Prospective validation of a dynamic prognostic model for identifying COVID-19 patients at high risk of rapid deterioration.

BACKGROUND
We sought to develop and prospectively validate a dynamic model that incorporates changes in biomarkers to predict rapid clinical deterioration in patients hospitalized for COVID-19.


METHODS
We established a retrospective cohort of hospitalized patients aged ≥18 years with laboratory-confirmed COVID-19 using electronic health records (EHR) from a large integrated care delivery network in Massachusetts including > 40 facilities from March to November 2020. A total of 71 factors, including time-varying vital signs and laboratory findings during hospitalization were screened. We used elastic net regression and tree-based scan statistics for variable selection to predict rapid deterioration, defined as progression by two levels of a published severity scale in the next 24 hours. The development cohort included the first 70% of patients identified chronologically in calendar time; the latter 30% served as the validation cohort. A cut-off point was estimated to alert clinicians of high risk of imminent clinical deterioration.


RESULTS
Overall, 3,706 patients (2,587 in the development and 1,119 in the validation cohort) met the eligibility criteria with a median of 6 days of follow-up. Twenty-four variables were selected in the final model, including 16 dynamic changes of laboratory results or vital signs. Area under the ROC curve was 0.81 (95% CI, 0.79 - 0.82) in the development set and 0.74 (95% CI, 0.71-0.78) in the validation set. The model was well calibrated (slope = 0.84 and intercept = -0.07 on the calibration plot in the validation set). The estimated cut-off point, with a positive predictive value of 83%, was 0.78.


CONCLUSIONS
Our prospectively validated dynamic prognostic model demonstrated temporal generalizability in a rapidly evolving pandemic and can be used to inform day-to-day treatment and resource allocation decisions based on dynamic changes in biophysiological factors. This article is protected by copyright. All rights reserved.