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Levin MA, Kia A, Timsina P, Cheng FY, Nguyen KAN, Kohli-Seth R, Lin HM, Ouyang Y, Freeman R, Reich DL. Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial. Crit Care Med 2024:00003246-990000000-00296. [PMID: 38380992 DOI: 10.1097/ccm.0000000000006243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
OBJECTIVES Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent directly to front-line providers would reduce escalations. DESIGN Single-center prospective pragmatic nonrandomized clustered clinical trial. SETTING Academic tertiary care medical center. PATIENTS Adult patients admitted to four medical-surgical units. Assignment to intervention or control arms was determined by initial unit admission. INTERVENTIONS Real-time alerts stratified according to predicted likelihood of deterioration sent either to the primary team or directly to the rapid response team (RRT). Clinical care and interventions were at the providers' discretion. For the control units, alerts were generated but not sent, and standard RRT activation criteria were used. MEASUREMENTS AND MAIN RESULTS The primary outcome was the rate of escalation per 1000 patient bed days. Secondary outcomes included the frequency of orders for fluids, medications, and diagnostic tests, and combined in-hospital and 30-day mortality. Propensity score modeling with stabilized inverse probability of treatment weight (IPTW) was used to account for differences between groups. Data from 2740 patients enrolled between July 2019 and March 2020 were analyzed (1488 intervention, 1252 control). Average age was 66.3 years and 1428 participants (52%) were female. The rate of escalation was 12.3 vs. 11.3 per 1000 patient bed days (difference, 1.0; 95% CI, -2.8 to 4.7) and IPTW adjusted incidence rate ratio 1.43 (95% CI, 1.16-1.78; p < 0.001). Patients in the intervention group were more likely to receive cardiovascular medication orders (16.1% vs. 11.3%; 4.7%; 95% CI, 2.1-7.4%) and IPTW adjusted relative risk (RR) (1.74; 95% CI, 1.39-2.18; p < 0.001). Combined in-hospital and 30-day-mortality was lower in the intervention group (7% vs. 9.3%; -2.4%; 95% CI, -4.5% to -0.2%) and IPTW adjusted RR (0.76; 95% CI, 0.58-0.99; p = 0.045). CONCLUSIONS Real-time machine learning alerts do not reduce the rate of escalation but may reduce mortality.
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Affiliation(s)
- Matthew A Levin
- Department of Anesthesiology, Perioperative, and Pain 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
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY
- Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Anesthesiology and Yale Center for Analytical Sciences, Yale School of Medicine, New Haven, CT
| | - Arash Kia
- Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Prem Timsina
- Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Fu-Yuan Cheng
- Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kim-Anh-Nhi Nguyen
- Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Roopa Kohli-Seth
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Hung-Mo Lin
- Department of Anesthesiology and Yale Center for Analytical Sciences, Yale School of Medicine, New Haven, CT
| | - Yuxia Ouyang
- Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Robert Freeman
- Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY
| | - David L Reich
- Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
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Nguyen KAN, Tandon P, Ghanavati S, Cheetirala SN, Timsina P, Freeman R, Reich D, Levin MA, Mazumdar M, Fayad ZA, Kia A. A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation. JMIR Form Res 2023; 7:e46905. [PMID: 37883177 PMCID: PMC10636624 DOI: 10.2196/46905] [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: 03/01/2023] [Revised: 05/18/2023] [Accepted: 06/27/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV. OBJECTIVE We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City. METHODS CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. Variables in the EMR included clinical and laboratory data routinely collected in the inpatient setting. The final fusion model gave a prediction likelihood for the need of intubation within 24 hours as well. RESULTS At a prediction probability threshold of 0.5, the fusion model provided 78.9% (95% CI 59%-96%) sensitivity, 83% (95% CI 76%-89%) specificity, 0.509 (95% CI 0.34-0.67) F1-score, 0.874 (95% CI 0.80-0.94) area under the receiver operating characteristic curve (AUROC), and 0.497 (95% CI 0.32-0.65) area under the precision recall curve (AUPRC) on the holdout set. Compared to the image classifier alone, which had an AUROC of 0.577 (95% CI 0.44-0.73) and an AUPRC of 0.206 (95% CI 0.08-0.38), the fusion model showed significant improvement (P<.001). The most important predictor variables were respiratory rate, C-reactive protein, oxygen saturation, and lactate dehydrogenase. The imaging probability score ranked 15th in overall feature importance. CONCLUSIONS We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19.
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Affiliation(s)
- Kim-Anh-Nhi Nguyen
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Pranai Tandon
- Department of Medicine Division of Pulmonary, Critical Care, and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Sahar Ghanavati
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Satya Narayana Cheetirala
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Prem Timsina
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Robert Freeman
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Hospital Administration, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - David Reich
- Hospital Administration, 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
| | - Matthew A Levin
- Department of Anesthesiology, Perioperative and Pain Medicine, 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
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Madhu Mazumdar
- Institute for Healthcare Delivery Science, 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
| | - 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
| | - Arash Kia
- Institute for Healthcare Delivery Science, 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
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3
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Patel D, Cheetirala SN, Raut G, Tamegue J, Kia A, Glicksberg B, Freeman R, Levin MA, Timsina P, Klang E. Predicting Adult Hospital Admission from Emergency Department Using Machine Learning: An Inclusive Gradient Boosting Model. J Clin Med 2022; 11:jcm11236888. [PMID: 36498463 PMCID: PMC9740100 DOI: 10.3390/jcm11236888] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/24/2022] [Accepted: 11/15/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND AIM We analyzed an inclusive gradient boosting model to predict hospital admission from the emergency department (ED) at different time points. We compared its results to multiple models built exclusively at each time point. METHODS This retrospective multisite study utilized ED data from the Mount Sinai Health System, NY, during 2015-2019. Data included tabular clinical features and free-text triage notes represented using bag-of-words. A full gradient boosting model, trained on data available at different time points (30, 60, 90, 120, and 150 min), was compared to single models trained exclusively at data available at each time point. This was conducted by concatenating the rows of data available at each time point to one data matrix for the full model, where each row is considered a separate case. RESULTS The cohort included 1,043,345 ED visits. The full model showed comparable results to the single models at all time points (AUCs 0.84-0.88 for different time points for both the full and single models). CONCLUSION A full model trained on data concatenated from different time points showed similar results to single models trained at each time point. An ML-based prediction model can use used for identifying hospital admission.
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Affiliation(s)
- Dhavalkumar Patel
- Mount Sinai Health System, New York, NY 10017, USA
- Correspondence: (D.P.); (E.K.)
| | | | - Ganesh Raut
- Mount Sinai Health System, New York, NY 10017, USA
| | | | - Arash Kia
- Mount Sinai Health System, New York, NY 10017, USA
| | | | | | - Matthew A. Levin
- Mount Sinai Health System, New York, NY 10017, USA
- Department of Anesthesiology, Perioperative and Pain Management, Mount Sinai Hospital, New York, NY 10017, USA
| | - Prem Timsina
- Mount Sinai Health System, New York, NY 10017, USA
| | - Eyal Klang
- Mount Sinai Health System, New York, NY 10017, USA
- Correspondence: (D.P.); (E.K.)
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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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5
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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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6
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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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7
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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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8
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Parchure P, Joshi H, Dharmarajan K, Freeman R, Reich DL, Mazumdar M, Timsina P, Kia A. Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19. BMJ Support Palliat Care 2020; 12:bmjspcare-2020-002602. [PMID: 32963059 PMCID: PMC8049537 DOI: 10.1136/bmjspcare-2020-002602] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [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: 07/28/2020] [Revised: 08/09/2020] [Accepted: 08/18/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records. METHODS A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results. RESULTS Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%). CONCLUSIONS Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.
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Affiliation(s)
- Prathamesh Parchure
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Himanshu Joshi
- Institute for Healthcare Delivery Science, 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, United States
| | - Kavita Dharmarajan
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Geriatrics and Palliative Care, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Robert Freeman
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Hospital Administration, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - David L Reich
- Hospital Administration, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Department of Anesthesiology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Madhu Mazumdar
- Institute for Healthcare Delivery Science, 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, United States
| | - Prem Timsina
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Arash Kia
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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9
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Nadkarni GN, Lala A, Bagiella E, Chang HL, Moreno PR, Pujadas E, Arvind V, Bose S, Charney AW, Chen MD, Cordon-Cardo C, Dunn AS, Farkouh ME, Glicksberg BS, Kia A, Kohli-Seth R, Levin MA, Timsina P, Zhao S, Fayad ZA, Fuster V. Anticoagulation, Bleeding, Mortality, and Pathology in Hospitalized Patients With COVID-19. J Am Coll Cardiol 2020; 76:1815-1826. [PMID: 32860872 PMCID: PMC7449655 DOI: 10.1016/j.jacc.2020.08.041] [Citation(s) in RCA: 327] [Impact Index Per Article: 81.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 08/20/2020] [Accepted: 08/20/2020] [Indexed: 12/22/2022]
Abstract
Background Thromboembolic disease is common in coronavirus disease-2019 (COVID-19). There is limited evidence on the association of in-hospital anticoagulation (AC) with outcomes and postmortem findings. Objectives The purpose of this study was to examine association of AC with in-hospital outcomes and describe thromboembolic findings on autopsies. Methods This retrospective analysis examined the association of AC with mortality, intubation, and major bleeding. Subanalyses were also conducted on the association of therapeutic versus prophylactic AC initiated ≤48 h from admission. Thromboembolic disease was contextualized by premortem AC among consecutive autopsies. Results Among 4,389 patients, median age was 65 years with 44% women. Compared with no AC (n = 1,530; 34.9%), therapeutic AC (n = 900; 20.5%) and prophylactic AC (n = 1,959; 44.6%) were associated with lower in-hospital mortality (adjusted hazard ratio [aHR]: 0.53; 95% confidence interval [CI]: 0.45 to 0.62 and aHR: 0.50; 95% CI: 0.45 to 0.57, respectively), and intubation (aHR: 0.69; 95% CI: 0.51 to 0.94 and aHR: 0.72; 95% CI: 0.58 to 0.89, respectively). When initiated ≤48 h from admission, there was no statistically significant difference between therapeutic (n = 766) versus prophylactic AC (n = 1,860) (aHR: 0.86; 95% CI: 0.73 to 1.02; p = 0.08). Overall, 89 patients (2%) had major bleeding adjudicated by clinician review, with 27 of 900 (3.0%) on therapeutic, 33 of 1,959 (1.7%) on prophylactic, and 29 of 1,530 (1.9%) on no AC. Of 26 autopsies, 11 (42%) had thromboembolic disease not clinically suspected and 3 of 11 (27%) were on therapeutic AC. Conclusions AC was associated with lower mortality and intubation among hospitalized COVID-19 patients. Compared with prophylactic AC, therapeutic AC was associated with lower mortality, although not statistically significant. Autopsies revealed frequent thromboembolic disease. These data may inform trials to determine optimal AC regimens.
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Affiliation(s)
- Girish N Nadkarni
- Mount Sinai Covid Informatics Center, New York, New York; Department of Medicine, 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; The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. https://twitter.com/girish_nadkarni
| | - Anuradha Lala
- Mount Sinai Covid Informatics Center, New York, New York; Department of Population Health Science and Policy, 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.
| | - Emilia Bagiella
- Department of Population Health Science and Policy, 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 Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. https://twitter.com/emiliabagiella
| | - Helena L Chang
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York; The Center for Biostatistics at the Icahn School of Medicine at Mount Sinai, New York, New York
| | - Pedro R Moreno
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Elisabet Pujadas
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Varun Arvind
- The Center for Biostatistics at the Icahn School of Medicine at Mount Sinai, New York, New York; Department of Orthopedics, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Sonali Bose
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alexander W Charney
- Mount Sinai Covid Informatics Center, New York, New York; Department of Psychiatry, 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
| | - Martin D Chen
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Carlos Cordon-Cardo
- Department of Pathology, Molecular and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrew S Dunn
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Michael E Farkouh
- Peter Munk Cardiac Centre and the Heart and Stroke Richard Lewar Centre of Excellence, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin S Glicksberg
- Mount Sinai Covid Informatics Center, 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
| | - Arash Kia
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Roopa Kohli-Seth
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew A Levin
- Mount Sinai Covid Informatics Center, New York, New York; Department of Anesthesiology, Perioperative and Pain Medicine, 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
| | - Shan Zhao
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Zahi A Fayad
- Mount Sinai Covid Informatics Center, New York, New York; The Zena and Michael A. Wiener Cardiovascular Institute, 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 Diagnostic, Molecular and Interventional Radiology, 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; Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain.
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10
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Timsina P, Joshi HN, Cheng FY, Kersch I, Wilson S, Colgan C, Freeman R, Reich DL, Mechanick J, Mazumdar M, Levin MA, Kia A. MUST-Plus: A Machine Learning Classifier That Improves Malnutrition Screening in Acute Care Facilities. J Am Coll Nutr 2020; 40:3-12. [PMID: 32701397 DOI: 10.1080/07315724.2020.1774821] [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: 12/27/2022]
Abstract
OBJECTIVE Malnutrition among hospital patients, a frequent, yet under-diagnosed problem is associated with adverse impact on patient outcome and health care costs. Development of highly accurate malnutrition screening tools is, therefore, essential for its timely detection, for providing nutritional care, and for addressing the concerns related to the suboptimal predictive value of the conventional screening tools, such as the Malnutrition Universal Screening Tool (MUST). We aimed to develop a machine learning (ML) based classifier (MUST-Plus) for more accurate prediction of malnutrition. METHOD A retrospective cohort with inpatient data consisting of anthropometric, lab biochemistry, clinical data, and demographics from adult (≥ 18 years) admissions at a large tertiary health care system between January 2017 and July 2018 was used. The registered dietitian (RD) nutritional assessments were used as the gold standard outcome label. The cohort was randomly split (70:30) into training and test sets. A random forest model was trained using 10-fold cross-validation on training set, and its predictive performance on test set was compared to MUST. RESULTS In all, 13.3% of admissions were associated with malnutrition in the test cohort. MUST-Plus provided 73.07% (95% confidence interval [CI]: 69.61%-76.33%) sensitivity, 76.89% (95% CI: 75.64%-78.11%) specificity, and 83.5% (95% CI: 82.0%-85.0%) area under the receiver operating curve (AUC). Compared to classic MUST, MUST-Plus demonstrated 30% higher sensitivity, 6% higher specificity, and 17% increased AUC. CONCLUSIONS ML-based MUST-Plus provided superior performance in identifying malnutrition compared to the classic MUST. The tool can be used for improving the operational efficiency of RDs by timely referrals of high-risk patients.
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Affiliation(s)
- Prem Timsina
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Himanshu N Joshi
- Institute for Healthcare Delivery Science, 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
| | - Fu-Yuan Cheng
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ilana Kersch
- Clinical Nutrition, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sara Wilson
- Clinical Nutrition, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Claudia Colgan
- Hospital Administration, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert Freeman
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Hospital Administration, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - David L Reich
- Hospital Administration, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jeffrey Mechanick
- Divisions of Cardiology and Endocrinology, Diabetes and Bone Disease, All at the Icahn School of Medicine at Mount Sinai, NY, New York, USA
| | - Madhu Mazumdar
- Institute for Healthcare Delivery Science, 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
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Arash Kia
- Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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11
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Cheng FY, Joshi H, Tandon P, Freeman R, Reich DL, Mazumdar M, Kohli-Seth R, Levin MA, Timsina P, Kia A. Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients. J Clin Med 2020; 9:jcm9061668. [PMID: 32492874 PMCID: PMC7356638 DOI: 10.3390/jcm9061668] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [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: 05/12/2020] [Revised: 05/27/2020] [Accepted: 05/28/2020] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES Approximately 20-30% of patients with COVID-19 require hospitalization, and 5-12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers' efforts and help hospitals plan their flow of operations. METHODS A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. RESULTS The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2-81.1%) sensitivity, 76.3% (95% CI: 74.7-77.9%) specificity, 76.2% (95% CI: 74.6-77.7%) accuracy, and 79.9% (95% CI: 75.2-84.6%) area under the receiver operating characteristics curve. CONCLUSIONS A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.
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Affiliation(s)
- Fu-Yuan Cheng
- Institute for Healthcare Delivery Science; Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA; (F.-Y.C.); (H.J.); (R.F.); (P.T.); (A.K.)
| | - Himanshu Joshi
- Institute for Healthcare Delivery Science; Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA; (F.-Y.C.); (H.J.); (R.F.); (P.T.); (A.K.)
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA
| | - Pranai Tandon
- Respiratory Institute, Icahn School of Medicine at Mount Sinai, 10 E 102nd St, New York, NY 10029, USA;
| | - Robert Freeman
- Institute for Healthcare Delivery Science; Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA; (F.-Y.C.); (H.J.); (R.F.); (P.T.); (A.K.)
- Hospital Administration; The Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA;
| | - David L Reich
- Hospital Administration; The Mount Sinai Hospital, 1 Gustave L. Levy Place, New York, NY 10029, USA;
- Department of Anesthesiology, Perioperative and Pain Medicine, 1 Gustave L. Levy Place, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Madhu Mazumdar
- Institute for Healthcare Delivery Science; Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA; (F.-Y.C.); (H.J.); (R.F.); (P.T.); (A.K.)
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA
- Correspondence: ; Tel.: +1-212-659-1470; Fax: +1-212-423-2998
| | - Roopa Kohli-Seth
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Matthew A. Levin
- Department of Anesthesiology, Perioperative and Pain Medicine, 1 Gustave L. Levy Place, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
- Department of Genetics and Genomic Sciences, 1 Gustave L. Levy Place, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Prem Timsina
- Institute for Healthcare Delivery Science; Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA; (F.-Y.C.); (H.J.); (R.F.); (P.T.); (A.K.)
| | - Arash Kia
- Institute for Healthcare Delivery Science; Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 10029, USA; (F.-Y.C.); (H.J.); (R.F.); (P.T.); (A.K.)
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12
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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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13
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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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14
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Kia A, Timsina P, Joshi HN, Klang E, Gupta RR, Freeman RM, Reich DL, Tomlinson MS, Dudley JT, Kohli-Seth R, Mazumdar M, Levin MA. MEWS++: Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model. J Clin Med 2020; 9:jcm9020343. [PMID: 32012659 PMCID: PMC7073544 DOI: 10.3390/jcm9020343] [Citation(s) in RCA: 24] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 01/08/2020] [Accepted: 01/17/2020] [Indexed: 01/21/2023] Open
Abstract
Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models’ performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.
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Affiliation(s)
- Arash Kia
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Prem Timsina
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Himanshu N. Joshi
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Eyal Klang
- Department of Diagnostic Imaging, The Chaim Sheba Medical Center at Tel HaShomer, Sackler Faculty of Medicine, Tel Aviv University, Ramat Gan 52662, Israel
| | - Rohit R. Gupta
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Robert M. Freeman
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - David L Reich
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Max S Tomlinson
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Joel T Dudley
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Roopa Kohli-Seth
- Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Madhu Mazumdar
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Matthew A Levin
- Institute for Healthcare Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Correspondence: ; Tel.: +212-241-8382
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El-Gayar O, Timsina P, Nawar N, Eid W. A systematic review of IT for diabetes self-management: are we there yet? Int J Med Inform 2013; 82:637-52. [PMID: 23792137 DOI: 10.1016/j.ijmedinf.2013.05.006] [Citation(s) in RCA: 112] [Impact Index Per Article: 10.2] [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: 10/03/2012] [Revised: 03/18/2013] [Accepted: 05/01/2013] [Indexed: 01/14/2023]
Abstract
BACKGROUND Recent advances in information technology (IT) coupled with the increased ubiquitous nature of information technology (IT) present unique opportunities for improving diabetes self-management. The objective of this paper is to determine, in a systematic review, how IT has been used to improve self-management for adults with Type 1 and Type 2 diabetes. METHODS The review covers articles extracted from relevant databases using search terms related information technology and diabetes self-management published after 1970 until August 2012. Additional articles were extracted using the citation map in Web of Science. Articles representing original research describing the use of IT as an enabler for self-management tasks performed by the patient are included in the final analysis. RESULTS Overall, 74% of studies showed some form of added benefit, 13% articles showed no-significant value provided by IT, and 13% of articles did not clearly define the added benefit due to IT. Information technologies used included the Internet (47%), cellular phones (32%), telemedicine (12%), and decision support techniques (9%). Limitations and research gaps identified include usability, real-time feedback, integration with provider electronic medical record (EMR), as well as analytics and decision support capabilities. CONCLUSION There is a distinct need for more comprehensive interventions, in which several technologies are integrated in order to be able to manage chronic conditions such as diabetes. Such IT interventions should be theoretically founded and should rely on principles of user-centered and socio-technical design in its planning, design and implementation. Moreover, the effectiveness of self-management systems should be assessed along multiple dimensions: motivation for self-management, long-term adherence, cost, adoption, satisfaction and outcomes as a final result.
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Affiliation(s)
- Omar El-Gayar
- College of Business and Information Systems, Dakota State University, Madison, SD, USA.
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Abstract
BACKGROUND Advancements in smartphone technology coupled with the proliferation of data connectivity has resulted in increased interest and unprecedented growth in mobile applications for diabetes self-management. The objective of this article is to determine, in a systematic review, whether diabetes applications have been helping patients with type 1 or type 2 diabetes self-manage their condition and to identify issues necessary for large-scale adoption of such interventions. METHODS The review covers commercial applications available on the Apple App Store (as a representative of commercially available applications) and articles published in relevant databases covering a period from January 1995 to August 2012. The review included all applications supporting any diabetes self-management task where the patient is the primary actor. RESULTS Available applications support self-management tasks such as physical exercise, insulin dosage or medication, blood glucose testing, and diet. Other support tasks considered include decision support, notification/alert, tagging of input data, and integration with social media. The review points to the potential for mobile applications to have a positive impact on diabetes self-management. Analysis indicates that application usage is associated with improved attitudes favorable to diabetes self-management. Limitations of the applications include lack of personalized feedback; usability issues, particularly the ease of data entry; and integration with patients and electronic health records. CONCLUSIONS Research into the adoption and use of user-centered and sociotechnical design principles is needed to improve usability, perceived usefulness, and, ultimately, adoption of the technology. Proliferation and efficacy of interventions involving mobile applications will benefit from a holistic approach that takes into account patients' expectations and providers' needs.
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Affiliation(s)
- Omar El-Gayar
- College of Business and Information Systems, Dakota State University, Madison, South Dakota 57402, USA.
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