Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients

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  • Victor Alfonso Rodriguez
    Department of Biomedical Informatics, Columbia University, New York, New York, USA
  • Shreyas Bhave
    Department of Biomedical Informatics, Columbia University, New York, New York, USA
  • Ruijun Chen
  • Chao Pang
    Department of Biomedical Informatics, Columbia University, New York, New York, USA
  • George Hripcsak
    Department of Biomedical Informatics, Columbia University, New York, New York, USA
  • Soumitra Sengupta
    Department of Biomedical Informatics, Columbia University, New York, New York, USA
  • Noemie Elhadad
    Department of Biomedical Informatics, Columbia University, New York, New York, USA
  • Robert Green
    Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York, USA
  • Jason Adelman
    Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
  • Katherine Schlosser Metitiri
    Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA
  • Pierre Elias
    Department of Biomedical Informatics, Columbia University, New York, New York, USA
  • Holden Groves
    Department of Anesthesiology, Columbia University Irving Medical Center, New York, New York, USA
  • Sumit Mohan
    Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
  • Karthik Natarajan
    Department of Biomedical Informatics, Columbia University, New York, New York, USA
  • Adler Perotte
    Department of Biomedical Informatics, Columbia University, New York, New York, USA

説明

<jats:title>Abstract</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Materials and Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>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.</jats:p></jats:sec>

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