and size and function from the electrocardiogram. medRxiv 2023:2023.04.25.23289130. [PMID: 37162979 PMCID: PMC10168487 DOI: 10.1101/2023.04.25.23289130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep-learning enabled 12-lead electrocardiogram analysis (DL-ECG) for estimation of RV size or function is unexplored. Methods We trained a DL-ECG model to predict RV dilation (RVEDV>120 mL/m2), RV dysfunction (RVEF≤40%), and numerical RVEDV/RVEF from 12-lead ECG paired with reference-standard cardiac MRI (cMRI) volumetric measurements in UK biobank (UKBB; n=42,938). We fine-tuned in a multi-center health system (MSHoriginal; n=3,019) with prospective validation over 4 months (MSHvalidation; n=115). We evaluated performance using area under the receiver operating curve (AUROC) for categorical and mean absolute error (MAE) for continuous measures overall and in key subgroups. We assessed association of RVEF prediction with transplant-free survival with Cox proportional hazards models. Results Prevalence of RV dysfunction for UKBB/MSHoriginal/MSHvalidation cohorts was 1.0%/18.0%/15.7%, respectively. RV dysfunction model AUROC for UKBB/MSHoriginal/MSHvalidation cohorts was 0.86/0.81/0.77, respectively. Prevalence of RV dilation for UKBB/MSHoriginal/MSHvalidation cohorts was 1.6%/10.6%/4.3%. RV dilation model AUROC for UKBB/MSHoriginal/MSHvalidation cohorts 0.91/0.81/0.92, respectively. MSHoriginal MAE was RVEF=7.8% and RVEDV=17.6 ml/m2. Performance was similar in key subgroups including with and without left ventricular dysfunction. Over median follow-up of 2.3 years, predicted RVEF was independently associated with composite outcome (HR 1.37 for each 10% decrease, p=0.046). Conclusions DL-ECG analysis can accurately identify significant RV dysfunction and dilation both overall and in key subgroups. Predicted RVEF is independently associated with clinical outcome.
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Affiliation(s)
- Son Q Duong
- Division of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ha My Thi Vy
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Liam R Butler
- Division of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Joshua Lampert
- Helmsley Center for Electrophysiology at The Mount Sinai Hospital, New York, NY
| | - Robert H Pass
- Division of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Alexander W Charney
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
- Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT
| | - Hayit Greenspan
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Bruce D Gelb
- Division of Pediatric Cardiology, Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ron Do
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish Nadkarni
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Division of Data Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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14
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Park JK, Petrazzini BO, Saha A, Vaid A, Vy HMT, Márquez‐Luna C, Chan L, Nadkarni GN, Do R. Machine Learning Identifies Plasma Metabolites Associated With Heart Failure in Underrepresented Populations With the TTR V122I Variant. J Am Heart Assoc 2023; 12:e027736. [PMID: 37042260 PMCID: PMC10227245 DOI: 10.1161/jaha.122.027736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 02/22/2023] [Indexed: 04/13/2023]
Affiliation(s)
- Joshua K. Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Medical Scientist Training ProgramIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Ben O. Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Aparna Saha
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Division of Data‐Driven and Digital Medicine (D3M)Icahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Ha My T. Vy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Carla Márquez‐Luna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Lili Chan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- The Mount Sinai Clinical Intelligence CenterIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Division of Data‐Driven and Digital Medicine (D3M)Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- The Mount Sinai Clinical Intelligence CenterIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount SinaiNew YorkNYUSA
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15
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Nadkarni G, Paranjpe I, Jayaraman P, Su CY, Zhou S, Chen S, Valle DD, Thompson R, Kenigsberg E, Zhao S, Jaladanki S, Chaudhary K, Ascolillo S, Vaid A, Gonzalez-Kozlova E, Kumar A, Paranjpe M, O'Hagan R, Kamat S, Gulamali F, Kauffman J, Xie H, Harris J, Patel M, Argueta K, Batchelor C, Nie K, Dellepiane S, Scott L, Levin M, He J, Suárez-Fariñas M, Coca S, Chan L, Azeloglu E, Schadt E, Beckmann N, Gnjatic S, Merad M, Kim-Schulze S, Richards JB, Glicksberg B, Charney A. Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection. Res Sq 2023:rs.3.rs-2379226. [PMID: 36993735 PMCID: PMC10055503 DOI: 10.21203/rs.3.rs-2379226/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). Results We demonstrate that COVID-AKI is associated with increased markers of tubular injury ( NGAL ) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2 , trefoil factor 3 , transmembrane emp24 domain-containing protein 10 , and cystatin-C indicating tubular dysfunction and injury. Conclusions Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.
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16
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Vaid A, Argulian E, Lerakis S, Beaulieu-Jones BK, Krittanawong C, Klang E, Lampert J, Reddy VY, Narula J, Nadkarni GN, Glicksberg BS. Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction. Commun Med (Lond) 2023; 3:24. [PMID: 36788316 PMCID: PMC9929085 DOI: 10.1038/s43856-023-00240-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/09/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making. METHODS In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients. We developed a Natural Language Processing pipeline to extract ground-truth labels about valvular status and paired these to Electrocardiograms (ECGs). We developed and externally validated deep learning models capable of detecting valvular disease, in addition to considering scenarios of clinical deployment. RESULTS We use 617,338 ECGs paired to transthoracic echocardiograms from 123,096 patients to develop a deep learning model for detection of Mitral Regurgitation. Area Under Receiver Operating Characteristic curve (AUROC) is 0.88 (95% CI:0.88-0.89) in internal testing, and 0.81 (95% CI:0.80-0.82) in external validation. To develop a model for detection of Aortic Stenosis, we use 617,338 Echo-ECG pairs for 128,628 patients. AUROC is 0.89 (95% CI: 0.88-0.89) in internal testing, going to 0.86 (95% CI: 0.85-0.87) in external validation. The model's performance increases leading up to the time of the diagnostic echo, and it performs well in validation against requirement of Transcatheter Aortic Valve Replacement procedures. CONCLUSIONS Deep learning based tools can increase the amount of information extracted from ubiquitous investigations such as the ECG. Such tools are inexpensive, can help in earlier disease detection, and potentially improve prognosis.
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Affiliation(s)
- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Section of Biomedical Data Science, Department of Medicine, University of Chicago, Chicago, IL, USA
| | | | - Eyal Klang
- Sheba Medical Center, Department of Diagnostic Imaging, Tel Hashomer, Israel
- Sackler Medical School, Tel Aviv University, Tel Aviv, 52621, Israel
| | - Joshua Lampert
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine (D3M), The Department of Medicine, Icahn School of Medicine at Mount Siniai, New York, NY, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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17
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Petrazzini BO, Vaid A, Park JK, Marquez-Luna C, Vy HM, Saha A, Chaudhary K, Cho J, Chan L, Argulian E, Narula J, Nadkarni G, Do R. Short-term prediction of coronary artery disease using serum metabolomic patterns. Am Heart J Plus 2022; 24:100232. [PMID: 36788979 PMCID: PMC9924019 DOI: 10.1016/j.ahjo.2022.100232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Ben Omega Petrazzini
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joshua K. Park
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carla Marquez-Luna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ha My Vy
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aparna Saha
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kumardeep Chaudhary
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lili Chan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA,The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Corresponding authors at: Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, PO Box 1243, New York, NY 10029, USA. (G. Nadkarni), (R. Do)
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA,The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA,Corresponding authors at: Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, PO Box 1243, New York, NY 10029, USA. (G. Nadkarni), (R. Do)
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18
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Honarvar H, Agarwal C, Somani S, Vaid A, Lampert J, Wanyan T, Reddy VY, Nadkarni GN, Miotto R, Zitnik M, Wang F, Glicksberg BS. Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation. Cardiovascular Digital Health Journal 2022; 3:220-231. [PMID: 36310683 PMCID: PMC9596304 DOI: 10.1016/j.cvdhj.2022.07.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background Electrocardiogram (ECG) deep learning (DL) has promise to improve the outcomes of patients with cardiovascular abnormalities. In ECG DL, researchers often use convolutional neural networks (CNNs) and traditionally use the full duration of raw ECG waveforms that create redundancies in feature learning and result in inaccurate predictions with large uncertainties. Objective For enhancing these predictions, we introduced a sub-waveform representation that leverages the rhythmic pattern of ECG waveforms (data-centric approach) rather than changing the CNN architecture (model-centric approach). Results We applied the proposed representation to a population with 92,446 patients to identify left ventricular dysfunction. We found that the sub-waveform representation increases the performance metrics compared to the full-waveform representation. We observed a 2% increase for area under the receiver operating characteristic curve and 10% increase for area under the precision-recall curve. We also carefully examined three reliability components of explainability, interpretability, and fairness. We provided an explanation for enhancements obtained by heartbeat alignment mechanism. By developing a new scoring system, we interpreted the clinical relevance of ECG features and showed that sub-waveform representation further pushes the scores towards clinical predictions. Finally, we showed that the new representation significantly reduces prediction uncertainties within subgroups that contributes to individual fairness. Conclusion We expect that this added control over the granularity of ECG data will improve the DL modeling for new artificial intelligence technologies in the cardiovascular space.
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Affiliation(s)
- Hossein Honarvar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Chirag Agarwal
- Department of Biomedical Informatics, Harvard University, Boston, Massachusetts
| | - Sulaiman Somani
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Akhil Vaid
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joshua Lampert
- Helmsley Center for Cardiac Electrophysiology, Mount Sinai Hospital, New York, New York
| | - Tingyi Wanyan
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana
| | - Vivek Y. Reddy
- Helmsley Center for Cardiac Electrophysiology, Mount Sinai Hospital, New York, New York
| | - Girish N. Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, Boston, Massachusetts
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Address reprint requests and correspondence: Dr Benjamin S. Glicksberg, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029.
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19
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Vaid A, Jiang JJ, Sawant A, Singh K, Kovatch P, Charney AW, Charytan DM, Divers J, Glicksberg BS, Chan L, Nadkarni GN. Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis. Clin J Am Soc Nephrol 2022; 17:1017-1025. [PMID: 35667835 PMCID: PMC9269621 DOI: 10.2215/cjn.16481221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/27/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND AND OBJECTIVES Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic/procedure codes. We developed four models: (1) trained from scratch in patients on hemodialysis, (2) pretrained on a publicly available set of natural images (ImageNet), (3) pretrained on all patients not on hemodialysis, and (4) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of ≤40%, 41% to ≤50%, and >50%. We compared performance by area under the receiver operating characteristic curve. RESULTS We extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and n=18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of ≤40% (n=461), 41%-50% (n=398), and >50% (n=1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59). CONCLUSION A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period. PODCAST This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_06_CJN16481221.mp3.
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Affiliation(s)
- Akhil Vaid
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York.,The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Joy J Jiang
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ashwin Sawant
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Karandeep Singh
- Department of Learning Health Systems, University of Michigan Medical School, Ann Arbor, Michigan
| | - Patricia Kovatch
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander W Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - David M Charytan
- Division of Nephrology, Department of Medicine, New York University Langone Medical Center and New York University Grossman School of Medicine, New York, New York
| | - Jasmin Divers
- Division of Health Services, Department of Medicine, New York University Langone Medical Center, New York, New York
| | - Benjamin S Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York.,The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lili Chan
- Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N Nadkarni
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York .,The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Data Driven and Digital Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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20
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Paranjpe I, Jayaraman P, Su C, Zhou S, Chen S, Thompson R, Del Valle DM, Kenigsberg E, Zhao S, Jaladanki S, Chaudhary K, Ascolillo S, Vaid A, Kumar A, Kozlova E, Paranjpe M, O’hagan R, Kamat S, Gulamali FF, Kauffman J, Xie H, Harris J, Patel M, Argueta K, Batchelor C, Nie K, Dellepiane S, Scott L, Levin MA, He JC, Suarez-farinas M, Coca SG, Chan L, Azeloglu EU, Schadt E, Beckmann N, Gnjatic S, Merad M, Kim-schulze S, Richards B, Glicksberg BS, Charney AW, Nadkarni GN. Proteomic Characterization of Acute Kidney Injury in Patients Hospitalized with SARS-CoV2 Infection.. [PMID: 36093350 PMCID: PMC9460972 DOI: 10.1101/2021.12.09.21267548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractAcute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Using measurements of ∼4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N= 437), we identified 413 higher plasma abundances of protein targets and 40 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p <0.05). Of these, 62 proteins were validated in an external cohort (p <0.05, N =261). We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p <0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.
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21
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Wanyan T, Honarvar H, Jaladanki SK, Zang C, Naik N, Somani S, De Freitas JK, Paranjpe I, Vaid A, Zhang J, Miotto R, Wang Z, Nadkarni GN, Zitnik M, Azad A, Wang F, Ding Y, Glicksberg BS. Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients. Patterns (N Y) 2021; 2:100389. [PMID: 34723227 PMCID: PMC8542449 DOI: 10.1016/j.patter.2021.100389] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 09/12/2021] [Accepted: 10/21/2021] [Indexed: 12/30/2022]
Abstract
Deep Learning (DL) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing DL models for the coronavirus-disease 2019 (COVID-19) pandemic where data are highly class imbalanced. Conventional approaches in DL use cross-entropy loss (CEL) which often suffers from poor margin classification. We show that contrastive loss (CL) improves the performance of CEL especially in imbalanced electronic health records (EHR) data for COVID-19 analyses. We use a diverse EHR data set to predict three outcomes: mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over multiple time windows. To compare the performance of CEL and CL, models are tested on the full data set and a restricted data set. CL models consistently outperform CEL models with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC.
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Affiliation(s)
- Tingyi Wanyan
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Hossein Honarvar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suraj K Jaladanki
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Nidhi Naik
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sulaiman Somani
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jessica K De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ishan Paranjpe
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jing Zhang
- Renmin University of China, Beijing, China
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA
| | - Girish N Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard University, USA
| | - Ariful Azad
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Ying Ding
- Dell Medical School, University of Texas at Austin, Austin, TX, USA.,School of Informatics, University of Texas at Austin, Austin, TX, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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22
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Vaid A, Johnson KW, Badgeley MA, Somani SS, Bicak M, Landi I, Russak A, Zhao S, Levin MA, Freeman RS, Charney AW, Kukar A, Kim B, Danilov T, Lerakis S, Argulian E, Narula J, Nadkarni GN, Glicksberg BS. Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram. JACC Cardiovasc Imaging 2021; 15:395-410. [PMID: 34656465 PMCID: PMC8917975 DOI: 10.1016/j.jcmg.2021.08.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/29/2021] [Accepted: 08/05/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVES This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population. BACKGROUND Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right- ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only. METHODS A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation. RESULTS We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% < LVEF ≤50%, and LVEF >50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation. CONCLUSIONS DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.
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Affiliation(s)
- Akhil Vaid
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kipp W Johnson
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Sulaiman S Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mesude Bicak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Isotta Landi
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Adam Russak
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matthew A Levin
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert S Freeman
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alexander W Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Atul Kukar
- Department of Cardiology, Mount Sinai Queens Hospital, Astoria, New York, USA, and Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Cardiology, Mount Sinai West Hospital and Icahn School of Medicine at Mount Sinai, New York, New York USA
| | - Bette Kim
- Mount Sinai Beth Israel Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Tatyana Danilov
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stamatios Lerakis
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jagat Narula
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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23
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Dellepiane S, Vaid A, Jaladanki SK, Coca S, Fayad ZA, Charney AW, Bottinger EP, He JC, Glicksberg BS, Chan L, Nadkarni G. Acute Kidney Injury in Patients Hospitalized With COVID-19 in New York City: Temporal Trends From March 2020 to April 2021. Kidney Med 2021; 3:877-879. [PMID: 34368666 PMCID: PMC8325375 DOI: 10.1016/j.xkme.2021.06.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Affiliation(s)
- Sergio Dellepiane
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Akhil Vaid
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Suraj K. Jaladanki
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Steven Coca
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Zahi A. Fayad
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander W. Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Psychiatry (AWC), Icahn School of Medicine at Mount Sinai, New York, New York
| | - Erwin P. Bottinger
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - John Cijiang He
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Benjamin S. Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lili Chan
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish Nadkarni
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
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24
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Beckmann ND, Comella PH, Cheng E, Lepow L, Beckmann AG, Tyler SR, Mouskas K, Simons NW, Hoffman GE, Francoeur NJ, Del Valle DM, Kang G, Do A, Moya E, Wilkins L, Le Berichel J, Chang C, Marvin R, Calorossi S, Lansky A, Walker L, Yi N, Yu A, Chung J, Hartnett M, Eaton M, Hatem S, Jamal H, Akyatan A, Tabachnikova A, Liharska LE, Cotter L, Fennessy B, Vaid A, Barturen G, Shah H, Wang YC, Sridhar SH, Soto J, Bose S, Madrid K, Ellis E, Merzier E, Vlachos K, Fishman N, Tin M, Smith M, Xie H, Patel M, Nie K, Argueta K, Harris J, Karekar N, Batchelor C, Lacunza J, Yishak M, Tuballes K, Scott I, Kumar A, Jaladanki S, Agashe C, Thompson R, Clark E, Losic B, Peters L, Roussos P, Zhu J, Wang W, Kasarskis A, Glicksberg BS, Nadkarni G, Bogunovic D, Elaiho C, Gangadharan S, Ofori-Amanfo G, Alesso-Carra K, Onel K, Wilson KM, Argmann C, Bunyavanich S, Alarcón-Riquelme ME, Marron TU, Rahman A, Kim-Schulze S, Gnjatic S, Gelb BD, Merad M, Sebra R, Schadt EE, Charney AW. Downregulation of exhausted cytotoxic T cells in gene expression networks of multisystem inflammatory syndrome in children. Nat Commun 2021; 12:4854. [PMID: 34381049 PMCID: PMC8357784 DOI: 10.1038/s41467-021-24981-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 07/19/2021] [Indexed: 02/07/2023] Open
Abstract
Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and pathology of multiple organs in individuals under 21 years of age in the weeks following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Although an autoimmune pathogenesis has been proposed, the genes, pathways and cell types causal to this new disease remain unknown. Here we perform RNA sequencing of blood from patients with MIS-C and controls to find disease-associated genes clustered in a co-expression module annotated to CD56dimCD57+ natural killer (NK) cells and exhausted CD8+ T cells. A similar transcriptome signature is replicated in an independent cohort of Kawasaki disease (KD), the related condition after which MIS-C was initially named. Probing a probabilistic causal network previously constructed from over 1,000 blood transcriptomes both validates the structure of this module and reveals nine key regulators, including TBX21, a central coordinator of exhausted CD8+ T cell differentiation. Together, this unbiased, transcriptome-wide survey implicates downregulation of NK cells and cytotoxic T cell exhaustion in the pathogenesis of MIS-C.
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Affiliation(s)
- Noam D Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA.
| | - Phillip H Comella
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Esther Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren Lepow
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aviva G Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Scott R Tyler
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Konstantinos Mouskas
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole W Simons
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nancy J Francoeur
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | | | - Gurpawan Kang
- Department of Medicine, Division of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anh Do
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Emily Moya
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lillian Wilkins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jessica Le Berichel
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christie Chang
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Marvin
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sharlene Calorossi
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alona Lansky
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura Walker
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nancy Yi
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex Yu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonathan Chung
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Melody Eaton
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sandra Hatem
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hajra Jamal
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alara Akyatan
- Department of of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexandra Tabachnikova
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lora E Liharska
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Liam Cotter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian Fennessy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Guillermo Barturen
- Department of Medical Genomics, Center for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government (GENYO), Granada, Spain
| | - Hardik Shah
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ying-Chih Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shwetha Hara Sridhar
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Juan Soto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Swaroop Bose
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Kent Madrid
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Ethan Ellis
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Elyze Merzier
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Konstantinos Vlachos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Nataly Fishman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Manying Tin
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Melissa Smith
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Hui Xie
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manishkumar Patel
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Nie
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kimberly Argueta
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jocelyn Harris
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Neha Karekar
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Craig Batchelor
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jose Lacunza
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mahlet Yishak
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kevin Tuballes
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ieisha Scott
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arvind Kumar
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Suraj Jaladanki
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charuta Agashe
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan Thompson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
| | - Evan Clark
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bojan Losic
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren Peters
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panagiotis Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jun Zhu
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wenhui Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish Nadkarni
- Mount Sinai COVID Informatics Center, New York, NY, USA
- Department of Medicine, Mount Sinai, New York, NY, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, USA
- Charles Bronfman Institute for Personalized Medicine, New York, NY, USA
| | - Dusan Bogunovic
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Cordelia Elaiho
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sandeep Gangadharan
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - George Ofori-Amanfo
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kasey Alesso-Carra
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kenan Onel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Karen M Wilson
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carmen Argmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Supinda Bunyavanich
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marta E Alarcón-Riquelme
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas U Marron
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adeeb Rahman
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce D Gelb
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mindich Child Health and Development Institute at Mount Sinai, New York, NY, USA
| | - Miriam Merad
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA
- Black Family Stem Cell Institute, New York, NY, USA
- Sema4, a Mount Sinai Venture, Stamford, CT, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA.
- Sema4, a Mount Sinai Venture, Stamford, CT, USA.
| | - Alexander W Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute of Data Science and Genomics Technology, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mount Sinai COVID Informatics Center, New York, NY, USA.
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Lampert J, Jaladanki S, Vaid A, Havaldar S, Nadkami G, Halperin JL, Miller MA, Koruth JS, Whang W, Turagam MK, Dukkipati SR, Glicksberg B, Reddy VY. B-PO01-082 ARTIFICIAL INTELLIGENCE-ASSISTED QRS AMPLITUDE ANALYSIS OF THE PRESENTING ELECTROCARDIOGRAM PREDICTS MORTALITY IN COVID-19. Heart Rhythm 2021. [PMCID: PMC8315748 DOI: 10.1016/j.hrthm.2021.06.227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Vaid A, Chan L, Chaudhary K, Jaladanki SK, Paranjpe I, Russak A, Kia A, Timsina P, Levin MA, He JC, Böttinger EP, Charney AW, Fayad ZA, Coca SG, Glicksberg BS, Nadkarni GN. Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19. Clin J Am Soc Nephrol 2021; 16:1158-1168. [PMID: 34031183 PMCID: PMC8455052 DOI: 10.2215/cjn.17311120] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 04/28/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND OBJECTIVES AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Using data from adult patients hospitalized with COVID-19 from five hospitals from the Mount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission. RESULTS A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precision-recall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction. CONCLUSIONS An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models. PODCAST This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_07_09_CJN17311120.mp3.
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Affiliation(s)
- Akhil Vaid
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lili Chan
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York,The Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kumardeep Chaudhary
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Suraj K. Jaladanki
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ishan Paranjpe
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Adam Russak
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Arash Kia
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Prem Timsina
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew A. Levin
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - John Cijiang He
- The Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Erwin P. Böttinger
- The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, New York,Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Alexander W. Charney
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York,The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York,The Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Zahi A. Fayad
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, BioMedical Engineering and Imaging Institute, Icahn School
| | - Steven G. Coca
- The Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Benjamin S. Glicksberg
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, New York,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N. Nadkarni
- The Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York,The Hasso Plattner Institute of Digital Health, Icahn School of Medicine at Mount Sinai, New York, New York,The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York,The Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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27
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Jaladanki SK, Vaid A, Sawant AS, Xu J, Shah K, Dellepiane S, Paranjpe I, Chan L, Kovatch P, Charney AW, Wang F, Glicksberg BS, Singh K, Nadkarni GN. Development of a federated learning approach to predict acute kidney injury in adult hospitalized patients with COVID-19 in New York City. medRxiv 2021:2021.07.25.21261105. [PMID: 34341802 PMCID: PMC8328073 DOI: 10.1101/2021.07.25.21261105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Federated learning is a technique for training predictive models without sharing patient-level data, thus maintaining data security while allowing inter-institutional collaboration. We used federated learning to predict acute kidney injury within three and seven days of admission, using demographics, comorbidities, vital signs, and laboratory values, in 4029 adults hospitalized with COVID-19 at five sociodemographically diverse New York City hospitals, between March-October 2020. Prediction performance of federated models was generally higher than single-hospital models and was comparable to pooled-data models. In the first use-case in kidney disease, federated learning improved prediction of a common complication of COVID-19, while preserving data privacy.
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Affiliation(s)
- Suraj K Jaladanki
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
| | - Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
| | - Ashwin S Sawant
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- The Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jie Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Kush Shah
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
| | - Sergio Dellepiane
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- The Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
| | - Lili Chan
- The Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Patricia Kovatch
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alexander W Charney
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Karandeep Singh
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Mount Sinai Clinical Intelligence Center (MSCIC), New York, New York, USA
- The Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Wanyan T, Vaid A, De Freitas JK, Somani S, Miotto R, Nadkarni GN, Azad A, Ding Y, Glicksberg BS. Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit. IEEE Trans Big Data 2021; 7:38-44. [PMID: 33768136 PMCID: PMC7990133 DOI: 10.1109/tbdata.2020.3048644] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 10/29/2020] [Accepted: 12/21/2020] [Indexed: 05/04/2023]
Abstract
Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall.
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Affiliation(s)
- Tingyi Wanyan
- Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
- School of Informatics, Computing, and EngineeringIndiana UniversityBloomingtonIN47405USA
- School of InformationUniversity of Texas at AustinAustinTX78712USA
| | - Akhil Vaid
- Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
| | - Jessica K De Freitas
- Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
| | - Sulaiman Somani
- Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
| | - Girish N. Nadkarni
- Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
- Department of MedicineIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
| | - Ariful Azad
- School of Informatics, Computing, and EngineeringIndiana UniversityBloomingtonIN47405USA
| | - Ying Ding
- School of InformationUniversity of Texas at AustinAustinTX78712USA
- Dell Medical SchoolUniversity of Texas at AustinAustinTX78712USA
| | - Benjamin S. Glicksberg
- Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY10029USA
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29
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Pattharanitima P, Vaid A, Jaladanki SK, Paranjpe I, O'Hagan R, Chauhan K, Van Vleck TT, Duffy A, Chaudhary K, Glicksberg BS, Neyra JA, Coca SG, Chan L, Nadkarni GN. Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury. Blood Purif 2021; 50:621-627. [PMID: 33631752 DOI: 10.1159/000513700] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 12/08/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND/AIMS Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. METHODS We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. RESULTS Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52-84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67-0.73), followed by MLP 0.59 (0.54-0.64), LR 0.57 (0.52-0.62), SVM 0.51 (0.46-0.56), AdaBoost 0.51 (0.46-0.55), RF 0.44 (0.39-0.48), and XGBoost 0.43 (CI 0.38-0.47). CONCLUSIONS A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.
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Affiliation(s)
- Pattharawin Pattharanitima
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand
| | - Akhil Vaid
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Ishan Paranjpe
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ross O'Hagan
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kinsuk Chauhan
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Tielman T Van Vleck
- Department of Genetics and Genomic Sciences, Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Aine Duffy
- Department of Genetics and Genomic Sciences, Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kumardeep Chaudhary
- Department of Genetics and Genomic Sciences, Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Javier A Neyra
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, Kentucky, USA
| | - Steven G Coca
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lili Chan
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA,
| | - Girish N Nadkarni
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Somani S, Russak AJ, Richter F, Zhao S, Vaid A, Chaudhry F, De Freitas JK, Naik N, Miotto R, Nadkarni GN, Narula J, Argulian E, Glicksberg BS. Deep learning and the electrocardiogram: review of the current state-of-the-art. Europace 2021; 23:1179-1191. [PMID: 33564873 PMCID: PMC8350862 DOI: 10.1093/europace/euaa377] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/25/2020] [Indexed: 12/22/2022] Open
Abstract
In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.
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Affiliation(s)
- Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
| | - Adam J Russak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Felix Richter
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
| | - Fayzan Chaudhry
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nidhi Naik
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA
| | - Riccardio Miotto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Pl., New York, NY 10029, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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31
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Vaid A, Jaladanki SK, Xu J, Teng S, Kumar A, Lee S, Somani S, Paranjpe I, De Freitas JK, Wanyan T, Johnson KW, Bicak M, Klang E, Kwon YJ, Costa A, Zhao S, Miotto R, Charney AW, Böttinger E, Fayad ZA, Nadkarni GN, Wang F, Glicksberg BS. Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach. JMIR Med Inform 2021; 9:e24207. [PMID: 33400679 PMCID: PMC7842859 DOI: 10.2196/24207] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/23/2020] [Accepted: 12/14/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. CONCLUSIONS The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.
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Affiliation(s)
- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
| | - Suraj K Jaladanki
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
| | - Jie Xu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Shelly Teng
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
| | - Arvind Kumar
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
| | - Samuel Lee
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
| | - Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
| | - Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Tingyi Wanyan
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Intelligent System Engineering, Indiana University, Bloomington, IN, United States
- School of Information, University of Texas Austin, Austin, TX, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
| | - Mesude Bicak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eyal Klang
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Young Joon Kwon
- Department of Neurological Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Anthony Costa
- Department of Neurological Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Riccardo Miotto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alexander W Charney
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Erwin Böttinger
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Mount Sinai Clinical Intelligence Center, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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32
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Wanyan T, Honarvar H, Jaladanki SK, Zang C, Naik N, Somani S, Freitas JKD, Paranjpe I, Vaid A, Miotto R, Nadkarni GN, Zitnik M, Wang F, Ding Y, Glicksberg BS. Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients. ArXiv 2021:2101.04013. [PMID: 33442560 PMCID: PMC7805456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, particularly within electronic health records (EHR) research. Conventional approaches in ML use cross-entropy loss (CEL) that often suffers from poor margin classification. For the first time, we show that contrastive loss (CL) improves the performance of CEL especially for imbalanced EHR data and the related COVID-19 analyses. This study has been approved by the Institutional Review Board at the Icahn School of Medicine at Mount Sinai. We use EHR data from five hospitals within the Mount Sinai Health System (MSHS) to predict mortality, intubation, and intensive care unit (ICU) transfer in hospitalized COVID-19 patients over 24 and 48 hour time windows. We train two sequential architectures (RNN and RETAIN) using two loss functions (CEL and CL). Models are tested on full sample data set which contain all available data and restricted data set to emulate higher class imbalance.CL models consistently outperform CEL models with the restricted data set on these tasks with differences ranging from 0.04 to 0.15 for AUPRC and 0.05 to 0.1 for AUROC. For the restricted sample, only the CL model maintains proper clustering and is able to identify important features, such as pulse oximetry. CL outperforms CEL in instances of severe class imbalance, on three EHR outcomes with respect to three performance metrics: predictive power, clustering, and feature importance. We believe that the developed CL framework can be expanded and used for EHR ML work in general.
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Chan L, Chaudhary K, Saha A, Chauhan K, Vaid A, Zhao S, Paranjpe I, Somani S, Richter F, Miotto R, Lala A, Kia A, Timsina P, Li L, Freeman R, Chen R, Narula J, Just AC, Horowitz C, Fayad Z, Cordon-Cardo C, Schadt E, Levin MA, Reich DL, Fuster V, Murphy B, He JC, Charney AW, Böttinger EP, Glicksberg BS, Coca SG, Nadkarni GN. AKI in Hospitalized Patients with COVID-19. J Am Soc Nephrol 2021; 32:151-160. [PMID: 32883700 PMCID: PMC7894657 DOI: 10.1681/asn.2020050615] [Citation(s) in RCA: 417] [Impact Index Per Article: 139.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 08/03/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described. METHODS This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. RESULTS Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. CONCLUSIONS AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.
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Affiliation(s)
- Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kumardeep Chaudhary
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Aparna Saha
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kinsuk Chauhan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Felix Richter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Riccardo Miotto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Anuradha Lala
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Arash Kia
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Prem Timsina
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Li Li
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Robert Freeman
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Rong Chen
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Allan C. Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Carol Horowitz
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Zahi Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Carlos Cordon-Cardo
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Eric Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew A. Levin
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - David L. Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Valentin Fuster
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Barbara Murphy
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - John C. He
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander W. Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Erwin P. Böttinger
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Benjamin S. Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Steven G. Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - on behalf of the Mount Sinai COVID Informatics Center (MSCIC)*
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
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Aggarwal S, Vaid A, Ramesh A, Parikh PM, Purohit S, Avasthi B, Gupta S, Ranjan S, Kaushal V, Salim S, Singh R, Minhas S, Doval D. Practical consensus recommendations on management of HR + ve early breast cancer with specific reference to genomic profiling. South Asian J Cancer 2020; 7:96-101. [PMID: 29721472 PMCID: PMC5909304 DOI: 10.4103/sajc.sajc_110_18] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is a heterogeneous disease and patients are managed clinically based on ER, PR, HER2 expression, and key risk factors. The use of gene expression assays for early stage disease is already common practice. These tests have found a place in risk stratifying the heterogeneous group of stage I–II breast cancers for recurrence, for predicting chemotherapy response, and for predicting breast cancer-related mortality. Most guidelines for hormone receptor (HR)–positive early breast cancer recommend addition of adjuvant chemotherapy for most women, leading to overtreatment, which causes considerable morbidity and cost. Expert oncologist discussed about strategies of gene expression assays and aid in chemotherapy recommendations for treatment of HR + ve EBC and the expert group used data from published literature, practical experience and opinion of a large group of academic oncologists to arrive at this practical consensus recommendations for the benefit of community oncologists.
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Affiliation(s)
- S Aggarwal
- Department of Medical Oncology, Sir Ganga Ram Hospital, New Delhi, India
| | - A Vaid
- Department of Medical Oncology, Medanata Hospital, Gurugram, Haryana, India
| | - A Ramesh
- Department of Medical Oncology, HCG Cancer Center, Chennai, Tamil Nadu, India.,Department of Medical Oncology, SMH Curie Cancer Center, New Delhi, India
| | - Purvish M Parikh
- Department of Oncology, Shalby Cancer and Research Institute, Mumbai, Maharashtra, India
| | - S Purohit
- Department of Medical Oncology, Artemis Hospital, Gurugram, Haryana, India
| | - B Avasthi
- Department or Radiation Oncology, Fortis Hospital, New Delhi, India
| | - S Gupta
- Department of Medical Oncology, Sarvodaya Hospital, Faridabad, Haryana, India
| | - S Ranjan
- Department of Medicine, INHS Sanjivani, Kochi, Kerala, India
| | - V Kaushal
- Department of Radiation Oncology, RCC, Rohtak, Haryana, India
| | - S Salim
- Department of Oncology, Hakim Sanaullah Cancer Center, Baramulla, Jammu and Kashmir, India
| | - R Singh
- Department of Medical Oncology, Army Hospital R and R, New Delhi, India
| | - S Minhas
- Department of Medical Oncology, Medanata Hospital, Gurugram, Haryana, India
| | - D Doval
- Department of Medical Oncology, Rajiv Gandhi Cancer Institute, New Delhi, India
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35
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Paranjpe I, Russak AJ, De Freitas JK, Lala A, Miotto R, Vaid A, Johnson KW, Danieletto M, Golden E, Meyer D, Singh M, Somani S, Kapoor A, O'Hagan R, Manna S, Nangia U, Jaladanki SK, O'Reilly P, Huckins LM, Glowe P, Kia A, Timsina P, Freeman RM, Levin MA, Jhang J, Firpo A, Kovatch P, Finkelstein J, Aberg JA, Bagiella E, Horowitz CR, Murphy B, Fayad ZA, Narula J, Nestler EJ, Fuster V, Cordon-Cardo C, Charney D, Reich DL, Just A, Bottinger EP, Charney AW, Glicksberg BS, Nadkarni GN. Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City. BMJ Open 2020; 10:e040736. [PMID: 33247020 PMCID: PMC7702220 DOI: 10.1136/bmjopen-2020-040736] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 10/24/2020] [Accepted: 10/26/2020] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive. DESIGN Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive. SETTING All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system. PARTICIPANTS Participants over the age of 18 years were included. PRIMARY OUTCOMES We investigated in-hospital mortality during the study period. RESULTS A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 μg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 μg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL. CONCLUSIONS In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.
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Affiliation(s)
- Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Adam J Russak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anuradha Lala
- The Zena and Michael A. Wiener Cardiovascular Institute, New York, New York, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Riccardo Miotto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matteo Danieletto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eddye Golden
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dara Meyer
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Manbir Singh
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Arjun Kapoor
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Ross O'Hagan
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Sayan Manna
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Udit Nangia
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Suraj K Jaladanki
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Paul O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Laura M Huckins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Patricia Glowe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Arash Kia
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Prem Timsina
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert M Freeman
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matthew A Levin
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jeffrey Jhang
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Adolfo Firpo
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Patricia Kovatch
- Mount Sinai Data Warehouse, Mount Sinai Health System, New York, New York, USA
| | - Joseph Finkelstein
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Judith A Aberg
- Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Icahn School of Medicine at Mount Sinai Department of Medicine, New York, New York, USA
| | - Emilia Bagiella
- The Zena and Michael A. Wiener Cardiovascular Institute, New York, New York, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carol R Horowitz
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Icahn School of Medicine at Mount Sinai Department of Medicine, New York, New York, USA
| | - Barbara Murphy
- Icahn School of Medicine at Mount Sinai Department of Medicine, New York, New York, USA
| | - Zahi A Fayad
- Icahn School of Medicine at Mount Sinai BioMedical Engineering and Imaging Institute, New York, New York, USA
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai Department of Medicine, New York, New York, USA
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eric J Nestler
- Icahn School of Medicine at Mount Sinai Friedman Brain Institute, New York, New York, USA
- The Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - V Fuster
- Department of Medicine, Division of Cardiology, Zena and Michael A. Wiener Cardiovascular Institute and Marie-Josée and Henry R. Kravis Center for Cardiovascular Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Carlos Cordon-Cardo
- Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dennis Charney
- Icahn School of Medicine at Mount Sinai Department of Psychiatry, New York, New York, USA
- The Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - David L Reich
- Icahn School of Medicine at Mount Sinai Department of Anesthesiology Perioperative and Pain Medicine, New York, New York, USA
| | - Allan Just
- Icahn School of Medicine at Mount Sinai Department of Environmental Medicine and Public Health, New York, New York, USA
- Institute for Exposomic Research, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erwin P Bottinger
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
| | - Alexander W Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York, USA
- Icahn School of Medicine at Mount Sinai Department of Medicine, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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36
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Vaid A, Somani S, Russak AJ, De Freitas JK, Chaudhry FF, Paranjpe I, Johnson KW, Lee SJ, Miotto R, Richter F, Zhao S, Beckmann ND, Naik N, Kia A, Timsina P, Lala A, Paranjpe M, Golden E, Danieletto M, Singh M, Meyer D, O'Reilly PF, Huckins L, Kovatch P, Finkelstein J, Freeman RM, Argulian E, Kasarskis A, Percha B, Aberg JA, Bagiella E, Horowitz CR, Murphy B, Nestler EJ, Schadt EE, Cho JH, Cordon-Cardo C, Fuster V, Charney DS, Reich DL, Bottinger EP, Levin MA, Narula J, Fayad ZA, Just AC, Charney AW, Nadkarni GN, Glicksberg BS. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. J Med Internet Res 2020; 22:e24018. [PMID: 33027032 PMCID: PMC7652593 DOI: 10.2196/24018] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.
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Affiliation(s)
- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Adam J Russak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Fayzan F Chaudhry
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Kipp W Johnson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Samuel J Lee
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Riccardo Miotto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Felix Richter
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Noam D Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Nidhi Naik
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Arash Kia
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Prem Timsina
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Anuradha Lala
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Eddye Golden
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Matteo Danieletto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Manbir Singh
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Dara Meyer
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Paul F O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Laura Huckins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Patricia Kovatch
- Mount Sinai Data Warehouse, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Joseph Finkelstein
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Robert M Freeman
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Andrew Kasarskis
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Mount Sinai Data Office, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Bethany Percha
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Judith A Aberg
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Emilia Bagiella
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Carol R Horowitz
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Barbara Murphy
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eric J Nestler
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Carlos Cordon-Cardo
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Valentin Fuster
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Dennis S Charney
- Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - David L Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Erwin P Bottinger
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Matthew A Levin
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Zahi A Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Alexander W Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Paranjpe I, Tsao N, Judy R, Paranjpe M, Chaudhary K, Klarin D, Forrest I, O'Hagan R, Kapoor A, Pfail J, Jaladanki S, Chaudhry F, Vaid A, Duy PQ, He JC, Glicksberg BS, Coca SG, Gupta M, Do R, Damrauer SM, Nadkarni GN. Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis. Kidney Int 2020; 98:1323-1330. [PMID: 32540406 PMCID: PMC7606592 DOI: 10.1016/j.kint.2020.04.055] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 03/30/2020] [Accepted: 04/16/2020] [Indexed: 11/25/2022]
Abstract
Urinary tract stones have high heritability indicating a strong genetic component. However, genome-wide association studies (GWAS) have uncovered only a few genome wide significant single nucleotide polymorphisms (SNPs). Polygenic risk scores (PRS) sum cumulative effect of many SNPs and shed light on underlying genetic architecture. Using GWAS summary statistics from 361,141 participants in the United Kingdom Biobank, we generated a PRS and determined association with stone diagnosis in 28,877 participants in the Mount Sinai BioMe Biobank. In BioMe (1,071 cases and 27,806 controls), for every standard deviation increase, we observed a significant increment in adjusted odds ratio of a factor of 1.2 (95% confidence interval 1.13-1.26). In comparison, a risk score comprised of GWAS significant SNPs was not significantly associated with diagnosis. After stratifying individuals into low and high-risk categories on clinical risk factors, there was a significant increment in adjusted odds ratio of 1.3 (1.12-1.6) in the low- and 1.2 (1.1-1.2) in the high-risk group for every standard deviation increment in PRS. In a 14,348-participant validation cohort (Penn Medicine Biobank), every standard deviation increment was associated with a significant adjusted odds ratio of 1.1 (1.03 - 1.2). Thus, a genome-wide PRS is associated with urinary tract stones overall and in the absence of known clinical risk factors and illustrates their complex polygenic architecture.
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Affiliation(s)
- Ishan Paranjpe
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Noah Tsao
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Renae Judy
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Manish Paranjpe
- Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA
| | - Kumardeep Chaudhary
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Derek Klarin
- Malcolm Randall Veterans Affairs Medical Center, Gainesville, Florida, USA; Division of Vascular Surgery and Endovascular Therapy, University of Florida College of Medicine, Gainesville, Florida, USA; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Iain Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ross O'Hagan
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Arjun Kapoor
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - John Pfail
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Suraj Jaladanki
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fayzan Chaudhry
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Akhil Vaid
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Phan Q Duy
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut, USA
| | - John Cijiang He
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Renal Program, James J Peters Veterans Affairs Medical Center at Bronx, New York, New York, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Steven G Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mantu Gupta
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Scott M Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Renal Program, James J Peters Veterans Affairs Medical Center at Bronx, New York, New York, USA.
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38
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Chaudhary K, Vaid A, Duffy Á, Paranjpe I, Jaladanki S, Paranjpe M, Johnson K, Gokhale A, Pattharanitima P, Chauhan K, O'Hagan R, Van Vleck T, Coca SG, Cooper R, Glicksberg B, Bottinger EP, Chan L, Nadkarni GN. Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury. Clin J Am Soc Nephrol 2020; 15:1557-1565. [PMID: 33033164 PMCID: PMC7646246 DOI: 10.2215/cjn.09330819] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 08/07/2020] [Indexed: 11/23/2022]
Abstract
BACKGROUND AND OBJECTIVES Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS We used the Medical Information Mart for Intensive Care III database, which consists of electronic health record data from intensive care units in a tertiary care hospital in the United States. We included patients ≥18 years with sepsis who developed AKI within 48 hours of intensive care unit admission. We then used deep learning to utilize all available vital signs, laboratory measurements, and comorbidities to identify subphenotypes. Outcomes were mortality 28 days after AKI and dialysis requirement. RESULTS We identified 4001 patients with sepsis-associated AKI. We utilized 2546 combined features for K-means clustering, identifying three subphenotypes. Subphenotype 1 had 1443 patients, and subphenotype 2 had 1898 patients, whereas subphenotype 3 had 660 patients. Subphenotype 1 had the lowest proportion of liver disease and lowest Simplified Acute Physiology Score II scores compared with subphenotypes 2 and 3. The proportions of patients with CKD were similar between subphenotypes 1 and 3 (15%) but highest in subphenotype 2 (21%). Subphenotype 1 had lower median bilirubin levels, aspartate aminotransferase, and alanine aminotransferase compared with subphenotypes 2 and 3. Patients in subphenotype 1 also had lower median lactate, lactate dehydrogenase, and white blood cell count than patients in subphenotypes 2 and 3. Subphenotype 1 also had lower creatinine and BUN than subphenotypes 2 and 3. Dialysis requirement was lowest in subphenotype 1 (4% versus 7% [subphenotype 2] versus 26% [subphenotype 3]). The mortality 28 days after AKI was lowest in subphenotype 1 (23% versus 35% [subphenotype 2] versus 49% [subphenotype 3]). After adjustment, the adjusted odds ratio for mortality for subphenotype 3, with subphenotype 1 as a reference, was 1.9 (95% confidence interval, 1.5 to 2.4). CONCLUSIONS Utilizing routinely collected laboratory variables, vital signs, and comorbidities, we were able to identify three distinct subphenotypes of sepsis-associated AKI with differing outcomes.
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Affiliation(s)
- Kumardeep Chaudhary
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Akhil Vaid
- Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Áine Duffy
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ishan Paranjpe
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Suraj Jaladanki
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Manish Paranjpe
- Harvard Medical School, Boston, Massachusetts.,Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Kipp Johnson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Avantee Gokhale
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Pattharawin Pattharanitima
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kinsuk Chauhan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ross O'Hagan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Tielman Van Vleck
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Steven G Coca
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Richard Cooper
- Department of Public Health Sciences, Loyola University School of Medicine, Chicago, Illinois
| | - Benjamin Glicksberg
- Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Erwin P Bottinger
- Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish N Nadkarni
- Hasso Plattner Institute of Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York .,Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
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39
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Beckmann ND, Comella PH, Cheng E, Lepow L, Beckmann AG, Mouskas K, Simons NW, Hoffman GE, Francoeur NJ, Del Valle DM, Kang G, Moya E, Wilkins L, Le Berichel J, Chang C, Marvin R, Calorossi S, Lansky A, Walker L, Yi N, Yu A, Hartnett M, Eaton M, Hatem S, Jamal H, Akyatan A, Tabachnikova A, Liharska LE, Cotter L, Fennessey B, Vaid A, Barturen G, Tyler SR, Shah H, Wang YC, Sridhar SH, Soto J, Bose S, Madrid K, Ellis E, Merzier E, Vlachos K, Fishman N, Tin M, Smith M, Xie H, Patel M, Argueta K, Harris J, Karekar N, Batchelor C, Lacunza J, Yishak M, Tuballes K, Scott L, Kumar A, Jaladanki S, Thompson R, Clark E, Losic B, Zhu J, Wang W, Kasarskis A, Glicksberg BS, Nadkarni G, Bogunovic D, Elaiho C, Gangadharan S, Ofori-Amanfo G, Alesso-Carra K, Onel K, Wilson KM, Argmann C, Alarcón-Riquelme ME, Marron TU, Rahman A, Kim-Schulze S, Gnjatic S, Gelb BD, Merad M, Sebra R, Schadt EE, Charney AW. Cytotoxic lymphocytes are dysregulated in multisystem inflammatory syndrome in children. medRxiv 2020:2020.08.29.20182899. [PMID: 32909006 PMCID: PMC7480058 DOI: 10.1101/2020.08.29.20182899] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Multisystem inflammatory syndrome in children (MIS-C) presents with fever, inflammation and multiple organ involvement in individuals under 21 years following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. To identify genes, pathways and cell types driving MIS-C, we sequenced the blood transcriptomes of MIS-C cases, pediatric cases of coronavirus disease 2019, and healthy controls. We define a MIS-C transcriptional signature partially shared with the transcriptional response to SARS-CoV-2 infection and with the signature of Kawasaki disease, a clinically similar condition. By projecting the MIS-C signature onto a co-expression network, we identified disease gene modules and found genes downregulated in MIS-C clustered in a module enriched for the transcriptional signatures of exhausted CD8 + T-cells and CD56 dim CD57 + NK cells. Bayesian network analyses revealed nine key regulators of this module, including TBX21 , a central coordinator of exhausted CD8 + T-cell differentiation. Together, these findings suggest dysregulated cytotoxic lymphocyte response to SARS-Cov-2 infection in MIS-C.
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Affiliation(s)
- Noam D. Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Phillip H. Comella
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Esther Cheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lauren Lepow
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Aviva G. Beckmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Konstantinos Mouskas
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nicole W. Simons
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Gabriel E. Hoffman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nancy J. Francoeur
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Diane Marie Del Valle
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Gurpawan Kang
- Department of Medicine, division of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Emily Moya
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lillian Wilkins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jessica Le Berichel
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Christie Chang
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Robert Marvin
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sharlene Calorossi
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Alona Lansky
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Laura Walker
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Nancy Yi
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Alex Yu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Matthew Hartnett
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Melody Eaton
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sandra Hatem
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Hajra Jamal
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Alara Akyatan
- Department of of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Alexandra Tabachnikova
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lora E. Liharska
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Liam Cotter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Brian Fennessey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Akhil Vaid
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Guillermo Barturen
- Department of Medical Genomics, Center for Genomics and Oncological Research Pfizer/University of Granada/Andalusian Regional Government (GENYO), 18007 Urb. los Vergeles, Granada, Spain
| | - Scott R. Tyler
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Hardik Shah
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ying-chih Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Shwetha Hara Sridhar
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Juan Soto
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Swaroop Bose
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Kent Madrid
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Ethan Ellis
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Elyze Merzier
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Konstantinos Vlachos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Nataly Fishman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Manying Tin
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Melissa Smith
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Hui Xie
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Manishkumar Patel
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kimberly Argueta
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jocelyn Harris
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Neha Karekar
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Craig Batchelor
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jose Lacunza
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Mahlet Yishak
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kevin Tuballes
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Leisha Scott
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Arvind Kumar
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Suraj Jaladanki
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ryan Thompson
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
| | - Evan Clark
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bojan Losic
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jun Zhu
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Wenhui Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Andrew Kasarskis
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Benjamin S. Glicksberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Girish Nadkarni
- Mount Sinai COVID Informatics Center, New York, NY 10029, USA
- Department of Medicine, Mount Sinai, New York, NY 10029, USA
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY 10029, USA
- Charles Bronfman Institute for Personalized Medicine, New York, NY 10029, USA
| | - Dusan Bogunovic
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Cordelia Elaiho
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sandeep Gangadharan
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - George Ofori-Amanfo
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kasey Alesso-Carra
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kenan Onel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Karen M. Wilson
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carmen Argmann
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Marta E. Alarcón-Riquelme
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Thomas U. Marron
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Adeeb Rahman
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Seunghee Kim-Schulze
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sacha Gnjatic
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Medicine, division of Hematology and Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bruce D. Gelb
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Departments of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mindich Child Health and Development Institute at Mount Sinai, New York, NY 10029, USA
| | - Miriam Merad
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
- Black Family Stem Cell Institute, New York, NY 10029, USA
- Sema4, a Mount Sinai venture, Stamford CT, 06902, USA
| | - Eric E. Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
- Sema4, a Mount Sinai venture, Stamford CT, 06902, USA
| | - Alexander W. Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Data Science and Genomics Technology, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mount Sinai COVID Informatics Center, New York, NY 10029, USA
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40
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Vaid A, Jaladanki SK, Xu J, Teng S, Kumar A, Lee S, Somani S, Paranjpe I, De Freitas JK, Wanyan T, Johnson KW, Bicak M, Klang E, Kwon YJ, Costa A, Zhao S, Miotto R, Charney AW, Böttinger E, Fayad ZA, Nadkarni GN, Wang F, Glicksberg BS. Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19. medRxiv 2020:2020.08.11.20172809. [PMID: 32817979 PMCID: PMC7430624 DOI: 10.1101/2020.08.11.20172809] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.
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Affiliation(s)
- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
| | - Suraj K Jaladanki
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
| | - Jie Xu
- Department of Population Health Sciences. Weill Cornell Medicine. New York, USA
| | - Shelly Teng
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
| | - Arvind Kumar
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
| | - Samuel Lee
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
| | - Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
| | - Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Tingyi Wanyan
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- Intelligent System Engineering, Indiana University, Bloomington, USA
- School of Information, University of Texas Austin, Austin, USA
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
| | - Mesude Bicak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Eyal Klang
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Young Joon Kwon
- Department of Neurological Surgery, Icahn School of Medicine, New York, USA
| | - Anthony Costa
- Department of Neurological Surgery, Icahn School of Medicine, New York, USA
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Riccardo Miotto
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alexander W Charney
- The Mount Sinai COVID Informatics Center, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Erwin Böttinger
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
- Digital Health Center, Hasso Plattner Institute, University of Potsdam, Germany
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Fei Wang
- Department of Population Health Sciences. Weill Cornell Medicine. New York, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA
- The Mount Sinai COVID Informatics Center, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, USA
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Lala A, Johnson KW, Januzzi JL, Russak AJ, Paranjpe I, Richter F, Zhao S, Somani S, Van Vleck T, Vaid A, Chaudhry F, De Freitas JK, Fayad ZA, Pinney SP, Levin M, Charney A, Bagiella E, Narula J, Glicksberg BS, Nadkarni G, Mancini DM, Fuster V. Prevalence and Impact of Myocardial Injury in Patients Hospitalized With COVID-19 Infection. J Am Coll Cardiol 2020; 76:533-546. [PMID: 32517963 PMCID: PMC7279721 DOI: 10.1016/j.jacc.2020.06.007] [Citation(s) in RCA: 509] [Impact Index Per Article: 127.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 06/02/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND The degree of myocardial injury, as reflected by troponin elevation, and associated outcomes among U.S. hospitalized patients with coronavirus disease-2019 (COVID-19) are unknown. OBJECTIVES The purpose of this study was to describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19. METHODS Patients with COVID-19 admitted to 1 of 5 Mount Sinai Health System hospitals in New York City between February 27, 2020, and April 12, 2020, with troponin-I (normal value <0.03 ng/ml) measured within 24 h of admission were included (n = 2,736). Demographics, medical histories, admission laboratory results, and outcomes were captured from the hospitals' electronic health records. RESULTS The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD), including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. In all, 985 (36%) patients had elevated troponin concentrations. After adjusting for disease severity and relevant clinical factors, even small amounts of myocardial injury (e.g., troponin I >0.03 to 0.09 ng/ml; n = 455; 16.6%) were significantly associated with death (adjusted hazard ratio: 1.75; 95% CI: 1.37 to 2.24; p < 0.001) while greater amounts (e.g., troponin I >0.09 ng/dl; n = 530; 19.4%) were significantly associated with higher risk (adjusted HR: 3.03; 95% CI: 2.42 to 3.80; p < 0.001). CONCLUSIONS Myocardial injury is prevalent among patients hospitalized with COVID-19; however, troponin concentrations were generally present at low levels. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation among patients hospitalized with COVID-19 is associated with higher risk of mortality.
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Affiliation(s)
- Anuradha Lala
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - James L Januzzi
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts; Baim Institute for Clinical Research, Cardiometabolic Trials, Boston, Massachusetts
| | - Adam J Russak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ishan Paranjpe
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Felix Richter
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sulaiman Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Tielman Van Vleck
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Fayzan Chaudhry
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York
| | - Jessica K De Freitas
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sean P Pinney
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew Levin
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Emilia Bagiella
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jagat Narula
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Girish Nadkarni
- The Hasso Plattner Institute for Digital Health at Mount Sinai, New York, New York; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Donna M Mancini
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Valentin Fuster
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
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42
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Russak AJ, Chaudhry F, De Freitas JK, Baron G, Chaudhry FF, Bienstock S, Paranjpe I, Vaid A, Ali M, Zhao S, Somani S, Richter F, Bawa T, Levy PD, Miotto R, Nadkarni GN, Johnson KW, Glicksberg BS. Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype. J Cardiovasc Pharmacol Ther 2020; 25:379-390. [PMID: 32495652 DOI: 10.1177/1074248420928651] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Despite substantial advances in the study, treatment, and prevention of cardiovascular disease, numerous challenges relating to optimally screening, diagnosing, and managing patients remain. Simultaneous improvements in computing power, data storage, and data analytics have led to the development of new techniques to address these challenges. One powerful tool to this end is machine learning (ML), which aims to algorithmically identify and represent structure within data. Machine learning's ability to efficiently analyze large and highly complex data sets make it a desirable investigative approach in modern biomedical research. Despite this potential and enormous public and private sector investment, few prospective studies have demonstrated improved clinical outcomes from this technology. This is particularly true in cardiology, despite its emphasis on objective, data-driven results. This threatens to stifle ML's growth and use in mainstream medicine. We outline the current state of ML in cardiology and outline methods through which impactful and sustainable ML research can occur. Following these steps can ensure ML reaches its potential as a transformative technology in medicine.
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Affiliation(s)
- Adam J Russak
- Department of Internal Medicine, Mount Sinai Hospital, New York, NY, USA.,Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Farhan Chaudhry
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
| | - Jessica K De Freitas
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Garrett Baron
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
| | - Fayzan F Chaudhry
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Solomon Bienstock
- Department of Internal Medicine, Mount Sinai Hospital, New York, NY, USA
| | - Ishan Paranjpe
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Akhil Vaid
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mohsin Ali
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shan Zhao
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sulaiman Somani
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Felix Richter
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tejeshwar Bawa
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
| | - Phillip D Levy
- Department of Emergency Medicine and Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
| | - Riccardo Miotto
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Division of Nephrology, Mount Sinai Hospital, New York, NY, USA.,Division of Cardiology, Mount Sinai Hospital, New York, NY, USA.,Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kipp W Johnson
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Chan L, Chaudhary K, Saha A, Chauhan K, Vaid A, Baweja M, Campbell K, Chun N, Chung M, Deshpande P, Farouk SS, Kaufman L, Kim T, Koncicki H, Lapsia V, Leisman S, Lu E, Meliambro K, Menon MC, Rein JL, Sharma S, Tokita J, Uribarri J, Vassalotti JA, Winston J, Mathews KS, Zhao S, Paranjpe I, Somani S, Richter F, Do R, Miotto R, Lala A, Kia A, Timsina P, Li L, Danieletto M, Golden E, Glowe P, Zweig M, Singh M, Freeman R, Chen R, Nestler E, Narula J, Just AC, Horowitz C, Aberg J, Loos RJF, Cho J, Fayad Z, Cordon-Cardo C, Schadt E, Levin MA, Reich DL, Fuster V, Murphy B, He JC, Charney AW, Bottinger EP, Glicksberg BS, Coca SG, Nadkarni GN. Acute Kidney Injury in Hospitalized Patients with COVID-19. medRxiv 2020. [PMID: 32511564 DOI: 10.1101/2020.05.04.20090944] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
IMPORTANCE Preliminary reports indicate that acute kidney injury (AKI) is common in coronavirus disease (COVID)-19 patients and is associated with worse outcomes. AKI in hospitalized COVID-19 patients in the United States is not well-described. OBJECTIVE To provide information about frequency, outcomes and recovery associated with AKI and dialysis in hospitalized COVID-19 patients. DESIGN Observational, retrospective study. SETTING Admitted to hospital between February 27 and April 15, 2020. PARTICIPANTS Patients aged ≥18 years with laboratory confirmed COVID-19 Exposures: AKI (peak serum creatinine increase of 0.3 mg/dL or 50% above baseline). Main Outcomes and Measures: Frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aOR) with mortality. We also trained and tested a machine learning model for predicting dialysis requirement with independent validation. RESULTS A total of 3,235 hospitalized patients were diagnosed with COVID-19. AKI occurred in 1406 (46%) patients overall and 280 (20%) with AKI required renal replacement therapy. The incidence of AKI (admission plus new cases) in patients admitted to the intensive care unit was 68% (553 of 815). In the entire cohort, the proportion with stages 1, 2, and 3 AKI were 35%, 20%, 45%, respectively. In those needing intensive care, the respective proportions were 20%, 17%, 63%, and 34% received acute renal replacement therapy. Independent predictors of severe AKI were chronic kidney disease, systolic blood pressure, and potassium at baseline. In-hospital mortality in patients with AKI was 41% overall and 52% in intensive care. The aOR for mortality associated with AKI was 9.6 (95% CI 7.4-12.3) overall and 20.9 (95% CI 11.7-37.3) in patients receiving intensive care. 56% of patients with AKI who were discharged alive recovered kidney function back to baseline. The area under the curve (AUC) for the machine learned predictive model using baseline features for dialysis requirement was 0.79 in a validation test. CONCLUSIONS AND RELEVANCE AKI is common in patients hospitalized with COVID-19, associated with worse mortality, and the majority of patients that survive do not recover kidney function. A machine-learned model using admission features had good performance for dialysis prediction and could be used for resource allocation.
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Abstract
PURPOSE OF REVIEW The universal adoption of electronic health records, improvement in technology, and the availability of continuous monitoring has generated large quantities of healthcare data. Machine learning is increasingly adopted by nephrology researchers to analyze this data in order to improve the care of their patients. RECENT FINDINGS In this review, we provide a broad overview of the different types of machine learning algorithms currently available and how researchers have applied these methods in nephrology research. Current applications have included prediction of acute kidney injury and chronic kidney disease along with progression of kidney disease. Researchers have demonstrated the ability of machine learning to read kidney biopsy samples, identify patient outcomes from unstructured data, and identify subtypes in complex diseases. We end with a discussion on the ethics and potential pitfalls of machine learning. SUMMARY Machine learning provides researchers with the ability to analyze data that were previously inaccessible. While still burgeoning, several studies show promising results, which will enable researchers to perform larger scale studies and clinicians the ability to provide more personalized care. However, we must ensure that implementation aids providers and does not lead to harm to patients.
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Affiliation(s)
- Lili Chan
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Akhil Vaid
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY
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Paranjpe I, Russak AJ, De Freitas JK, Lala A, Miotto R, Vaid A, Johnson KW, Danieletto M, Golden E, Meyer D, Singh M, Somani S, Manna S, Nangia U, Kapoor A, O'Hagan R, O'Reilly PF, Huckins LM, Glowe P, Kia A, Timsina P, Freeman RM, Levin MA, Jhang J, Firpo A, Kovatch P, Finkelstein J, Aberg JA, Bagiella E, Horowitz CR, Murphy B, Fayad ZA, Narula J, Nestler EJ, Fuster V, Cordon-Cardo C, Charney DS, Reich DL, Just AC, Bottinger EP, Charney AW, Glicksberg BS, Nadkarni GN. Clinical Characteristics of Hospitalized Covid-19 Patients in New York City. medRxiv 2020. [PMID: 32511655 DOI: 10.1101/2020.04.19.20062117] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Background The coronavirus 2019 (Covid-19) pandemic is a global public health crisis, with over 1.6 million cases and 95,000 deaths worldwide. Data are needed regarding the clinical course of hospitalized patients, particularly in the United States. Methods Demographic, clinical, and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed Covid-19 between February 27 and April 2, 2020 were identified through institutional electronic health records. We conducted a descriptive study of patients who had in-hospital mortality or were discharged alive. Results A total of 2,199 patients with Covid-19 were hospitalized during the study period. As of April 2 nd , 1,121 (51%) patients remained hospitalized, and 1,078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 ug/ml, C-reactive protein was 162 mg/L, and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 ug/ml, C-reactive protein was 79 mg/L, and procalcitonin was 0.09 ng/mL. Conclusions This is the largest and most diverse case series of hospitalized patients with Covid-19 in the United States to date. Requirement of intensive care and mortality were high. Patients who died typically had pre-existing conditions and severe perturbations in inflammatory markers.
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Lala A, Johnson KW, Russak AJ, Paranjpe I, Zhao S, Solani S, Vaid A, Chaudhry F, De Freitas JK, Fayad ZA, Pinney SP, Levin M, Charney A, Bagiella E, Narula J, Glicksberg BS, Nadkarni G, Januzzi J, Mancini DM, Fuster V. Prevalence and Impact of Myocardial Injury in Patients Hospitalized with COVID-19 Infection. medRxiv 2020. [PMID: 32511658 DOI: 10.1101/2020.04.20.20072702] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND The degree of myocardial injury, reflected by troponin elevation, and associated outcomes among hospitalized patients with Coronavirus Disease (COVID-19) in the US are unknown. OBJECTIVES To describe the degree of myocardial injury and associated outcomes in a large hospitalized cohort with laboratory-confirmed COVID-19. METHODS Patients with COVID-19 admitted to one of five Mount Sinai Health System hospitals in New York City between February 27th and April 12th, 2020 with troponin-I (normal value <0.03ng/mL) measured within 24 hours of admission were included (n=2,736). Demographics, medical history, admission labs, and outcomes were captured from the hospital EHR. RESULTS The median age was 66.4 years, with 59.6% men. Cardiovascular disease (CVD) including coronary artery disease, atrial fibrillation, and heart failure, was more prevalent in patients with higher troponin concentrations, as were hypertension and diabetes. A total of 506 (18.5%) patients died during hospitalization. Even small amounts of myocardial injury (e.g. troponin I 0.03-0.09ng/mL, n=455, 16.6%) were associated with death (adjusted HR: 1.77, 95% CI 1.39-2.26; P<0.001) while greater amounts (e.g. troponin I>0.09 ng/dL, n=530, 19.4%) were associated with more pronounced risk (adjusted HR 3.23, 95% CI 2.59-4.02). CONCLUSIONS Myocardial injury is prevalent among patients hospitalized with COVID-19, and is associated with higher risk of mortality. Patients with CVD are more likely to have myocardial injury than patients without CVD. Troponin elevation likely reflects non-ischemic or secondary myocardial injury.
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Vaid A, Patil C, Sanghariyat A, Rane R, Visani A, Mukherjee S, Joseph A, Ranjan M, Augustine S, Sooraj KP, Rathore V, Nema SK, Agraj A, Garg G, Sharma A, Sharma M, Pansare K, Krishna CM, Banerjee J, Chandra S. Emerging Advanced Technologies Developed by IPR for Bio Medical Applications ‑.A Review. Neurol India 2020; 68:26-34. [PMID: 32129239 DOI: 10.4103/0028-3886.279707] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Over the last decade, research has intensified worldwide on the use of low-temperature plasmas in medicine and healthcare. Researchers have discovered many methods of applying plasmas to living tissues to deactivate pathogens; to end the flow of blood without damaging healthy tissue; to sanitize wounds and accelerate its healing; and to selectively kill malignant cancer cells. This review paper presents the latest development of advanced and plasma-based technologies used for applications in neurology in particular. Institute for Plasma Research (IPR), an aided institute of the Department of Atomic Energy (DAE), has also developed various technologies in some of these areas. One of these is an Atmospheric Pressure Plasma Jet (APPJ). This device is being studied to treat skin diseases, for coagulation of blood at faster rates and its interaction with oral, lung, and brain cancer cells. In certain cases, in-vitro studies have yielded encouraging results and limited in-vivo studies have been initiated. Plasma activated water has been produced in the laboratory for microbial disinfection, with potential applications in the health sector. Recently, plasmonic nanoparticle arrays which allow detection of very low concentrations of chemicals is studied in detail to allow early-stage detection of diseases. IPR has also been developing AI-based software called DeepCXR and AIBacilli for automated, high-speed screening and detection of footprints of tuberculosis (TB) in Chest X-ray images and for recognizing single/multiple TB bacilli in sputum smear test images, respectively. Deep Learning systems are increasingly being used around the world for analyzing electroencephalogram (EEG) signals for emotion recognition, mental workload, and seizure detection.
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Affiliation(s)
- A Vaid
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - C Patil
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - A Sanghariyat
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - R Rane
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - A Visani
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - S Mukherjee
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | | | - M Ranjan
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - S Augustine
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - K P Sooraj
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - V Rathore
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - S K Nema
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - A Agraj
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - G Garg
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - A Sharma
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - M Sharma
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - K Pansare
- Institute for Plasma Research, Gandhinagar, Gujarat, India
| | - C Murali Krishna
- Advanced Centre for Treatment, Research and Education in Cancer, TMC, Mumbai, Maharashtra, India
| | | | - Sarat Chandra
- Advanced Centre for Treatment, Research and Education in Cancer, TMC, Mumbai, Maharashtra, India
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Mehta S, Fernandez F, Villagrán C, Niklitschek S, Ávila J, Botelho R, Frauenfelder A, Vieira D, Ceschim M, Merchant S, Vaid A, Pinto G, Vallenilla I, Perez Del Nogal G, Prieto L, Luna M, Daher DB, Pisana L, Lee J. ENRICHING ARTIFICIAL INTELLIGENCE ST-ELEVATION MYOCARDIAL INFARCTION (STEMI) DETECTION ALGORITHMS WITH DIFFERENTIAL DIAGNOSES. J Am Coll Cardiol 2020. [DOI: 10.1016/s0735-1097(20)34097-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Mehta S, Fernandez F, Villagran C, Niklitschek S, Avila J, Botelho R, Frauenfelder A, Ceschim M, Vieira D, Merchant S, Vaid A, Pinto G, Vallenilla I, Perez del Nogal G, Prieto L, Daher DB, Luna M, Pisana L, Lee JE, Mazzini J, Vijayan Y. TELEMEDICINE TO GUIDE AMI MANAGEMENT: INTRODUCING A NEW METRIC FOR EVALUATING EFFICIENCY. J Am Coll Cardiol 2020. [DOI: 10.1016/s0735-1097(20)34093-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Mehta S, Fernandez F, Botelho R, Niklitschek S, Villagran C, Avila J, Frauenfelder A, Vieira D, Ceschim M, Merchant S, Vaid A, Pinto G, Vallenilla I, Perez del Nogal G, Prieto L, Luna M, Daher DB, Quintero S, Mazzini J, Vijayan Y. PRAGMATIC LESSONS FOR CONSTRUCTING POPULATION-BASED STEMI NETWORKS USING TELEMEDICINE. J Am Coll Cardiol 2020. [DOI: 10.1016/s0735-1097(20)34238-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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