Akhil Vaid, Sulaiman Somani, Adam J. Russak, Jessica K. De Freitas, Fayzan F. Chaudhry, Ishan Paranjpe, Kipp W. Johnson, Samuel J. Lee, Riccardo Miotto, Felix Richter, Shan Zhao, Noam D. Beckmann, Nidhi Naik, Arash Kia, Prem Timsina, Anuradha Lala, Manish Paranjpe, Eddye Golden, Matteo Danieletto, Manbir Singh, Dara Meyer, Paul F. O'Reilly, Laura Huckins, Patricia Kovatch, Joseph Finkelstein, Robert M. Freeman, Edgar Argulian, Andrew Kasarskis, Bethany Percha, Judith A. Aberg, Emilia Bagiella, Carol R. Horowitz, Barbara Murphy, Eric J. Nestler, Eric E. Schadt, Judy H. Cho, Carlos Cordon-Cardo, Valentin Fuster, Dennis S. Charney, David L. Reich, Erwin Böttinger, Matthew A. Levin, Jagat Narula, Zahi A. Fayad, Allan C. Just, Alexander W. Charney, Girish N. Nadkarni, Benjamin S. Glicksberg
- Background:
COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.
Objective:
The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.
Methods:
We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from fiveBackground:
COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking.
Objective:
The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points.
Methods:
We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions.
Results:
Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction.
Conclusions:
We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.…
MetadatenAuthor details: | Akhil VaidORCiD, Sulaiman SomaniORCiD, Adam J. RussakORCiD, Jessica K. De FreitasORCiD, Fayzan F. ChaudhryORCiD, Ishan ParanjpeORCiD, Kipp W. JohnsonORCiD, Samuel J. LeeORCiD, Riccardo MiottoORCiD, Felix RichterORCiD, Shan ZhaoORCiD, Noam D. BeckmannORCiD, Nidhi NaikORCiD, Arash KiaORCiD, Prem TimsinaORCiD, Anuradha LalaORCiD, Manish ParanjpeORCiD, Eddye GoldenORCiD, Matteo DanielettoORCiD, Manbir SinghORCiD, Dara MeyerORCiD, Paul F. O'ReillyORCiD, Laura HuckinsORCiD, Patricia KovatchORCiD, Joseph FinkelsteinORCiD, Robert M. FreemanORCiD, Edgar ArgulianORCiD, Andrew KasarskisORCiD, Bethany PerchaORCiD, Judith A. AbergORCiD, Emilia BagiellaORCiD, Carol R. HorowitzORCiD, Barbara MurphyORCiD, Eric J. NestlerORCiD, Eric E. SchadtORCiD, Judy H. ChoORCiD, Carlos Cordon-CardoORCiD, Valentin FusterORCiD, Dennis S. CharneyORCiD, David L. ReichORCiD, Erwin BöttingerORCiDGND, Matthew A. LevinORCiD, Jagat NarulaORCiD, Zahi A. FayadORCiD, Allan C. JustORCiD, Alexander W. CharneyORCiD, Girish N. NadkarniORCiD, Benjamin S. GlicksbergORCiD |
---|
DOI: | https://doi.org/10.2196/24018 |
---|
ISSN: | 1439-4456 |
---|
ISSN: | 1438-8871 |
---|
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/33027032 |
---|
Title of parent work (English): | Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR |
---|
Publisher: | Healthcare World |
---|
Place of publishing: | Richmond, Va. |
---|
Publication type: | Article |
---|
Language: | English |
---|
Date of first publication: | 2020/09/01 |
---|
Publication year: | 2020 |
---|
Release date: | 2023/10/10 |
---|
Tag: | COVID-19; EHR; TRIPOD; clinical; cohort; electronic health record; hospital; informatics; machine learning; mortality; performance; prediction |
---|
Volume: | 22 |
---|
Issue: | 11 |
---|
Article number: | e24018 |
---|
Number of pages: | 19 |
---|
Funding institution: | National Center for Advancing Translational Sciences, National; Institutes of HealthUnited States Department of Health & Human; ServicesNational Institutes of Health (NIH) - USANIH National Center for; Advancing Translational Sciences (NCATS) [U54 TR001433-05] |
---|
Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
---|
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke |
---|
| 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
---|
Peer review: | Referiert |
---|
Publishing method: | Open Access / Gold Open-Access |
---|
| DOAJ gelistet |
---|
License (German): | CC-BY - Namensnennung 4.0 International |
---|