Fig. 4: Ranking autoantibodies as predictors of disease severity reveals an overlap between their patterns in moderate and severe COVID-19.

a Receiver operating characteristic (ROC) curves of 17 antibodies from mild (n = 74), moderate (n = 63), and severe (n = 32) COVID-19 patients versus healthy controls (n = 77) with an area under the curve (AUC) of 89.8% (for controls), 87.6% (for mild), 88,7% (for moderate), and 75.5% (for severe). b Stable curve showing number of trees and out-of-bag (OOB) error rate of 30.05%. c ROC curve of the same antibodies as in (a) from mild COVID-19 and moderate/severe COVID-19 patients compared to healthy controls with an AUC of 93.1% (for controls), 87.7% (for mild) and 96.2% (for moderate/severe). d Stable curve showing number of trees and OOB error rate of 22,95%. e Ranking of the top 10 autoantibody predictors of disease severity according to the mean minimal depth (black vertical bar with the mean value) calculated based on the number of trees. The blue color gradient reveals the minimum and maximum minimal depths for each variable. f Variable importance score plot based on Gini decrease and number (no.) of nodes for each variable showing which variable (antibody) presents a higher score in predicting COVID-19 severity. Source data are provided as a Source Data file.