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Kwong JCC, Wu J, Malik S, Khondker A, Gupta N, Bodnariuc N, Narayana K, Malik M, van der Kwast TH, Johnson AEW, Zlotta AR, Kulkarni GS. Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI. NPJ Digit Med 2024; 7:98. [PMID: 38637674 PMCID: PMC11026453 DOI: 10.1038/s41746-024-01088-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 03/29/2024] [Indexed: 04/20/2024] Open
Abstract
Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32-93) and median cohort size of 125 (IQR 93-309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.
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Affiliation(s)
- Jethro C C Kwong
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Jeremy Wu
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Shamir Malik
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Adree Khondker
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Naveen Gupta
- Georgetown University School of Medicine, Georgetown University, Washington, DC, USA
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Nicole Bodnariuc
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - Mikail Malik
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Theodorus H van der Kwast
- Laboratory Medicine Program, University Health Network, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, Canada
| | - Alistair E W Johnson
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Alexandre R Zlotta
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Division of Urology, Department of Surgery, Mount Sinai Hospital, Sinai Health System, Toronto, ON, Canada
- Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Girish S Kulkarni
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada.
- Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
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Xu J, Mazwi M, Johnson AEW. AnnoDash, a clinical terminology annotation dashboard. JAMIA Open 2023; 6:ooad046. [PMID: 37425489 PMCID: PMC10329488 DOI: 10.1093/jamiaopen/ooad046] [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: 06/03/2023] [Revised: 06/07/2023] [Accepted: 06/30/2023] [Indexed: 07/11/2023] Open
Abstract
Background Standard ontologies are critical for interoperability and multisite analyses of health data. Nevertheless, mapping concepts to ontologies is often done with generic tools and is labor-intensive. Contextualizing candidate concepts within source data is also done in an ad hoc manner. Methods and Results We present AnnoDash, a flexible dashboard to support annotation of concepts with terms from a given ontology. Text-based similarity is used to identify likely matches, and large language models are used to improve ontology ranking. A convenient interface is provided to visualize observations associated with a concept, supporting the disambiguation of vague concept descriptions. Time-series plots contrast the concept with known clinical measurements. We evaluated the dashboard qualitatively against several ontologies (SNOMED CT, LOINC, etc.) by using MIMIC-IV measurements. The dashboard is web-based and step-by-step instructions for deployment are provided, simplifying usage for nontechnical audiences. The modular code structure enables users to extend upon components, including improving similarity scoring, constructing new plots, or configuring new ontologies. Conclusion AnnoDash, an improved clinical terminology annotation tool, can facilitate data harmonizing by promoting mapping of clinical data. AnnoDash is freely available at https://github.com/justin13601/AnnoDash (https://doi.org/10.5281/zenodo.8043943).
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Affiliation(s)
- Justin Xu
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Mjaye Mazwi
- Department of Critical Care Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alistair E W Johnson
- Corresponding Author: Alistair E. W. Johnson, DPhil, Child Health Evaluative Sciences, The Hospital for Sick Children, 686 Bay Street, Toronto, ON M5G 0A4, Canada;
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Kwong JCC, Khondker A, Lajkosz K, McDermott MBA, Frigola XB, McCradden MD, Mamdani M, Kulkarni GS, Johnson AEW. APPRAISE-AI Tool for Quantitative Evaluation of AI Studies for Clinical Decision Support. JAMA Netw Open 2023; 6:e2335377. [PMID: 37747733 PMCID: PMC10520738 DOI: 10.1001/jamanetworkopen.2023.35377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/14/2023] [Indexed: 09/26/2023] Open
Abstract
Importance Artificial intelligence (AI) has gained considerable attention in health care, yet concerns have been raised around appropriate methods and fairness. Current AI reporting guidelines do not provide a means of quantifying overall quality of AI research, limiting their ability to compare models addressing the same clinical question. Objective To develop a tool (APPRAISE-AI) to evaluate the methodological and reporting quality of AI prediction models for clinical decision support. Design, Setting, and Participants This quality improvement study evaluated AI studies in the model development, silent, and clinical trial phases using the APPRAISE-AI tool, a quantitative method for evaluating quality of AI studies across 6 domains: clinical relevance, data quality, methodological conduct, robustness of results, reporting quality, and reproducibility. These domains included 24 items with a maximum overall score of 100 points. Points were assigned to each item, with higher points indicating stronger methodological or reporting quality. The tool was applied to a systematic review on machine learning to estimate sepsis that included articles published until September 13, 2019. Data analysis was performed from September to December 2022. Main Outcomes and Measures The primary outcomes were interrater and intrarater reliability and the correlation between APPRAISE-AI scores and expert scores, 3-year citation rate, number of Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) low risk-of-bias domains, and overall adherence to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. Results A total of 28 studies were included. Overall APPRAISE-AI scores ranged from 33 (low quality) to 67 (high quality). Most studies were moderate quality. The 5 lowest scoring items included source of data, sample size calculation, bias assessment, error analysis, and transparency. Overall APPRAISE-AI scores were associated with expert scores (Spearman ρ, 0.82; 95% CI, 0.64-0.91; P < .001), 3-year citation rate (Spearman ρ, 0.69; 95% CI, 0.43-0.85; P < .001), number of QUADAS-2 low risk-of-bias domains (Spearman ρ, 0.56; 95% CI, 0.24-0.77; P = .002), and adherence to the TRIPOD statement (Spearman ρ, 0.87; 95% CI, 0.73-0.94; P < .001). Intraclass correlation coefficient ranges for interrater and intrarater reliability were 0.74 to 1.00 for individual items, 0.81 to 0.99 for individual domains, and 0.91 to 0.98 for overall scores. Conclusions and Relevance In this quality improvement study, APPRAISE-AI demonstrated strong interrater and intrarater reliability and correlated well with several study quality measures. This tool may provide a quantitative approach for investigators, reviewers, editors, and funding organizations to compare the research quality across AI studies for clinical decision support.
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Affiliation(s)
- Jethro C. C. Kwong
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Adree Khondker
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Katherine Lajkosz
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Biostatistics, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | | | - Xavier Borrat Frigola
- Laboratory for Computational Physiology, Harvard–Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge
- Anesthesiology and Critical Care Department, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Melissa D. McCradden
- Department of Bioethics, The Hospital for Sick Children, Toronto, Ontario, Canada
- Genetics & Genome Biology Research Program, Peter Gilgan Centre for Research and Learning, Toronto, Ontario, Canada
- Division of Clinical and Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Muhammad Mamdani
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
- Data Science and Advanced Analytics, Unity Health Toronto, Toronto, Ontario, Canada
| | - Girish S. Kulkarni
- Division of Urology, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Alistair E. W. Johnson
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Child Health Evaluative Sciences, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
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Rincon TA, Raffa J, Celi LA, Badawi O, Johnson AEW, Pollard T, Deliberato RO, Pierce JD. Evaluation of evolving sepsis screening criteria in discriminating suspected sepsis and mortality among adult patients admitted to the intensive care unit. Int J Nurs Stud 2023; 145:104529. [PMID: 37307638 DOI: 10.1016/j.ijnurstu.2023.104529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/08/2023] [Accepted: 05/14/2023] [Indexed: 06/14/2023]
Abstract
BACKGROUND Institutions struggle with successful use of sepsis alerts within electronic health records. OBJECTIVE Test the association of sepsis screening measurement criteria in discrimination of mortality and detection of sepsis in a large dataset. DESIGN Retrospective, cohort study using a large United States (U.S.) intensive care database. The Institutional Review Board exempt status was obtained from Kansas University Medical Center Human Research Protection Program (10-1-2015). SETTING 334 U.S. hospitals participating in the eICU Research Institute. PARTICIPANTS Nine hundred twelve thousand five hundred and nine adult intensive care admissions from 183 hospitals. METHODS Exposures included: systemic inflammatory response syndrome criteria ≥ 2 (Sepsis-1); systemic inflammatory response syndrome criteria with organ failure criteria ≥ 3.5 points (Sepsis-2); and sepsis-related organ failure assessment score ≥ 2 and quick score ≥ 2 (Sepsis-3). Discrimination of outcomes was determined with/without (adjusted/unadjusted) baseline risk exposure to a model. The receiver operating characteristic curve (AUROC) and odds ratios (ORs) for each decile of baseline risk of sepsis or death were assessed. RESULTS Within the eligible cohort of 912,509, a total of 86,219 (9.4 %) patients did not survive their hospital stay and 186,870 (20.5 %) met the definition of suspected sepsis. For suspected sepsis discrimination, Sepsis-2 (unadjusted AUROC 0.67, 99 % CI: 0.66-0.67 and adjusted AUROC 0.77, 99 % CI: 0.77-0.77) outperformed Sepsis-3 (SOFA unadjusted AUROC 0.61, 99 % CI: 0.61-0.61 and adjusted AUROC 0.74, 99 % CI: 0.74-0.74) (qSOFA unadjusted AUROC 0.59, 99 % CI: 0.59-0.60 and adjusted AUROC 0.73, 99 % CI: 0.73-0.73). Sepsis-2 also outperformed Sepsis-1 (unadjusted AUROC 0.58, 99 % CI: 0.58-0.58 and adjusted AUROC 0.73, 99 % CI: 0.73-0.73). In between differences of AUROCs were statistically significantly different. Sepsis-2 ORs were higher for the outcome of suspected sepsis when considering deciles of risk than the other measurement systems. CONCLUSIONS AND RELEVANCE Sepsis-2 outperformed other systems in suspected sepsis detection and was comparable to SOFA in prognostic accuracy of mortality in adult intensive care patients.
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Affiliation(s)
- Teresa A Rincon
- UMass Chan Medical School, Tan Chingfen Graduate School of Nursing, 55 Lake Ave, North Worcester, MA 01655, USA; Blue Cirrus Consulting, 8595 Pelham Rd #400-402, Greenville, SC 29615, USA.
| | - Jesse Raffa
- Laboratory for Computational Physiology, Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA
| | - Omar Badawi
- Laboratory for Computational Physiology, Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Pharmacy Practice and Science, University of Maryland School of Pharmacy, Baltimore, MD 21201, USA
| | - Alistair E W Johnson
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research & Learning, The Hospital for Sick Children, 686 Bay St., Toronto, ON M5G 0A4, Canada
| | - Tom Pollard
- Laboratory for Computational Physiology, Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rodrigo Octávio Deliberato
- Laboratory for Computational Physiology, Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Janet D Pierce
- University of Kansas, School of Nursing, Kansas City, KS 66160, USA
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Chen AY, Deng CY, Calvachi-Prieto P, Armengol de la Hoz MÁ, Khazi-Syed A, Chen C, Scurlock C, Becker CD, Johnson AEW, Celi LA, Dagan A. A Large-Scale Multicenter Retrospective Study on Nephrotoxicity Associated With Empiric Broad-Spectrum Antibiotics in Critically Ill Patients. Chest 2023; 164:355-368. [PMID: 37040818 PMCID: PMC10475819 DOI: 10.1016/j.chest.2023.03.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/13/2023] Open
Abstract
BACKGROUND Evidence regarding acute kidney injury associated with concomitant administration of vancomycin and piperacillin-tazobactam is conflicting, particularly in patients in the ICU. RESEARCH QUESTION Does a difference exist in the association between commonly prescribed empiric antibiotics on ICU admission (vancomycin and piperacillin-tazobactam, vancomycin and cefepime, and vancomycin and meropenem) and acute kidney injury? STUDY DESIGN AND METHODS This was a retrospective cohort study using data from the eICU Research Institute, which contains records for ICU stays between 2010 and 2015 across 335 hospitals. Patients were enrolled if they received vancomycin and piperacillin-tazobactam, vancomycin and cefepime, or vancomycin and meropenem exclusively. Patients initially admitted to the ED were included. Patients with hospital stay duration of < 1 h, receiving dialysis, or with missing data were excluded. Acute kidney injury was defined as Kidney Disease: Improving Global Outcomes stage 2 or 3 based on serum creatinine component. Propensity score matching was used to match patients in the control (vancomycin and meropenem or vancomycin and cefepime) and treatment (vancomycin and piperacillin-tazobactam) groups, and ORs were calculated. Sensitivity analyses were performed to study the effect of longer courses of combination therapy and patients with renal insufficiency on admission. RESULTS Thirty-five thousand six hundred fifty-four patients met inclusion criteria (vancomycin and piperacillin-tazobactam, n = 27,459; vancomycin and cefepime, n = 6,371; vancomycin and meropenem, n = 1,824). Vancomycin and piperacillin-tazobactam was associated with a higher risk of acute kidney injury and initiation of dialysis when compared with that of both vancomycin and cefepime (Acute kidney injury: OR, 1.37 [95% CI, 1.25-1.49]; dialysis: OR, 1.28 [95% CI, 1.14-1.45]) and vancomycin and meropenem (Acute kidney injury: OR, 1.27 [95%, 1.06-1.52]; dialysis: OR, 1.56 [95% CI, 1.23-2.00]). The odds of acute kidney injury developing was especially pronounced in patients without renal insufficiency receiving a longer duration of vancomycin and piperacillin-tazobactam therapy compared with vancomycin and meropenem therapy. INTERPRETATION VPT is associated with a higher risk of acute kidney injury than both vancomycin and cefepime and vancomycin and meropenem in patients in the ICU, especially for patients with normal initial kidney function requiring longer durations of therapy. Clinicians should consider vancomycin and meropenem or vancomycin and cefepime to reduce the risk of nephrotoxicity for patients in the ICU.
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Affiliation(s)
- Alyssa Y Chen
- The University of Texas Southwestern Medical School, Dallas, TX; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA
| | - Chih-Ying Deng
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; Department of Bioinformatics, Harvard Medical School, Massachusetts General Hospital, Boston, MA
| | - Paola Calvachi-Prieto
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; Department of Bioinformatics, Harvard Medical School, Massachusetts General Hospital, Boston, MA
| | - Miguel Ángel Armengol de la Hoz
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; Cardiovascular Research Center, Harvard Medical School, Massachusetts General Hospital, Boston, MA; Biomedical Engineering and Telemedicine Group, Biomedical Technology Centre CTB, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Christina Chen
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA; Department of Medicine, University of California, San Francisco and San Francisco VA Health Care System, San Francisco, CA
| | - Corey Scurlock
- Department of Medicine and eHealth Center, New York Medical College/Westchester Medical Center, Valhalla, NY
| | - Christian D Becker
- Department of Medicine and eHealth Center, New York Medical College/Westchester Medical Center, Valhalla, NY
| | - Alistair E W Johnson
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; Department of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Alon Dagan
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA; Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA.
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Moharrami M, Farmer J, Singhal S, Watson E, Glogauer M, Johnson AEW, Schwendicke F, Quinonez C. Detecting dental caries on oral photographs using artificial intelligence: A systematic review. Oral Dis 2023. [PMID: 37392423 DOI: 10.1111/odi.14659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/19/2023] [Accepted: 06/15/2023] [Indexed: 07/03/2023]
Abstract
OBJECTIVES This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs. METHODS Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus. RESULTS Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1-scores for classification and detection tasks were 68.3%-94.3% and 42.8%-95.4%, respectively. Irrespective of the task, F1-scores were 68.3%-95.4% for professional cameras, 78.8%-87.6%, for intraoral cameras, and 42.8%-80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity. CONCLUSION Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient-clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions.
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Affiliation(s)
- Mohammad Moharrami
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Julie Farmer
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
| | - Sonica Singhal
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Health Promotion, Chronic Disease and Injury Prevention Department, Public Health Ontario, Toronto, Canada
| | - Erin Watson
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Michael Glogauer
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Dentistry, Centre for Advanced Dental Research and Care, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Alistair E W Johnson
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
- Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carlos Quinonez
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
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Kwong JCC, Khondker A, Meng E, Taylor N, Kuk C, Perlis N, Kulkarni GS, Hamilton RJ, Fleshner NE, Finelli A, van der Kwast TH, Ali A, Jamal M, Papanikolaou F, Short T, Srigley JR, Colinet V, Peltier A, Diamand R, Lefebvre Y, Mandoorah Q, Sanchez-Salas R, Macek P, Cathelineau X, Eklund M, Johnson AEW, Feifer A, Zlotta AR. Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study. Lancet Digit Health 2023; 5:e435-e445. [PMID: 37211455 DOI: 10.1016/s2589-7500(23)00067-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 02/11/2023] [Accepted: 03/22/2023] [Indexed: 05/23/2023]
Abstract
BACKGROUND Accurate prediction of side-specific extraprostatic extension (ssEPE) is essential for performing nerve-sparing surgery to mitigate treatment-related side-effects such as impotence and incontinence in patients with localised prostate cancer. Artificial intelligence (AI) might provide robust and personalised ssEPE predictions to better inform nerve-sparing strategy during radical prostatectomy. We aimed to develop, externally validate, and perform an algorithmic audit of an AI-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA). METHODS Each prostatic lobe was treated as an individual case such that each patient contributed two cases to the overall cohort. SEPERA was trained on 1022 cases from a community hospital network (Trillium Health Partners; Mississauga, ON, Canada) between 2010 and 2020. Subsequently, SEPERA was externally validated on 3914 cases across three academic centres: Princess Margaret Cancer Centre (Toronto, ON, Canada) from 2008 to 2020; L'Institut Mutualiste Montsouris (Paris, France) from 2010 to 2020; and Jules Bordet Institute (Brussels, Belgium) from 2015 to 2020. Model performance was characterised by area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), calibration, and net benefit. SEPERA was compared against contemporary nomograms (ie, Sayyid nomogram, Soeterik nomogram [non-MRI and MRI]), as well as a separate logistic regression model using the same variables included in SEPERA. An algorithmic audit was performed to assess model bias and identify common patient characteristics among predictive errors. FINDINGS Overall, 2468 patients comprising 4936 cases (ie, prostatic lobes) were included in this study. SEPERA was well calibrated and had the best performance across all validation cohorts (pooled AUROC of 0·77 [95% CI 0·75-0·78] and pooled AUPRC of 0·61 [0·58-0·63]). In patients with pathological ssEPE despite benign ipsilateral biopsies, SEPERA correctly predicted ssEPE in 72 (68%) of 106 cases compared with the other models (47 [44%] in the logistic regression model, none in the Sayyid model, 13 [12%] in the Soeterik non-MRI model, and five [5%] in the Soeterik MRI model). SEPERA had higher net benefit than the other models to predict ssEPE, enabling more patients to safely undergo nerve-sparing. In the algorithmic audit, no evidence of model bias was observed, with no significant difference in AUROC when stratified by race, biopsy year, age, biopsy type (systematic only vs systematic and MRI-targeted biopsy), biopsy location (academic vs community), and D'Amico risk group. According to the audit, the most common errors were false positives, particularly for older patients with high-risk disease. No aggressive tumours (ie, grade >2 or high-risk disease) were found among false negatives. INTERPRETATION We demonstrated the accuracy, safety, and generalisability of using SEPERA to personalise nerve-sparing approaches during radical prostatectomy. FUNDING None.
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Affiliation(s)
- Jethro C C Kwong
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Adree Khondker
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Eric Meng
- Faculty of Medicine, Queen's University, Kingston, ON, Canada
| | - Nicholas Taylor
- Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Cynthia Kuk
- Division of Urology, Department of Surgery, Mount Sinai Hospital, Sinai Health System, Toronto, ON, Canada
| | - Nathan Perlis
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Girish S Kulkarni
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada
| | - Robert J Hamilton
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Neil E Fleshner
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Antonio Finelli
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Theodorus H van der Kwast
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; Laboratory Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Amna Ali
- Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada
| | - Munir Jamal
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Frank Papanikolaou
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Thomas Short
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - John R Srigley
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
| | - Valentin Colinet
- Division of Urology, Department of Surgery, Jules Bordet Institute, Brussels, Belgium
| | - Alexandre Peltier
- Division of Urology, Department of Surgery, Jules Bordet Institute, Brussels, Belgium
| | - Romain Diamand
- Division of Urology, Department of Surgery, Jules Bordet Institute, Brussels, Belgium
| | - Yolene Lefebvre
- Department of Medical Imagery, Jules Bordet Institute, Brussels, Belgium
| | - Qusay Mandoorah
- Division of Urology, Department of Surgery, L'Institut Mutualiste Montsouris, Paris, France
| | - Rafael Sanchez-Salas
- Division of Urology, Department of Surgery, L'Institut Mutualiste Montsouris, Paris, France
| | - Petr Macek
- Division of Urology, Department of Surgery, L'Institut Mutualiste Montsouris, Paris, France
| | - Xavier Cathelineau
- Division of Urology, Department of Surgery, L'Institut Mutualiste Montsouris, Paris, France
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Alistair E W Johnson
- Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada
| | - Andrew Feifer
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada
| | - Alexandre R Zlotta
- Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Division of Urology, Department of Surgery, Mount Sinai Hospital, Sinai Health System, Toronto, ON, Canada.
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Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Hao S, Moody B, Gow B, Lehman LWH, Celi LA, Mark RG. Author Correction: MIMIC-IV, a freely accessible electronic health record dataset. Sci Data 2023; 10:219. [PMID: 37072428 PMCID: PMC10113185 DOI: 10.1038/s41597-023-02136-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023] Open
Affiliation(s)
- Alistair E W Johnson
- Massachusetts Institute of Technology, Cambridge, MA, USA.
- The Hospital for Sick Children, Toronto, ON, Canada.
| | | | - Lu Shen
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alvin Gayles
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Ayad Shammout
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Steven Horng
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Tom J Pollard
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sicheng Hao
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benjamin Moody
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian Gow
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Leo A Celi
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Roger G Mark
- Massachusetts Institute of Technology, Cambridge, MA, USA
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Bennett AM, Ulrich H, van Damme P, Wiedekopf J, Johnson AEW. MIMIC-IV on FHIR: converting a decade of in-patient data into an exchangeable, interoperable format. J Am Med Inform Assoc 2023; 30:718-725. [PMID: 36688534 PMCID: PMC10018258 DOI: 10.1093/jamia/ocad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/01/2022] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE Convert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR. MATERIALS AND METHODS FHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide. RESULTS The modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915 000 resources. The full dataset contained 315 000 patients covering approximately 5 840 000 resources. The final datasets in NDJSON format are accessible on PhysioNet. DISCUSSION Our work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work. CONCLUSION The newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.
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Affiliation(s)
- Alex M Bennett
- Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Hannes Ulrich
- Institute for Medical Informatics and Statistics, Kiel University and University Hospital Center Schleswig-Holstein, Campus Kiel, Germany
| | - Philip van Damme
- Department of Medical Informatics, Amsterdam UMC Location University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health, Digital Health & Methodology, Amsterdam, The Netherlands
| | - Joshua Wiedekopf
- IT Center for Clinical Research, University of Lübeck and University Hospital Center Schleswig-Holstein, Campus Lübeck, Germany
| | - Alistair E W Johnson
- Corresponding Author: Alistair E. W. Johnson, Child Health Evaluative Sciences, The Hospital for Sick Children, 686 Bay St., Toronto, ON M5G 0A4, Canada;
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Johnson AEW, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, Pollard TJ, Moody B, Gow B, Lehman LWH, Celi LA, Mark RG. Author Correction: MIMIC-IV, a freely accessible electronic health record dataset. Sci Data 2023; 10:31. [PMID: 36646711 PMCID: PMC9842744 DOI: 10.1038/s41597-023-01945-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Affiliation(s)
- Alistair E. W. Johnson
- grid.116068.80000 0001 2341 2786Massachusetts Institute of Technology, Cambridge, MA USA ,grid.42327.300000 0004 0473 9646The Hospital for Sick Children, Toronto, ON Canada
| | - Lucas Bulgarelli
- grid.116068.80000 0001 2341 2786Massachusetts Institute of Technology, Cambridge, MA USA
| | - Lu Shen
- grid.239395.70000 0000 9011 8547Beth Israel Deaconess Medical Center, Boston, MA USA
| | - Alvin Gayles
- grid.239395.70000 0000 9011 8547Beth Israel Deaconess Medical Center, Boston, MA USA
| | - Ayad Shammout
- grid.239395.70000 0000 9011 8547Beth Israel Deaconess Medical Center, Boston, MA USA
| | - Steven Horng
- grid.239395.70000 0000 9011 8547Beth Israel Deaconess Medical Center, Boston, MA USA
| | - Tom J. Pollard
- grid.116068.80000 0001 2341 2786Massachusetts Institute of Technology, Cambridge, MA USA
| | - Benjamin Moody
- grid.116068.80000 0001 2341 2786Massachusetts Institute of Technology, Cambridge, MA USA
| | - Brian Gow
- grid.116068.80000 0001 2341 2786Massachusetts Institute of Technology, Cambridge, MA USA
| | - Li-wei H. Lehman
- grid.116068.80000 0001 2341 2786Massachusetts Institute of Technology, Cambridge, MA USA
| | - Leo A. Celi
- grid.116068.80000 0001 2341 2786Massachusetts Institute of Technology, Cambridge, MA USA ,grid.239395.70000 0000 9011 8547Beth Israel Deaconess Medical Center, Boston, MA USA
| | - Roger G. Mark
- grid.116068.80000 0001 2341 2786Massachusetts Institute of Technology, Cambridge, MA USA
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Hofford MR, Yu SC, Johnson AEW, Lai AM, Payne PRO, Michelson AP. OpenSep: a generalizable open source pipeline for SOFA score calculation and Sepsis-3 classification. JAMIA Open 2022; 5:ooac105. [PMID: 36570030 PMCID: PMC9772813 DOI: 10.1093/jamiaopen/ooac105] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/25/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
EHR-based sepsis research often uses heterogeneous definitions of sepsis leading to poor generalizability and difficulty in comparing studies to each other. We have developed OpenSep, an open-source pipeline for sepsis phenotyping according to the Sepsis-3 definition, as well as determination of time of sepsis onset and SOFA scores. The Minimal Sepsis Data Model was developed alongside the pipeline to enable the execution of the pipeline to diverse sources of electronic health record data. The pipeline's accuracy was validated by applying it to the MIMIC-IV version 1.0 data and comparing sepsis onset and SOFA scores to those produced by the pipeline developed by the curators of MIMIC. We demonstrated high reliability between both the sepsis onsets and SOFA scores, however the use of the Minimal Sepsis Data model developed for this work allows our pipeline to be applied to more broadly to data sources beyond MIMIC.
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Affiliation(s)
- Mackenzie R Hofford
- Corresponding Author: Mackenzie R. Hofford, MD, Department of Medicine, Institute for Informatics, Washington University School of Medicine in St. Louis, 4444 Forest Park Avenue, Suite 6318, St. Louis, MO 63108, USA;
| | - Sean C Yu
- Department of Medicine, Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA,Department of Biomedical Engineering, School of Engineering, Washington University School in St. Louis, St. Louis, Missouri, USA
| | - Alistair E W Johnson
- Program in Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada
| | - Albert M Lai
- Department of Medicine, Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Philip R O Payne
- Department of Medicine, Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Andrew P Michelson
- Department of Medicine, Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA,Division of Pulmonary and Critical Care, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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Raffa JD, Johnson AEW, O'Brien Z, Pollard TJ, Mark RG, Celi LA, Pilcher D, Badawi O. The authors reply. Crit Care Med 2022; 50:e801-e802. [PMID: 36227051 DOI: 10.1097/ccm.0000000000005648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Jesse D Raffa
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Alistair E W Johnson
- Peter Gilgan Center for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Tom J Pollard
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Roger G Mark
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
- Beth Israel Deaconess Medical Center, Boston, MA
| | - Leo A Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
- Beth Israel Deaconess Medical Center, Boston, MA
| | - David Pilcher
- Department of Intensive Care and Hyperbaric Medicine, Alfred Hospital, Melbourne, VIC, Australia
- Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Alfred Hospital, Melbourne, VIC, Australia
- Centre for Outcome and Resource Evaluation, Australian and New Zealand Intensive Care Society, Melbourne, VIC, Australia
| | - Omar Badawi
- Medical Device Innovation Consortium, Arlington, VA
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Abstract
OBJECTIVES To develop and demonstrate the feasibility of a Global Open Source Severity of Illness Score (GOSSIS)-1 for critical care patients, which generalizes across healthcare systems and countries. DESIGN A merger of several critical care multicenter cohorts derived from registry and electronic health record data. Data were split into training (70%) and test (30%) sets, using each set exclusively for development and evaluation, respectively. Missing data were imputed when not available. SETTING/PATIENTS Two large multicenter datasets from Australia and New Zealand (Australian and New Zealand Intensive Care Society Adult Patient Database [ANZICS-APD]) and the United States (eICU Collaborative Research Database [eICU-CRD]) representing 249,229 and 131,051 patients, respectively. ANZICS-APD and eICU-CRD contributed data from 162 and 204 hospitals, respectively. The cohort included all ICU admissions discharged in 2014-2015, excluding patients less than 16 years old, admissions less than 6 hours, and those with a previous ICU stay. INTERVENTIONS Not applicable. MEASUREMENTS AND MAIN RESULTS GOSSIS-1 uses data collected during the ICU stay's first 24 hours, including extrema values for vital signs and laboratory results, admission diagnosis, the Glasgow Coma Scale, chronic comorbidities, and admission/demographic variables. The datasets showed significant variation in admission-related variables, case-mix, and average physiologic state. Despite this heterogeneity, test set discrimination of GOSSIS-1 was high (area under the receiver operator characteristic curve [AUROC], 0.918; 95% CI, 0.915-0.921) and calibration was excellent (standardized mortality ratio [SMR], 0.986; 95% CI, 0.966-1.005; Brier score, 0.050). Performance was held within ANZICS-APD (AUROC, 0.925; SMR, 0.982; Brier score, 0.047) and eICU-CRD (AUROC, 0.904; SMR, 0.992; Brier score, 0.055). Compared with GOSSIS-1, Acute Physiology and Chronic Health Evaluation (APACHE)-IIIj (ANZICS-APD) and APACHE-IVa (eICU-CRD), had worse discrimination with AUROCs of 0.904 and 0.869, and poorer calibration with SMRs of 0.594 and 0.770, and Brier scores of 0.059 and 0.063, respectively. CONCLUSIONS GOSSIS-1 is a modern, free, open-source inhospital mortality prediction algorithm for critical care patients, achieving excellent discrimination and calibration across three countries.
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Affiliation(s)
- Jesse D Raffa
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Alistair E W Johnson
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | | | - Tom J Pollard
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Roger G Mark
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
- Beth Israel Deaconess Medical Center, Boston, MA
| | - Leo A Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
- Beth Israel Deaconess Medical Center, Boston, MA
| | - David Pilcher
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
- Austin Health, Melbourne, VIC, Australia
- Beth Israel Deaconess Medical Center, Boston, MA
- Department of Intensive Care and Hyperbaric Medicine, Alfred Hospital, Melbourne, VIC, Australia
- Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Alfred Hospital, Melbourne, VIC, Australia
- Centre for Outcome and Resource Evaluation, Australian and New Zealand Intensive Care Society, Melbourne, VIC, Australia
- Connected Care Informatics, Philips Healthcare, Baltimore, MD
| | - Omar Badawi
- Connected Care Informatics, Philips Healthcare, Baltimore, MD
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Guo LL, Pfohl SR, Fries J, Johnson AEW, Posada J, Aftandilian C, Shah N, Sung L. Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Sci Rep 2022; 12:2726. [PMID: 35177653 PMCID: PMC8854561 DOI: 10.1038/s41598-022-06484-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 01/31/2022] [Indexed: 11/24/2022] Open
Abstract
Temporal dataset shift associated with changes in healthcare over time is a barrier to deploying machine learning-based clinical decision support systems. Algorithms that learn robust models by estimating invariant properties across time periods for domain generalization (DG) and unsupervised domain adaptation (UDA) might be suitable to proactively mitigate dataset shift. The objective was to characterize the impact of temporal dataset shift on clinical prediction models and benchmark DG and UDA algorithms on improving model robustness. In this cohort study, intensive care unit patients from the MIMIC-IV database were categorized by year groups (2008–2010, 2011–2013, 2014–2016 and 2017–2019).
Tasks were predicting mortality, long length of stay, sepsis and invasive ventilation. Feedforward neural networks were used as prediction models. The baseline experiment trained models using empirical risk minimization (ERM) on 2008–2010 (ERM[08–10]) and evaluated them on subsequent year groups. DG experiment trained models using algorithms that estimated invariant properties using 2008–2016 and evaluated them on 2017–2019. UDA experiment leveraged unlabelled samples from 2017 to 2019 for unsupervised distribution matching. DG and UDA models were compared to ERM[08–16] models trained using 2008–2016. Main performance measures were area-under-the-receiver-operating-characteristic curve (AUROC), area-under-the-precision-recall curve and absolute calibration error. Threshold-based metrics including false-positives and false-negatives were used to assess the clinical impact of temporal dataset shift and its mitigation strategies. In the baseline experiments, dataset shift was most evident for sepsis prediction (maximum AUROC drop, 0.090; 95% confidence interval (CI), 0.080–0.101). Considering a scenario of 100 consecutively admitted patients showed that ERM[08–10] applied to 2017–2019 was associated with one additional false-negative among 11 patients with sepsis, when compared to the model applied to 2008–2010. When compared with ERM[08–16], DG and UDA experiments failed to produce more robust models (range of AUROC difference, − 0.003 to 0.050). In conclusion, DG and UDA failed to produce more robust models compared to ERM in the setting of temporal dataset shift. Alternate approaches are required to preserve model performance over time in clinical medicine.
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Affiliation(s)
- Lin Lawrence Guo
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
| | - Stephen R Pfohl
- Biomedical Informatics Research, Stanford University, Palo Alto, USA
| | - Jason Fries
- Biomedical Informatics Research, Stanford University, Palo Alto, USA
| | - Alistair E W Johnson
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jose Posada
- Biomedical Informatics Research, Stanford University, Palo Alto, USA
| | | | - Nigam Shah
- Biomedical Informatics Research, Stanford University, Palo Alto, USA
| | - Lillian Sung
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada. .,Division of Haematology/Oncology, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G1X8, Canada.
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15
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Naik GS, Buchbinder EI, Cohen JV, Manos MP, Johnson AEW, Bowling P, Aizer AA, Schoenfeld JD, Lawrence DP, Haq R, Hodi FS, Sullivan RJ, Ott PA. Long-term Overall Survival and Predictors in Anti-PD-1-naive Melanoma Patients With Brain Metastases Treated With Immune Checkpoint Inhibitors in the Real-world Setting: A Multicohort Study. J Immunother 2021; 44:307-318. [PMID: 34406158 DOI: 10.1097/cji.0000000000000385] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/29/2021] [Indexed: 01/09/2023]
Abstract
Long-term survival outcomes among melanoma patients with brain metastases treated with immune checkpoint inhibitors are limited. In this retrospective study at 2 centers, metastatic melanoma patients with radiographic evidence of brain metastases who received anti-programmed death-1 (PD-1) monotherapy or nivolumab in combination with ipilimumab between 2014 and 2017 were included. Overall survival (OS) was assessed in diagnosis-specific graded prognostic assessment (ds-GPA) and melanoma-molecular graded prognostic assessment (molGPA) prognostic risk groups. Baseline clinical covariates were used to identify predictors of OS in univariate/multivariable Cox proportional-hazards models. A total of 84 patients (58 monotherapy, 26 combination) were included with a median duration of follow-up of 43.4 months (maximum: 5.1 y). The median OS [95% confidence interval (CI)] was 3.1 months (1.8, 7) for ds-GPA 0-1, 22.1 months [5.4, not reached (NR)] for ds-GPA 2 and NR (24.9, NR) for ds-GPA 3-4 in the monotherapy cohort [hazard ratio (HR) for ds-GPA 3-4 vs. 0-1: 0.13 (95% CI: 0.052, 0.32); 0.29 (95% CI: 0.12, 0.63) for ds-GPA 2 vs. 0-1]. The median OS was 1.1 months (95% CI: 0.3, NR) for ds-GPA 0-1, 11.8 months (95% CI: 2.9, 23.3) for ds-GPA 2 and 24.4 months (95% CI: 3.4, NR) for ds-GPA 3-4 in the combination cohort [HR for 3-4 vs. 0-1: 0.013 (95% CI: 0.0012, 0.14); HR for ds-GPA 2 vs. 0-1: 0.033 (0.0035, 0.31)]. Predictors associated with longer survival included ds-GPA or molGPA>1 (among prognostic indices), neutrophil-to-lymphocyte ratio (<4 vs. ≥4), while high lactate dehydrogenase, neurological symptoms, and leptomeningeal metastases were associated with shorter survival. Baseline ds-GPA/molGPA>1 and neutrophil-to-lymphocyte ratio <4 were strong predictors of long-term survival to anti-PD-1-based immune checkpoint inhibitors in melanoma brain metastases patients previously naive to anti-PD-1 therapy in a real-world clinical setting treated at independent centers.
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Affiliation(s)
- Girish S Naik
- Department of Medical Oncology, Dana-Farber Cancer Institute
- Harvard Medical School
| | - Elizabeth I Buchbinder
- Department of Medical Oncology, Dana-Farber Cancer Institute
- Harvard Medical School
- Brigham and Women's Hospital
| | - Justine V Cohen
- Harvard Medical School
- Center for Melanoma, Massachusetts General Hospital, Boston
| | - Michael P Manos
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Alistair E W Johnson
- Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Peter Bowling
- Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Ayal A Aizer
- Harvard Medical School
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center
| | - Jonathan D Schoenfeld
- Harvard Medical School
- Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center
| | - Donald P Lawrence
- Harvard Medical School
- Center for Melanoma, Massachusetts General Hospital, Boston
| | - Rizwan Haq
- Department of Medical Oncology, Dana-Farber Cancer Institute
- Harvard Medical School
- Brigham and Women's Hospital
| | - Frank Stephen Hodi
- Department of Medical Oncology, Dana-Farber Cancer Institute
- Harvard Medical School
- Brigham and Women's Hospital
| | - Ryan J Sullivan
- Harvard Medical School
- Center for Melanoma, Massachusetts General Hospital, Boston
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute
- Harvard Medical School
- Brigham and Women's Hospital
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Swart P, Deliberato RO, Johnson AEW, Pollard TJ, Bulgarelli L, Pelosi P, de Abreu MG, Schultz MJ, Neto AS. Impact of sex on use of low tidal volume ventilation in invasively ventilated ICU patients-A mediation analysis using two observational cohorts. PLoS One 2021; 16:e0253933. [PMID: 34260619 PMCID: PMC8279424 DOI: 10.1371/journal.pone.0253933] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/15/2021] [Indexed: 11/22/2022] Open
Abstract
Background Studies in patients receiving invasive ventilation show important differences in use of low tidal volume (VT) ventilation (LTVV) between females and males. The aims of this study were to describe temporal changes in VT and to determine what factors drive the sex difference in use of LTVV. Methods and findings This is a posthoc analysis of 2 large longitudinal projects in 59 ICUs in the United States, the ‘Medical information Mart for Intensive Care III’ (MIMIC III) and the ‘eICU Collaborative Research DataBase’. The proportion of patients under LTVV (median VT < 8 ml/kg PBW), was the primary outcome. Mediation analysis, a method to dissect total effect into direct and indirect effects, was used to understand which factors drive the sex difference. We included 3614 (44%) females and 4593 (56%) males. Median VT declined over the years, but with a persistent difference between females (from median 10.2 (9.1 to 11.4) to 8.2 (7.5 to 9.1) ml/kg PBW) vs. males (from median 9.2 [IQR 8.2 to 10.1] to 7.3 [IQR 6.6 to 8.0] ml/kg PBW) (P < .001). In females versus males, use of LTVV increased from 5 to 50% versus from 12 to 78% (difference, –27% [–29% to –25%]; P < .001). The sex difference was mainly driven by patients’ body height and actual body weight (adjusted average causal mediation effect, –30% [–33% to –27%]; P < .001, and 4 [3% to 4%]; P < .001). Conclusions While LTVV is increasingly used in females and males, females continue to receive LTVV less often than males. The sex difference is mainly driven by patients’ body height and actual body weight, and not necessarily by sex. Use of LTVV in females could improve by paying more attention to a correct calculation of VT, i.e., using the correct body height.
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Affiliation(s)
- Pien Swart
- Department of Intensive Care, Amsterdam UMC, Amsterdam, The Netherlands
- * E-mail:
| | - Rodrigo Octavio Deliberato
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, São Paulo, Brazil
- Big Data Analytics Group, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Alistair E. W. Johnson
- Laboratory for Computational Physiology, Institute for Medical Engineering & Science, MIT, Cambridge, MA, United States of America
| | - Tom J. Pollard
- Laboratory for Computational Physiology, Institute for Medical Engineering & Science, MIT, Cambridge, MA, United States of America
| | - Lucas Bulgarelli
- Laboratory for Computational Physiology, Institute for Medical Engineering & Science, MIT, Cambridge, MA, United States of America
| | - Paolo Pelosi
- IRCCS San Martino Policlinico Hospital, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Marcelo Gama de Abreu
- Pulmonary Engineering Group, Department of Anaesthesiology and Intensive Care Medicine, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Outcomes Research Consortium, Cleveland, OH, United States of America
| | - Marcus J. Schultz
- Department of Intensive Care, Amsterdam UMC, Amsterdam, The Netherlands
- Laboratory of Experimental Intensive Care and Anaesthesia (L·E·I·C·A), Amsterdam UMC, Amsterdam, The Netherlands
- Mahidol Oxford Tropical Medicine Research Unit (MORU), Mahidol University, Bangkok, Thailand
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Ary Serpa Neto
- Department of Intensive Care, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, São Paulo, Brazil
- Pulmonary Division, Cardio–Pulmonary Department, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
- Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, Australia
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17
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Kuo PC, Tsai CC, López DM, Karargyris A, Pollard TJ, Johnson AEW, Celi LA. Recalibration of deep learning models for abnormality detection in smartphone-captured chest radiograph. NPJ Digit Med 2021; 4:25. [PMID: 33589700 PMCID: PMC7884693 DOI: 10.1038/s41746-021-00393-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 01/11/2021] [Indexed: 12/22/2022] Open
Abstract
Image-based teleconsultation using smartphones has become increasingly popular. In parallel, deep learning algorithms have been developed to detect radiological findings in chest X-rays (CXRs). However, the feasibility of using smartphones to automate this process has yet to be evaluated. This study developed a recalibration method to build deep learning models to detect radiological findings on CXR photographs. Two publicly available databases (MIMIC-CXR and CheXpert) were used to build the models, and four derivative datasets containing 6453 CXR photographs were collected to evaluate model performance. After recalibration, the model achieved areas under the receiver operating characteristic curve of 0.80 (95% confidence interval: 0.78-0.82), 0.88 (0.86-0.90), 0.81 (0.79-0.84), 0.79 (0.77-0.81), 0.84 (0.80-0.88), and 0.90 (0.88-0.92), respectively, for detecting cardiomegaly, edema, consolidation, atelectasis, pneumothorax, and pleural effusion. The recalibration strategy, respectively, recovered 84.9%, 83.5%, 53.2%, 57.8%, 69.9%, and 83.0% of performance losses of the uncalibrated model. We conclude that the recalibration method can transfer models from digital CXRs to CXR photographs, which is expected to help physicians' clinical works.
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Affiliation(s)
- Po-Chih Kuo
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Cheng Che Tsai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Diego M López
- Telematics Department, University of Cauca, Popayán, Cauca, Colombia
| | | | - Tom J Pollard
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alistair E W Johnson
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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18
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Bulgarelli L, Deliberato RO, Johnson AEW. Prediction on critically ill patients: The role of "big data". J Crit Care 2020; 60:64-68. [PMID: 32763775 DOI: 10.1016/j.jcrc.2020.07.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 07/11/2020] [Accepted: 07/15/2020] [Indexed: 12/12/2022]
Abstract
Accurate outcome prediction in Intensive Care Units (ICUs) would allow for better treatment planning, risk adjustment of study populations, and overall improvements in patient care. In the past, prognostic models have focused on mortality using simple ordinal severity of illness scores which could be tabulated manually by a human. With the improvements in computing power and proliferation of electronic medical records, entirely new approaches have become possible. Here we review the latest advances in outcome prediction, paying close attention to methods which are widely applicable and provide a high-level overview of the challenges the field currently faces.
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Affiliation(s)
- Lucas Bulgarelli
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, USA; Big Data Analytics Department, Hospital Israelita Albert Einstein, São Paulo, Brazil.
| | - Rodrigo Octávio Deliberato
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, USA; Department of Clinical Data Science Research, Endpoint Health, Inc., USA
| | - Alistair E W Johnson
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, USA
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19
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Cai Z, Li J, Johnson AEW, Zhang X, Shen Q, Zhang J, Liu C. Rule-based rough-refined two-step-procedure for real-time premature beat detection in single-lead ECG. Physiol Meas 2020; 41:054004. [DOI: 10.1088/1361-6579/ab87b4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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20
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Johnson AEW, Bulgarelli L, Pollard TJ. Deidentification of free-text medical records using pre-trained bidirectional transformers. Proc ACM Conf Health Inference Learn (2020) 2020; 2020:214-221. [PMID: 34350426 PMCID: PMC8330601 DOI: 10.1145/3368555.3384455] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The ability of caregivers and investigators to share patient data is fundamental to many areas of clinical practice and biomedical research. Prior to sharing, it is often necessary to remove identifiers such as names, contact details, and dates in order to protect patient privacy. Deidentification, the process of removing identifiers, is challenging, however. High-quality annotated data for developing models is scarce; many target identifiers are highly heterogenous (for example, there are uncountable variations of patient names); and in practice anything less than perfect sensitivity may be considered a failure. As a result, patient data is often withheld when sharing would be beneficial, and identifiable patient data is often divulged when a deidentified version would suffice. In recent years, advances in machine learning methods have led to rapid performance improvements in natural language processing tasks, in particular with the advent of large-scale pretrained language models. In this paper we develop and evaluate an approach for deidentification of clinical notes based on a bidirectional transformer model. We propose human interpretable evaluation measures and demonstrate state of the art performance against modern baseline models. Finally, we highlight current challenges in deidentification, including the absence of clear annotation guidelines, lack of portability of models, and paucity of training data. Code to develop our model is open source, allowing for broad reuse.
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Affiliation(s)
| | | | - Tom J Pollard
- Massachusetts Institute of Technology, Cambridge, MA, USA
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21
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Johnson AEW, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng CY, Mark RG, Horng S. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data 2019; 6:317. [PMID: 31831740 PMCID: PMC6908718 DOI: 10.1038/s41597-019-0322-0] [Citation(s) in RCA: 258] [Impact Index Per Article: 51.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 11/11/2019] [Indexed: 12/18/2022] Open
Abstract
Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's chest, but requires specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. Here we describe MIMIC-CXR, a large dataset of 227,835 imaging studies for 65,379 patients presenting to the Beth Israel Deaconess Medical Center Emergency Department between 2011-2016. Each imaging study can contain one or more images, usually a frontal view and a lateral view. A total of 377,110 images are available in the dataset. Studies are made available with a semi-structured free-text radiology report that describes the radiological findings of the images, written by a practicing radiologist contemporaneously during routine clinical care. All images and reports have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in computer vision, natural language processing, and clinical data mining.
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Affiliation(s)
- Alistair E W Johnson
- Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Tom J Pollard
- Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Seth J Berkowitz
- Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Nathaniel R Greenbaum
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Chih-Ying Deng
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Roger G Mark
- Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven Horng
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
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22
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Naik GS, Waikar SS, Johnson AEW, Buchbinder EI, Haq R, Hodi FS, Schoenfeld JD, Ott PA. Complex inter-relationship of body mass index, gender and serum creatinine on survival: exploring the obesity paradox in melanoma patients treated with checkpoint inhibition. J Immunother Cancer 2019; 7:89. [PMID: 30922394 PMCID: PMC6440018 DOI: 10.1186/s40425-019-0512-5] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [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: 09/04/2018] [Accepted: 01/16/2019] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND A male gender driven obesity paradox (improved survival for overweight/obese patients compared to normal weight) was recently shown in melanoma in the context of checkpoint inhibition (anti-PD-1/anti-CTLA4 monotherapy) in a pooled meta-analysis. We characterized the relationship of Body Mass Index (BMI) with survival and explored gender-based interactions with surrogates of body composition/malnutrition in the context of PD-1 blockade as monotherapy or in combination with ipilimumab in a real-world setting. METHODS Advanced melanoma patients who received at least one dose of pembrolizumab, nivolumab, or nivolumab plus ipilimumab (combination) from June 2014 to September 2016 were included in this retrospective cohort study (N = 139). Overall Survival (OS) and Progression Free Survival (PFS) were the main outcomes. Analysis was performed using Random Survival Forests (RSF)/ multivariable Cox Proportional-Hazards models. RESULTS Overweight/Class-I (25- < 35 kg/m2) obese patients had a significantly lower risk of mortality (adjusted-HR:0.26; 95%CI:0.1-0.71; p-value = 0.008) and progressive disease (adjusted-HR:0.43; 95%CI:0.19-0.95; p-value:0.038) compared to normal-weight (18.5- < 25 kg/m2). Class II/III obesity (compared to normal-weight) had an adjusted HR of 0.42 (95%CI: 0.1-1.77; p-value: 0.238) for OS and 1 (95%CI:0.34-2.94; p-value:0.991) for PFS. Exploration of interactions for OS showed that the association was predominantly driven by males (adjusted-HRmales:0.11; 95%CI:0.03-0.4; adjusted-HRfemales: 0.56; 95%CI:0.16-1.89; p-valueinteraction:0.044); the association was not seen in patients with serum creatinine< 0.9 mg/dL (adjusted-HR:0.43; 95%CI:0.15-1.24; p-valueinteraction:0.020), who were predominantly females. These observations were made in both the anti-PD-1 monotherapy (n = 79) and combination therapy (anti-PD-1/CTLA-4, n = 60) cohorts. CONCLUSIONS The findings support the existence of an "obesity paradox" restricted to overweight/Class-I obesity in the real-world setting; the association was driven predominantly by males who largely had higher serum creatinine levels, a surrogate for skeletal muscle mass in the setting of metastatic disease. These observations suggest that sarcopenia (low skeletal muscle mass) or direct measures of body mass composition may be more suitable predictors of survival in melanoma patients treated with PD-1 blockade (monotherapy/combination).
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Affiliation(s)
- Girish S Naik
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.,Harvard Medical School, Boston, MA, USA
| | - Sushrut S Waikar
- Harvard Medical School, Boston, MA, USA.,Division of Renal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alistair E W Johnson
- Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elizabeth I Buchbinder
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.,Harvard Medical School, Boston, MA, USA
| | - Rizwan Haq
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.,Harvard Medical School, Boston, MA, USA
| | - F Stephen Hodi
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.,Harvard Medical School, Boston, MA, USA
| | - Jonathan D Schoenfeld
- Harvard Medical School, Boston, MA, USA.,Department of Radiation Oncology, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, USA
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA. .,Harvard Medical School, Boston, MA, USA. .,Melanoma Center & Center for Immuno-Oncology, Boston, MA, USA.
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23
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Bose S, Johnson AEW, Moskowitz A, Celi LA, Raffa JD. Authors' Response to the Intensive Care Unit Discharge Delay and In-Hospital Mortality. J Intensive Care Med 2018:885066618816686. [PMID: 30526218 DOI: 10.1177/0885066618816686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Somnath Bose
- 1 Department of Anesthesia Critical Care and Pain Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alistair E W Johnson
- 2 Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ari Moskowitz
- 3 Division of Pulmonary, Critical Care and Sleep Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Leo Anthony Celi
- 2 Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- 3 Division of Pulmonary, Critical Care and Sleep Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jesse D Raffa
- 2 Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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24
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Serpa Neto A, Deliberato RO, Johnson AEW, Bos LD, Amorim P, Pereira SM, Cazati DC, Cordioli RL, Correa TD, Pollard TJ, Schettino GPP, Timenetsky KT, Celi LA, Pelosi P, Gama de Abreu M, Schultz MJ. Mechanical power of ventilation is associated with mortality in critically ill patients: an analysis of patients in two observational cohorts. Intensive Care Med 2018; 44:1914-1922. [PMID: 30291378 DOI: 10.1007/s00134-018-5375-6] [Citation(s) in RCA: 242] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 09/10/2018] [Indexed: 12/21/2022]
Abstract
PURPOSE Mechanical power (MP) may unify variables known to be related to development of ventilator-induced lung injury. The aim of this study is to examine the association between MP and mortality in critically ill patients receiving invasive ventilation for at least 48 h. METHODS This is an analysis of data stored in the databases of the MIMIC-III and eICU. Critically ill patients receiving invasive ventilation for at least 48 h were included. The exposure of interest was MP. The primary outcome was in-hospital mortality. RESULTS Data from 8207 patients were analyzed. Median MP during the second 24 h was 21.4 (16.2-28.1) J/min in MIMIC-III and 16.0 (11.7-22.1) J/min in eICU. MP was independently associated with in-hospital mortality [odds ratio per 5 J/min increase (OR) 1.06 (95% confidence interval (CI) 1.01-1.11); p = 0.021 in MIMIC-III, and 1.10 (1.02-1.18); p = 0.010 in eICU]. MP was also associated with ICU mortality, 30-day mortality, and with ventilator-free days, ICU and hospital length of stay. Even at low tidal volume, high MP was associated with in-hospital mortality [OR 1.70 (1.32-2.18); p < 0.001] and other secondary outcomes. Finally, there is a consistent increase in the risk of death with MP higher than 17.0 J/min. CONCLUSION High MP of ventilation is independently associated with higher in-hospital mortality and several other outcomes in ICU patients receiving invasive ventilation for at least 48 h.
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Affiliation(s)
- Ary Serpa Neto
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (LEICA), Academic Medical Center, Amsterdam, The Netherlands.
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, Albert Einstein Avenue, 700, São Paulo, Brazil.
- Laboratory for Critical Care Research, Hospital Israelita Albert Einstein, São Paulo, Brazil.
| | - Rodrigo Octavio Deliberato
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, Albert Einstein Avenue, 700, São Paulo, Brazil
- Laboratory for Critical Care Research, Hospital Israelita Albert Einstein, São Paulo, Brazil
- Big Data Analytics Group, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Alistair E W Johnson
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
| | - Lieuwe D Bos
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (LEICA), Academic Medical Center, Amsterdam, The Netherlands
| | - Pedro Amorim
- Department of Innovation, Hospital Israelita Albert Einstein, São Paulo, Brazil
| | | | - Denise Carnieli Cazati
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, Albert Einstein Avenue, 700, São Paulo, Brazil
| | - Ricardo L Cordioli
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, Albert Einstein Avenue, 700, São Paulo, Brazil
| | - Thiago Domingos Correa
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, Albert Einstein Avenue, 700, São Paulo, Brazil
| | - Tom J Pollard
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
| | - Guilherme P P Schettino
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, Albert Einstein Avenue, 700, São Paulo, Brazil
| | - Karina T Timenetsky
- Department of Critical Care Medicine, Hospital Israelita Albert Einstein, Albert Einstein Avenue, 700, São Paulo, Brazil
| | - Leo A Celi
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Paolo Pelosi
- Department of Surgical Sciences and Integrated Diagnostics, San Martino Policlinico Hospital, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) for Oncology, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Marcelo Gama de Abreu
- Pulmonary Engineering Group, Department of Anesthesiology and Intensive Care Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Marcus J Schultz
- Department of Intensive Care and Laboratory of Experimental Intensive Care and Anesthesiology (LEICA), Academic Medical Center, Amsterdam, The Netherlands
- Mahidol Oxford Tropical Medicine Research Unit (MORU), Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
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25
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Bose S, Johnson AEW, Moskowitz A, Celi LA, Raffa JD. Impact of Intensive Care Unit Discharge Delays on Patient Outcomes: A Retrospective Cohort Study. J Intensive Care Med 2018; 34:924-929. [PMID: 30270722 DOI: 10.1177/0885066618800276] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Patients often overstay in intensive care units (ICU) after they are deemed discharge ready. The objective of this study was to examine the impact of such discharge delays (DD) on subsequent in-hospital morbidity and mortality. DESIGN Retrospective cohort study. SETTING Single tertiary academic medical center. PATIENTS Adult patients admitted to the medical ICU between 2005 and 2011. INTERVENTIONS For all patients, DD (ie, time between request for a ward bed and time of ICU discharge) was calculated. Discharge delays was dichotomized as long (≥24 hours) or short (<24 hours). Multivariable linear and logistic regressions were used to assess the association between dichotomized DD and post-ICU clinical outcomes. RESULTS Overall, 9673 discharges were included of which 10.4% patients had long DDs. In the fully adjusted model, a long delay was not associated with increased odds of death (adjusted odds ratio [aOR]: 0.99, 95% confidence interval [CI]: 0.74-1.31, P = .95) but was associated with a shorter log plus one of post-ICU discharge length of stay (LOS; regression coefficient: -0.13, 95% CI: -0.17 to -0.08, P < .001). Longer DD was not associated with more hospital-free days (HFD: a composite of post-ICU LOS and in-hospital mortality). Shorter DDs were associated with shorter LOS when LOS was measured from the time of ward bed request as opposed to the time of ICU discharge. CONCLUSIONS In this study, long DD was associated with a slight decrease in post-ICU LOS but longer LOS when measured from the point of ward bed request, suggesting a potential role for more aggressive discharge planning in the ICU for patients with long DDs. There was no association between long DD and subsequent mortality or HFD.
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Affiliation(s)
- Somnath Bose
- Department of Anesthesia Critical Care and Pain Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alistair E W Johnson
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ari Moskowitz
- Division of Pulmonary, Critical Care and Sleep Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Division of Pulmonary, Critical Care and Sleep Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jesse D Raffa
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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26
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Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 2018; 5:180178. [PMID: 30204154 PMCID: PMC6132188 DOI: 10.1038/sdata.2018.178] [Citation(s) in RCA: 510] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 06/21/2018] [Indexed: 12/14/2022] Open
Abstract
Critical care patients are monitored closely through the course of their illness. As a result of this monitoring, large amounts of data are routinely collected for these patients. Philips Healthcare has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. The database is deidentified, and includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, treatment information, and more. Data are publicly available after registration, including completion of a training course in research with human subjects and signing of a data use agreement mandating responsible handling of the data and adhering to the principle of collaborative research. The freely available nature of the data will support a number of applications including the development of machine learning algorithms, decision support tools, and clinical research.
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Affiliation(s)
- Tom J. Pollard
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Alistair E. W. Johnson
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jesse D. Raffa
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Leo A. Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Roger G. Mark
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Omar Badawi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of eICU Research and Development, Philips Healthcare, Baltimore, MD 21202, USA
- Department of Pharmacy Practice and Science, University of Maryland, School of Pharmacy, Baltimore, MD 21201, USA
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27
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Abstract
OBJECTIVE The fusion of multiple noisy labels for biomedical data (such as ECG annotations, which may be obtained from human experts or from automated systems) into a single robust annotation has many applications in physiologic monitoring. Directly modelling the difficulty of the task has the potential to improve the fusion of such labels. This paper proposes a means for the incorporation of task difficulty, as quantified by 'signal quality', into the fusion process. APPROACH We propose a Bayesian fusion model to infer a consensus through aggregating labels, where the labels are provided by multiple imperfect automated algorithms (or 'annotators'). Our model incorporates the signal quality of the underlying recording when fusing labels. We compare our proposed model with previously published approaches. Two publicly available datasets were used to demonstrate the feasibility of our proposed model: one focused on QT interval estimation in the ECG and the other focused on respiratory rate (RR) estimation from the photoplethysmogram (PPG). We inferred the hyperparameters of our model using maximum- a posteriori inference and Gibbs sampling. MAIN RESULTS For the QT dataset, our model significantly outperformed the previously published models (root-mean-square error of [Formula: see text] ms for our model versus [Formula: see text] ms from the best existing model) when fusing labels from only three annotators. For the RR dataset, no improvement was observed compared to the same model without signal quality modelling, where our model outperformed existing models (mean-absolute error of [Formula: see text] bpm for our model versus [Formula: see text] bpm from the best existing model). We conclude that our approach demonstrates the feasibility of using a signal quality metric as a confidence measure to improve label fusion. SIGNIFICANCE Our Bayesian learning model provides an extension over existing work to incorporate signal quality as a confidence measure to improve the reliability of fusing labels from biomedical datasets.
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Affiliation(s)
- Tingting Zhu
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
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28
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Pollard TJ, Johnson AEW, Raffa JD, Mark RG. tableone: An open source Python package for producing summary statistics for research papers. JAMIA Open 2018; 1:26-31. [PMID: 31984317 PMCID: PMC6951995 DOI: 10.1093/jamiaopen/ooy012] [Citation(s) in RCA: 93] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 03/02/2018] [Accepted: 04/20/2018] [Indexed: 11/27/2022] Open
Abstract
Objectives In quantitative research, understanding basic parameters of the study population is key for interpretation of the results. As a result, it is typical for the first table (“Table 1”) of a research paper to include summary statistics for the study data. Our objectives are 2-fold. First, we seek to provide a simple, reproducible method for providing summary statistics for research papers in the Python programming language. Second, we seek to use the package to improve the quality of summary statistics reported in research papers. Materials and Methods The tableone package is developed following good practice guidelines for scientific computing and all code is made available under a permissive MIT License. A testing framework runs on a continuous integration server, helping to maintain code stability. Issues are tracked openly and public contributions are encouraged. Results The tableone software package automatically compiles summary statistics into publishable formats such as CSV, HTML, and LaTeX. An executable Jupyter Notebook demonstrates application of the package to a subset of data from the MIMIC-III database. Tests such as Tukey’s rule for outlier detection and Hartigan’s Dip Test for modality are computed to highlight potential issues in summarizing the data. Discussion and Conclusion We present open source software for researchers to facilitate carrying out reproducible studies in Python, an increasingly popular language in scientific research. The toolkit is intended to mature over time with community feedback and input. Development of a common tool for summarizing data may help to promote good practice when used as a supplement to existing guidelines and recommendations. We encourage use of tableone alongside other methods of descriptive statistics and, in particular, visualization to ensure appropriate data handling. We also suggest seeking guidance from a statistician when using tableone for a research study, especially prior to submitting the study for publication.
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Affiliation(s)
- Tom J Pollard
- Massachusetts Institute of Technology (MIT), MIT Laboratory for Computational Physiology, Cambridge, Massachusetts, USA
| | - Alistair E W Johnson
- Massachusetts Institute of Technology (MIT), MIT Laboratory for Computational Physiology, Cambridge, Massachusetts, USA
| | - Jesse D Raffa
- Massachusetts Institute of Technology (MIT), MIT Laboratory for Computational Physiology, Cambridge, Massachusetts, USA
| | - Roger G Mark
- Massachusetts Institute of Technology (MIT), MIT Laboratory for Computational Physiology, Cambridge, Massachusetts, USA
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Johnson AEW, Mark RG. Real-time mortality prediction in the Intensive Care Unit. AMIA Annu Symp Proc 2018; 2017:994-1003. [PMID: 29854167 PMCID: PMC5977709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Real-time prediction of mortality for intensive care unit patients has the potential to provide physicians with a simple and easily interpretable synthesis of patient acuity. Here we extract data from a random time during each patient's ICU stay. We believe this sampling scheme allows for the application of the model(s) across a future patient's entire ICU stay. The AUROC of a Gradient Boosting model was high (AUROC=0.920), even though no information about diagnosis or comorbid burden was utilized. We also compare models using data from the first 24 hours of a patient's stay against published severity of illness scores, and find the Gradient Boosting model greatly outperformed the frequently used Simplified Acute Physiology Score II (AUROC = 0.927 vs. 0.809). We nuance this performance with comparison to the literature, provide our interpretation, and discuss potential avenues for improvement.
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Affiliation(s)
| | - Roger G Mark
- Massachussetts Institute of Technology, Cambridge, MA, USA
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Sandfort V, Johnson AEW, Kunz LM, Vargas JD, Rosing DR. Prolonged Elevated Heart Rate and 90-Day Survival in Acutely Ill Patients: Data From the MIMIC-III Database. J Intensive Care Med 2018; 34:622-629. [PMID: 29402151 DOI: 10.1177/0885066618756828] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [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: 12/24/2022]
Abstract
PURPOSE We sought to evaluate the association of prolonged elevated heart rate (peHR) with survival in acutely ill patients. METHODS We used a large observational intensive care unit (ICU) database (Multiparameter Intelligent Monitoring in Intensive Care III [MIMIC-III]), where frequent heart rate measurements were available. The peHR was defined as a heart rate >100 beats/min in 11 of 12 consecutive hours. The outcome was survival status at 90 days. We collected heart rates, disease severity (simplified acute physiology scores [SAPS II]), comorbidities (Charlson scores), and International Classification of Diseases (ICD) diagnosis information in 31 513 patients from the MIMIC-III ICU database. Propensity score (PS) methods followed by inverse probability weighting based on the PS was used to balance the 2 groups (the presence/absence of peHR). Multivariable weighted logistic regression was used to assess for association of peHR with the outcome survival at 90 days adjusting for additional covariates. RESULTS The mean age was 64 years, and the most frequent main disease category was circulatory disease (41%). The mean SAPS II score was 35, and the mean Charlson comorbidity score was 2.3. Overall survival of the cohort at 90 days was 82%. Adjusted logistic regression showed a significantly increased risk of death within 90 days in patients with an episode of peHR (P < .001; odds ratio for death 1.79; confidence interval, 1.69-1.88). This finding was independent of median heart rate. CONCLUSION We found a significant association of peHR with decreased survival in a large and heterogenous cohort of ICU patients.
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Affiliation(s)
- Veit Sandfort
- 1 Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.,2 Department of Internal Medicine, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Alistair E W Johnson
- 3 Institute of Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lauren M Kunz
- 4 Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - Jose D Vargas
- 1 Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.,5 National Human Genome Research Institute, Bethesda, MD, USA.,6 Division of Cardiology, MedStar Georgetown University Hospital, Washington DC, USA
| | - Douglas R Rosing
- 7 Cardiovascular Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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Johnson AEW, Stone DJ, Celi LA, Pollard TJ. The MIMIC Code Repository: enabling reproducibility in critical care research. J Am Med Inform Assoc 2018; 25:32-39. [PMID: 29036464 PMCID: PMC6381763 DOI: 10.1093/jamia/ocx084] [Citation(s) in RCA: 180] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 07/11/2017] [Accepted: 07/27/2017] [Indexed: 12/13/2022] Open
Abstract
Objective Lack of reproducibility in medical studies is a barrier to the generation of a robust knowledge base to support clinical decision-making. In this paper we outline the Medical Information Mart for Intensive Care (MIMIC) Code Repository, a centralized code base for generating reproducible studies on an openly available critical care dataset. Materials and Methods Code is provided to load the data into a relational structure, create extractions of the data, and reproduce entire analysis plans including research studies. Results Concepts extracted include severity of illness scores, comorbid status, administrative definitions of sepsis, physiologic criteria for sepsis, organ failure scores, treatment administration, and more. Executable documents are used for tutorials and reproduce published studies end-to-end, providing a template for future researchers to replicate. The repository's issue tracker enables community discussion about the data and concepts, allowing users to collaboratively improve the resource. Discussion The centralized repository provides a platform for users of the data to interact directly with the data generators, facilitating greater understanding of the data. It also provides a location for the community to collaborate on necessary concepts for research progress and share them with a larger audience. Consistent application of the same code for underlying concepts is a key step in ensuring that research studies on the MIMIC database are comparable and reproducible. Conclusion By providing open source code alongside the freely accessible MIMIC-III database, we enable end-to-end reproducible analysis of electronic health records.
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Affiliation(s)
| | - David J Stone
- University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Leo A Celi
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Tom J Pollard
- Massachusetts Institute of Technology, Cambridge, MA, USA
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Charlton PH, Birrenkott DA, Bonnici T, Pimentel MAF, Johnson AEW, Alastruey J, Tarassenko L, Watkinson PJ, Beale R, Clifton DA. Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review. IEEE Rev Biomed Eng 2017; 11:2-20. [PMID: 29990026 PMCID: PMC7612521 DOI: 10.1109/rbme.2017.2763681] [Citation(s) in RCA: 124] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.
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Affiliation(s)
- Peter H. Charlton
- Department of Biomedical Engineering, King’s College London, London SE1 7EH, U.K., and also with the Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Drew A. Birrenkott
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Timothy Bonnici
- Nuffield Department of Medicine, University of Oxford, Oxford OX3 9DU, U.K., and also with the Department of Asthma, Allergy, and Lung Biology, King’s College London, London SE1 7EH, U.K
| | | | - Alistair E. W. Johnson
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Jordi Alastruey
- Department of Biomedical Engineering, King’s College London, London SE1 7EH, U.K
| | - Lionel Tarassenko
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Peter J. Watkinson
- Kadoorie Centre for Critical Care Research and Education, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, U.K
| | - Richard Beale
- Department of Asthma, Allergy and Lung Biology, King’s College London, London SE1 7EH, U.K
| | - David A. Clifton
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
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Zalewski A, Long W, Johnson AEW, Mark RG, Lehman LWH. Estimating Patient's Health State Using Latent Structure Inferred from Clinical Time Series and Text. IEEE EMBS Int Conf Biomed Health Inform 2017. [PMID: 28630952 DOI: 10.1109/bhi.2017.7897302] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Modern intensive care units (ICUs) collect large volumes of data in monitoring critically ill patients. Clinicians in the ICUs face the challenge of interpreting large volumes of high-dimensional data to diagnose and treat patients. In this work, we explore the use of Hierarchical Dirichlet Processes (HDP) as a Bayesian nonparametric framework to infer patients' states of health by combining multiple sources of data. In particular, we employ HDP to combine clinical time series and text from the nursing progress notes in a probabilistic topic modeling framework for patient risk stratification. Given a patient cohort, we use HDP to infer latent "topics" shared across multimodal patient data from the entire cohort. Each topic is modeled as a multinomial distribution over a vocabulary of codewords, defined over heterogeneous data sources. We evaluate the clinical utility of the learned topic structure using the first 24-hour ICU data from over 17,000 adult patients in the MIMIC-II database to estimate patients' risks of in-hospital mortality. Our results demonstrate that our approach provides a viable framework for combining different data modalities to model patient's states of health, and can potentially be used to generate alerts to identify patients at high risk of hospital mortality.
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Affiliation(s)
- Aaron Zalewski
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - William Long
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Alistair E W Johnson
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Roger G Mark
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
| | - Li-Wei H Lehman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA
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Pimentel MAF, Johnson AEW, Charlton PH, Birrenkott D, Watkinson PJ, Tarassenko L, Clifton DA. Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters. IEEE Trans Biomed Eng 2016; 64:1914-1923. [PMID: 27875128 PMCID: PMC6051482 DOI: 10.1109/tbme.2016.2613124] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG)
typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on
independent “validation” datasets. The lack of robustness of existing methods directly results in a lack
of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the
robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use
of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three
respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on
two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in
different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of
existing methods in the literature. Results: The proposed method achieved comparable accuracy to
existing methods in the literature, with mean absolute errors (median, 25\documentclass[12pt]{minimal}
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}{}$\text {th}$\end{document} percentiles for a window size of 32 seconds) of 1.5 (0.3–3.3) and 4.0 (1.8–5.5) breaths
per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over
90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the
proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly
available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical
practice.
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Affiliation(s)
- Marco A F Pimentel
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, U.K
| | - Alistair E W Johnson
- Institute for Medical Engineering & ScienceMassachusetts Institute of Technology
| | | | - Drew Birrenkott
- Department of Engineering ScienceInstitute of Biomedical EngineeringUniversity of Oxford
| | | | - Lionel Tarassenko
- Department of Engineering ScienceInstitute of Biomedical EngineeringUniversity of Oxford
| | - David A Clifton
- Department of Engineering ScienceInstitute of Biomedical EngineeringUniversity of Oxford
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Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. MIMIC-III, a freely accessible critical care database. Sci Data 2016; 3:160035. [PMID: 27219127 PMCID: PMC4878278 DOI: 10.1038/sdata.2016.35] [Citation(s) in RCA: 2455] [Impact Index Per Article: 306.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 04/25/2016] [Indexed: 12/11/2022] Open
Abstract
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.
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Affiliation(s)
- Alistair E W Johnson
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Tom J Pollard
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Lu Shen
- Information Systems, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, USA
| | - Li-Wei H Lehman
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Mengling Feng
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore 138632, Singapore
| | - Mohammad Ghassemi
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Benjamin Moody
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Peter Szolovits
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Information Systems, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, USA
| | - Roger G Mark
- Laboratory for Computational Physiology, MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.,Information Systems, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02215, USA
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Johnson AEW, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. Machine Learning and Decision Support in Critical Care. Proc IEEE Inst Electr Electron Eng 2016; 104:444-466. [PMID: 27765959 PMCID: PMC5066876 DOI: 10.1109/jproc.2015.2501978] [Citation(s) in RCA: 156] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply re-using the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding "secondary use of medical records" and "Big Data" analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of "precision medicine." This review article covers many of the issues involved in the collection and preprocessing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; on-line patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data.
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Affiliation(s)
- Alistair E. W. Johnson
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Boston, USA
| | - Mohammad M. Ghassemi
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Boston, USA
| | - Shamim Nemati
- Department of Biomedical Informatics, Emory University, Atlanta, USA
| | - Katherine E. Niehaus
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - David A. Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, USA; Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA
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Johnson AEW, Burgess J, Pimentel MAF, Clifton DA, Young JD, Watkinson PJ, Tarassenko L. Physiological trajectory of patients pre and post ICU discharge. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2014:3160-3. [PMID: 25570661 DOI: 10.1109/embc.2014.6944293] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The intensive care unit (ICU) admits the most severely ill patients, and the goal of the unit can be interpreted as stabilizing patient physiology. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff. Early detection of physiological deterioration has been highlighted as a key step to reduce ICU readmission and improve patient outcomes. Vital signs were collected for a dataset of 98 patients admitted to an ICU and who survived to hospital discharge after their stay on a step-down ward. A model of physiological normality was developed using data from the day of hospital discharge, and patients were retrospectively evaluated throughout their stay using this model. We show that the physiology of patients being cared for in the ICU improves very rapidly in the three days prior to discharge, and furthermore, that this recovery continues during their stay on the ward, albeit at a slower rate.
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Abstract
The electrocardiogram (ECG) is a well studied signal from which many clinically relevant parameters can be derived, such as heart rate. A key component in the estimation of these parameters is the accurate detection of the R peak in the QRS complex. While corruption of the ECG by movement artefact or sensor failure can result in poor delineation of the R peak, use of synchronously measured signals could allow for resolution of the R peak even scenarios with poor quality ECG recordings. Robust estimation of R peak locations from multimodal signals facilitates real time monitoring and is likely to reduce false alarms due to inaccurate derived parameters.We propose a method which fuses R peaks detected on the ECG using an energy detector with those detected on the arterial blood pressure (ABP) waveform using the length transform. A signal quality index (SQI) for the two signals is then derived. The ECG SQI is based upon the agreement between two distinct peak detectors. The ABP SQI estimates the blood pressure at various phases in the cardiac cycle and only accepts the signal as good quality if the values are physiologically plausible. Detections from these two signals were merged by selecting the R peak detections from the signal with a higher SQI. The approach presented in this paper was evaluated on datasets provided for the Physionet/Computing in Cardiology Challenge 2014. The algorithm achieved a sensitivity of 95.1% and positive predictive value of 89.3% on an external evaluation set, and achieved a score of 91.5%.The method here demonstrated excellent performance across a variety of signal morphologies collected during clinical practice. Fusion of R peaks from other signals has the potential to provide informed estimates of the R peak location in situations where the ECG is noisy or completely absent. Source code for the algorithm is made available freely online.
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Affiliation(s)
- Alistair E W Johnson
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
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Oster J, Behar J, Sayadi O, Nemati S, Johnson AEW, Clifford GD. Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters. IEEE Trans Biomed Eng 2015; 62:2125-34. [PMID: 25680203 DOI: 10.1109/tbme.2015.2402236] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Automatic processing and accurate diagnosis of pathological electrocardiogram (ECG) signals remains a challenge. As long-term ECG recordings continue to increase in prevalence, driven partly by the ease of remote monitoring technology usage, the need to automate ECG analysis continues to grow. In previous studies, a model-based ECG filtering approach to ECG data from healthy subjects has been applied to facilitate accurate online filtering and analysis of physiological signals. We propose an extension of this approach, which models not only normal and ventricular heartbeats, but also morphologies not previously encountered. A switching Kalman filter approach is introduced to enable the automatic selection of the most likely mode (beat type), while simultaneously filtering the signal using appropriate prior knowledge. Novelty detection is also made possible by incorporating a third mode for the detection of unknown (not previously observed) morphologies, and denoted as X-factor. This new approach is compared to state-of-the-art techniques for the ventricular heartbeat classification in the MIT-BIH arrhythmia and Incart databases. F1 scores of 98.3% and 99.5% were found on each database, respectively, which are superior to other published algorithms' results reported on the same databases. Only 3% of all the beats were discarded as X-factor, and the majority of these beats contained high levels of noise. The proposed technique demonstrates accurate beat classification in the presence of previously unseen (and unlearned) morphologies and noise, and provides an automated method for morphological analysis of arbitrary (unknown) ECG leads.
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