Evidence Hub

Categorization of COVID-19 severity to determine mortality risk

Written by Admin | Feb 14, 2023
Purpose Algorithms for classification of inpatient COVID-19 severity are necessary for confounding control in studies using real-world data. Methods Using Healthverity chargemaster and claims data, we selected patients hospitalized with COVID-19 between April 2020 and February 2021, and classified them by severity at admission using an algorithm we developed based on respiratory support requirements (supplemental oxygen or non-invasive ventilation, O2/NIV, invasive mechanical ventilation, IMV, or NEITHER). To evaluate the utility of the algorithm, patients were followed from admission until death, discharge, or a 28-day maximum to report mortality risks and rates overall and by stratified by severity. Trends for heterogeneity in mortality risk and rate across severity classifications were evaluated using Cochran-Armitage and Logrank trend tests, respectively. Results Among 118 117 patients, the algorithm categorized patients in increasing severity as NEITHER (36.7%), O2/NIV (54.3%), and IMV (9.0%). Associated mortality risk (and 95% CI) was 11.8% (11.6–12.0%) overall and increased with severity [3.4% (3.2–3.5%), 11.5% (11.3–11.8%), 47.3% (46.3–48.2%); p < 0.001]. Mortality rate per 1000 person-days (and 95% CI) was 15.1 (14.9–15.4) overall and increased with severity [5.7 (5.4–6.0), 14.5 (14.2–14.9), 32.7 (31.8–33.6); p < 0.001]. Conclusion As expected, we observed a positive association between the algorithm-defined severity on admission and 28-day mortality risk and rate. Although performance remains to be validated, this provides some assurance that this algorithm may be used for confounding control or stratification in treatment effect studies.

View Publication