Classification and severity progression measure of COVID-19 patients using pairs of multi-omic factors

Abstract

Early detection and effective treatment of severe COVID-19 patients remain two major challenges during the current pandemic. Analysis of molecular changes in blood samples of severe patients is one of the promising approaches to this problem. From thousands of proteomic, metabolomic, lipidomic, and transcriptomic biomarkers selected in other research, we identify several pairs of biomarkers that after additional nonlinear spline transformation are highly effective in classifying and predicting severe COVID-19 cases. The performance of these pairs is evaluated in-sample, in a cross-validation exercise, and in an out-of-sample analysis on two independent datasets. We further improve our classifier by identifying complementary pairs using hierarchical clustering. In a result, we achieve 96–98% AUC on the validation data. Our findings can help medical experts to identify small groups of biomarkers that after nonlinear transformation can be used to construct a cost-effective test for patient screening and prediction of severity progression.