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
Hlavní autoři: Rodriguez, Victor Alfonso, Bhave, Shreyas, Chen, Ruijun, Pang, Chao, Hripcsak, George, Sengupta, Soumitra, Elhadad, Noemie, Green, Robert, Adelman, Jason, Metitiri, Katherine Schlosser, Elias, Pierre, Groves, Holden, Mohan, Sumit, Natarajan, Karthik, Perotte, Adler
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 14.07.2021
Témata:
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
COVID-19
supervised machine learning
patient readmission
renal replacement therapy
artificial
respiration
ISSN:1527-974X, 1527-974X
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Shrnutí: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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ISSN:1527-974X
1527-974X
DOI:10.1093/jamia/ocab029