Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients
Journal of the American Medical Informatics Association, 2021•academic.oup.com
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 …
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 …
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.
Oxford University Press以上显示的是最相近的搜索结果。 查看全部搜索结果