Development of a Real-Time Risk Model (RTRM) for Predicting In-Hospital COVID-19 Mortality

Abstract

Background: With over 83 million cases and 1.8 million deaths reported worldwide by the end of 2020 for SARS-CoV-2 (COVID-19), there is an urgent need to enhance identification of high-risk populations to properly evaluate therapy effectiveness with real-world evidence and improve outcomes. Methods: Baseline and daily indicators were evaluated using electronic health records for 46,971 patients hospitalized with COVID-19 from 176 HCA Healthcare-affiliated hospitals, presenting from March to September 2020, to develop a real-time risk model (RTRM) of all-cause, hospitalized mortality. Patient facility, dates-of-care, clinico-demographics, comorbidities, vitals, laboratory markers, and respiratory support findings were aggregated in a logistic regression model. Findings: The RTRM predicted overall mortality as well as mortality 1, 3, and 7 days in advance with an area under the receiver operating characteristic curve (AUCROC) of 0.905, 0.911, 0.905, and 0.901 respectively, significantly outperforming a combined model of age and daily modified WHO progression scale (all p<0.0001; AUCROC, 0.846, 0.848, 0.850, and 0.852). The RTRM delineated risk at presentation from ongoing risk associated with medical care and showed that mortality rates decreased over time due to both decreased severity and changes in care. Interpretation: To our knowledge, this study is the largest of its kind to comprehensively evaluate predictors and incorporate daily risk measures of COVID-19 mortality. The RTRM validates current literature trends in mortality across time and allows direct translation for research and clinical applications.

Convalescent plasma associates with reduced mortality and improved clinical trajectory in patients hospitalized with COVID-19

“…Inconsistencies in supply-and-demand occurred throughout 2020 in various outbreak locations. These challenges made it difficult for most research groups to create a well-matched comparison cohort; however, we were able to capitalize on the granularity of our RTRM and the coarsened exact matching approach to match on hundreds of clinico-demographic and biomarker features to identify a properly matched cohort (29). Although RCTs are the gold-standard for assessing efficacy, their deployment during a rapidly evolving pandemic is especially challenging at-scale and in the community setting.…”

“…RTRM risk trajectory (secondary outcome). Daily mortality risk scores were generated for each patient across the length of their hospitalization using probabilities from our COVID-19 Real-Time Risk Model (RTRM), which incorporates hundreds of structured medical record features such as clinico-demographics, comorbidities, laboratory values, secondary infections, complications, oxygenation details, and oxygen supplementation (29). When using the daily RTRM probabilities as a longitudinal outcome measure of progression and/or recovery, we also included baseline RTRM probability as a matching variable.…”

“…The WHO PS is a modified 6-point scale, adapted from the WHO R&D Blueprint group to assess disease severity and measure clinical improvement in hospitalized patients (Supplemental Table 10) (29,36). Patients were assigned a daily WHO PS based on their most severe status that day.…”

“…Challenges for retrospective analyses to-date have been the difficulty in accurately generating a matched synthetic control as well as having a sensitive indicator of disease progression and therapeutic response. We previously reported a realtime risk model (RTRM) for COVID-19 that provides a daily granular measure of disease progression to adequately match on baseline disease severity and create a risk trajectory for each patient (29). Using both the daily RTRM probabilities and COVID-19 WHO progression scale (WHO PS), we retrospectively examined the association of CP with all-cause, in-hospital mortality and clinical recovery in matched cohorts derived from our COVID-19 registry.…”