Abstract: JMIR MEDICAL INFORMATICS Lerner et al Original Paper Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS) Ivan Lerner1,2,3, MD; Arnaud Serret-Larmande1,2, MD; Bastien Rance1,3, PhD; Nicolas Garcelon3,4, PhD; Anita Burgun1,2,3, MD, PhD; Laurent Chouchana5, PhD, PharmD; Antoine Neuraz1,2,3, MD, PhD 1 Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, Paris, France 2 Informatique biomédicale, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France 3 HeKA Team, Inria, Paris, France 4 Inserm UMR 1163, Data Science Platform, Université de Paris, Imagine Institute, Paris, France 5 Centre Régional de Pharmacovigilance, Service de Pharmacologie, Hôpital Cochin, Assistance Publique - Hôpitaux de Paris, Centre - Université de Paris, Paris, France Corresponding Author: Antoine Neuraz, MD, PhD Inserm Centre de Recherche des Cordeliers Sorbonne Université Université de Paris 15 Rue de l'École de Médecine Paris, 75006 France Phone: 33 01 44 27 64 82 Email: antoine.neuraz@aphp.fr Related Article: This is a corrected version. See correction statement in: https://medinform.jmir.org/2022/4/e38505 Abstract Background: Patients hospitalized for a given condition may be receiving other treatments for other contemporary conditions or comorbidities. The use of such observational clinical data for pharmacological hypothesis generation is appealing in the context of an emerging disease but particularly challenging due to the presence of drug indication bias. Objective: With this study, our main objective was the development and validation of a fully data-driven pipeline that would address this challenge. Our secondary objective was to generate pharmacological hypotheses in patients with COVID-19 and demonstrate the clinical relevance of the pipeline. Methods: We developed a pharmacopeia-wide association study (PharmWAS) pipeline inspired from the PheWAS methodology, which systematically screens for associations between the whole pharmacopeia and a clinical phenotype. First, a fully data-driven procedure based on adaptive least absolute shrinkage and selection operator (LASSO) determined drug-specific adjustment sets. Second, we computed several measures of association, including robust methods based on propensity scores (PSs) to control indication bias. Finally, we applied the Benjamini and Hochberg procedure of the false discovery rate (FDR). We applied this method in a multicenter retrospective cohort study using electronic medical records from 16 university hospitals of the Greater Paris area. We included all adult patients between 18 and 95 years old hospitalized in conventional wards for COVID-19 between February 1, 2020, and June 15, 2021. We investigated the association between drug prescription within 48 hours from admission and 28-day mortality. We validated our data-driven pipeline against a knowledge-based pipeline on 3 treatments of reference, for which experts agreed on the expected association with mortality. We then demonstrated its clinical relevance by screening all drugs prescribed in more than 100 patients to generate pharmacological hypotheses. Results: A total of 5783 patients were included in the analysis. The median age at admission was 69.2 (IQR 56.7-81.1) years, and 3390 (58.62%) of the patients were male. The performance of our automated pipeline was comparable or better for controlling bias than..