Vitals are Vital: Simpler Clinical Data Model Predicts Decompensation in COVID-19 Patients

Objective Several risk scores have been developed and tested on coronavirus disease 2019 (COVID-19) patients to predict clinical decompensation. We aimed to compare an institutional, automated, custom-built early warning score (EWS) to the National Early Warning Score (NEWS) in COVID-19 patients. Methods A retrospective cohort analysis was performed on patients with COVID-19 infection who were admitted to an intermediate ward from March to December 2020. A machine learning–based customized EWS algorithm, which incorporates demographics, laboratory values, vital signs, and comorbidities, and the NEWS, which uses vital signs only, were calculated at 12-hour intervals. These patients were retrospectively assessed for decompensation in the subsequent 12 or 24 hours, defined as death or transfer to an intensive care unit. Results Of 709 patients, 112 (15.8%) had a decompensation event. Using the custom EWS, decompensation within 12 and 24 hours was predicted with areas under the receiver operating curve (AUC) of 0.81 and 0.79, respectively. The NEWS score applied to the same population yielded AUCs of 0.83 and 0.81, respectively. The 24-hour negative predictive values (NPV) of the NEWS and EWS in patients identified as low risk were 99.6 and 99.2%, respectively. Conclusion The NEWS score performs as well as a customized EWS in COVID-19 patients, demonstrating the significance of vital signs in predicting outcomes. The relatively high positive predictive value and NPV of both scores are indispensable for optimally allocating clinical resources. In this relatively young, healthy population, a more complex score incorporating electronic health record data beyond vital signs does not add clinical benefit.

Duke Scholars

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