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

Pietro E. Cippà; Federica Cugnata; Paolo Ferrari; Chiara Brombin; Lorenzo Ruinelli; Giorgia Bianchi; Nicola Beria; Lukas Schulz; Enos Bernasconi; Paolo Merlani; Alessandro Ceschi; Clelia Di Serio

doi:10.1073/pnas.2016877118

New Research In

Edited by Purvesh Khatri, Stanford University, Stanford, CA, and accepted by Editorial Board Member Robert Langer November 12, 2020 (received for review August 10, 2020)

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Correction for Cippà et al., A data-driven approach to identify risk profiles and protective drugs in COVID-19 - February 08, 2021

Significance

The global outbreak of COVID-19 infections generated an unprecedented need to develop novel therapeutic strategies. The SARS-CoV-2 virus enters host cells after binding to the angiotensin-converting enzyme 2 (ACE2), but whether renin−angiotensin−aldosterone system inhibitors (RAASi) are beneficial remains controversial. Standard statistical approaches may fail in assessing medications effects, due to multiple sources of bias in COVID-19 case series collected on an emergency basis. We present a data-driven approach to tackle these challenges. Multilayer risk stratifications were derived for assessing drugs effect, while Bayesian networks were estimated, to analyze dependencies among risk factors’ and treatments’ impact on survival. We provide strong evidence for protectivity of RAASi on hospitalized patients that call for randomized controlled trials of RAASi as COVID-19 treatment option.

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.

Footnotes

↵¹P.E.C., F.C., P.F., A.C., and C.D.S. contributed equally to this work.
↵²To whom correspondence may be addressed. Email: diserio.clelia{at}unisr.it or Pietro.Cippa{at}eoc.ch.

P.E.C., P.F., A.C., and C.D.S. designed research; L.R., G.B., N.B., and L.S. collected and processed data; E.B. and P.M., performed research; F.C., C.B., and C.D.S. performed statistical analysis; F.C., C.B., and C.D.S. developed tools for analysis; P.E.C., F.C., P.F., C.B., A.C., and C.D.S. discussed results; and P.E.C., F.C., P.F., C.B., A.C., and C.D.S. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission. P.K. is a guest editor invited by the Editorial Board.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2016877118/-/DCSupplemental.

Data Availability.

Anonymized data have been deposited in The Open Science Framework at https://osf.io/sj4zu. The password to open the file will be provided by the authors upon request.