A data-driven approach to identify risk profiles and protective drugs in COVID-19

Abstract

As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensin-converting enzyme 2 (ACE2), but whether or not renin-angiotensin-aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19.

Keywords: Bayesian network; COVID-19; RAAS; survival tree.

Fig. 1.

ST analysis (A) and Kaplan−Meier curves and log-rank test (B) for the three risk groups obtained from the ST analysis based on their HR computed in the final nodes. The low-risk group includes those patients falling in final nodes with an HR lower than 1 (n = 322, 63.5%), patients in the medium-risk group are those with an HR between 1 and 2 (n = 166, 21.6%), and patients in the high-risk groups are those with an HR higher than 2 (n = 88, 16.8%). The P value associated with the log-rank test is also displayed.

Fig. 2.

ST analysis without medications as input variables (A) and Kaplan−Meier curves and log-rank test (B) for the two risk groups obtained from the ST analysis based on their HR computed in the final nodes. The low-risk group includes those patients falling in final nodes with an HR lower that 1 (n = 391, 67.8%), and patients in the high-risk groups are those with an HR higher than 2 (n = 185, 32.12%). The P value associated with the log-rank test is also displayed. Kaplan−Meier curves and log-rank test for the low-risk group (C) and the high-risk group (D) identified by the ST analysis without medications as input variables. Analysis aimed at examining the differences between patients treated with and without RAAS blockers in the two risk groups. The P value associated with the log-rank test is also displayed.

Fig. 3.

Bayesian network analysis (A) exploring the dependence structure of the data. Conditional probabilities of the target variable (in-hospital death) given several scenarios by fixing hypertension and RAASi (B) and CVD and RAASi (C).