Mortality Prediction Using Medication-Related Data in Patients with Covid-19

ABSTRACT

INTRODUCTION: The medication regimen complexityintensive care unit (MRC-ICU) score was developed prior to the existence of COVID-19 and has demonstrated an association with increased mortality, ICU length of stay, fluid balance, drug interactions, and quantity and quality of pharmacist interventions. Previous reports have questioned the ability of traditional predictors of mortality in critically ill patients to predict death in patients with COVID-19. The purpose of this study was to assess if MRC-ICU could predict mortality patients with COVID-19. METHOD(S) A single-center, observational study was conducted from August 2020 to January 2021. The primary outcome of this study was the area under the receiver operating characteristic (AUROC) for mortality for the 48- hour MRC-ICU. Age, sequential organ failure assessment (SOFA), and World Health Organization (WHO) COVID-19 Severity Classification were also assessed. Logistic regression was also performed to predict mortality as well as WHO Severity Classification at 7 days. RESULT(S) A total of 149 patients were included. The median SOFA score was 8 (IQR 5 - 11) and median MRC-ICU score at 48 hours was 15 (IQR 7 - 21). The inhospital mortality rate was 36% (n = 54). The AUROC for MRC-ICU was 0.71 (95% Confidence Interval (CI), 0.62 - 0.78) compared to 0.66 for age, 0.81 SOFA, and 0.72 for the WHO Severity Classification. In univariate analysis, age, SOFA, MRC-ICU, and WHO Severity Classification all demonstrated significant association with mortality, while SOFA, MRC-ICU, and WHO Severity Classification demonstrated significant association with WHO Severity Classification at 7 days. A multiple logistic regression model for mortality was developed using these four predictors. CONCLUSION(S) In the first analysis of medication-related variables as a predictor of severity and mortality in COVID-19, MRC-ICU demonstrated acceptable predictive ability;however, SOFA was the strongest predictor in both AUROC and regression analysis.