Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients
Abstract Objective Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate...
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| Vydáno v: | Journal of the American Medical Informatics Association : JAMIA Ročník 28; číslo 7; s. 1480 - 1488 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
Oxford University Press
14.07.2021
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| ISSN: | 1527-974X, 1527-974X |
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| Abstract | Abstract
Objective
Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.
Materials and Methods
For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output.
Results
The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.
Discussion
Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.
Conclusions
We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results. |
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| AbstractList | Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.OBJECTIVECoronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output.MATERIALS AND METHODSFor each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output.The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.RESULTSThe predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.DISCUSSIONOur models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.CONCLUSIONSWe develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results. Abstract Objective Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. Materials and Methods For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output. Results The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. Discussion Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. Conclusions We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results. Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output. The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results. |
| Author | Sengupta, Soumitra Elhadad, Noemie Adelman, Jason Chen, Ruijun Pang, Chao Natarajan, Karthik Hripcsak, George Elias, Pierre Groves, Holden Green, Robert Perotte, Adler Rodriguez, Victor Alfonso Metitiri, Katherine Schlosser Mohan, Sumit Bhave, Shreyas |
| Author_xml | – sequence: 1 givenname: Victor Alfonso surname: Rodriguez fullname: Rodriguez, Victor Alfonso email: victor.a.rodriguez@columbia.edu organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 2 givenname: Shreyas surname: Bhave fullname: Bhave, Shreyas organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 3 givenname: Ruijun orcidid: 0000-0001-5281-4143 surname: Chen fullname: Chen, Ruijun organization: Department of Biomedical Informatics, Columbia University – sequence: 4 givenname: Chao surname: Pang fullname: Pang, Chao organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 5 givenname: George surname: Hripcsak fullname: Hripcsak, George organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 6 givenname: Soumitra surname: Sengupta fullname: Sengupta, Soumitra organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 7 givenname: Noemie surname: Elhadad fullname: Elhadad, Noemie organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 8 givenname: Robert surname: Green fullname: Green, Robert organization: Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York, USA – sequence: 9 givenname: Jason surname: Adelman fullname: Adelman, Jason organization: Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA – sequence: 10 givenname: Katherine Schlosser surname: Metitiri fullname: Metitiri, Katherine Schlosser organization: Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA – sequence: 11 givenname: Pierre surname: Elias fullname: Elias, Pierre organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 12 givenname: Holden surname: Groves fullname: Groves, Holden organization: Department of Anesthesiology, Columbia University Irving Medical Center, New York, New York, USA – sequence: 13 givenname: Sumit surname: Mohan fullname: Mohan, Sumit organization: Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA – sequence: 14 givenname: Karthik orcidid: 0000-0002-9066-9431 surname: Natarajan fullname: Natarajan, Karthik organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 15 givenname: Adler surname: Perotte fullname: Perotte, Adler organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA |
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| Keywords | COVID-19 supervised machine learning patient readmission renal replacement therapy artificial respiration |
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Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal... Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy... |
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| SubjectTerms | Adolescent Adult Aged Aged, 80 and over Area Under Curve COVID-19 - complications COVID-19 - therapy Electronic Health Records Female Humans Logistic Models Male Middle Aged Models, Statistical Patient Readmission Prognosis Renal Replacement Therapy Respiration, Artificial Retrospective Studies ROC Curve Statistics, Nonparametric Young Adult |
| Title | Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients |
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