Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide Association Study (PharmWAS)
Lerner et al.
, Mining Electronic Health Records for Drugs Associated With 28-day Mortality in COVID-19: Pharmacopoeia-wide..
, JMIR Medical Informatics, doi:10.2196/35190
Retrospective 5,783 hospitalized patients in France, showing higher mortality with paracetamol use, without statistical significance.
risk of death, 26.9% higher, RR 1.27, p = 0.10, odds ratio converted to relative risk, weighted and trimmed, day 28, control prevalance approximated with overall prevalence.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Lerner et al., 30 Mar 2022, retrospective, France, peer-reviewed, median age 69.2, 7 authors, study period 1 February, 2020 - 15 June, 2021.
Abstract: JMIR MEDICAL INFORMATICS
Lerner et al
Mining Electronic Health Records for Drugs Associated With
28-day Mortality in COVID-19: Pharmacopoeia-wide Association
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
Inserm, Centre de Recherche des Cordeliers, Sorbonne Université, Paris, France
Informatique biomédicale, Hôpital Necker-Enfants Malades, Assistance Publique - Hôpitaux de Paris, Paris, France
HeKA Team, Inria, Paris, France
Inserm UMR 1163, Data Science Platform, Université de Paris, Imagine Institute, Paris, France
Centre Régional de Pharmacovigilance, Service de Pharmacologie, Hôpital Cochin, Assistance Publique - Hôpitaux de Paris, Centre - Université de
Paris, Paris, France
Antoine Neuraz, MD, PhD
Centre de Recherche des Cordeliers
Université de Paris
15 Rue de l'École de Médecine
Phone: 33 01 44 27 64 82
This is a corrected version. See correction statement in: https://medinform.jmir.org/2022/4/e38505
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
is less effective
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