Baseline Human Metabolic Profiling and Risk of Death from COVID-19: Conceptualization of Multivariate Prediction Model Development via Retrospective Database Analysis in the United States Department of Veterans Affairs

Background/Objectives: Prediction models are implemented frequently, yet, compared with other study designs, their incorporation of clinical measurements (CMs; i.e., vital signs and laboratory results) is rather underdeveloped. The purpose is to describe methods used and illustrate clinical utility in parameters systematically derived from CMs; as a case study, we use the risk of all-cause mortality following coronavirus disease 2019 (COVID-19) as the basis for prognosis. Methods: We identified cases through the Department of Veterans Affairs COVID-19 Shared Data Resource, utilizing data from the first visit until 14 days before testing positive. Thirteen parameters were derived from each of the 11 CMs, capturing departures from normality considering variability and time. The 143 candidate predictors were used to generate the main logistic regression model. The area under the receiver operating characteristic curve (AUROC) analysis was performed to assess discrimination between those who lived and died for subset and main regressions; for comparison, this was performed for an age-only model and the Charlson Comorbidity and Elixhauser Indices. Results: There were 329,491 patients. The main model’s AUROC (0.785 ± 0.002) was similar to the age-only model (0.783 ± 0.002; p > 0.05) and significantly greater than the comorbidity indices’ (range: 0.675 ± 0.002 to 0.729 ± 0.002; p < 0.001 each). Conclusions: The study found several parameters were significant determinants of mortality following COVID-19, highlighting the importance of a systematic approach for multivariate modeling to obtain informative insights into underlying pathophysiology. The main model outperforms common comorbidity indices as a summary metric for pre-existing conditions in this case study. If validated, this approach could revolutionize the way CMs are handled in multivariate models.