A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes

Aug 5, 2026

Chronic medication’s influence on COVID-19 outcomes has remained contested since the pandemic began, motivating a data-driven assessment of how long-term treatments relate to infection severity and prognosis. Using machine learning, the study analyzes 137,835 COVID-19 patients in Catalonia (Feb–Sep 2020) to predict hospitalization, ICU admission, and mortality, complemented by logistic regression and focused sensitivity analyses. Results highlight key medication-related predictors and suggest protective associations for specific drug classes.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

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Aim

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Results

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Impact statements

Introduction

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Study population, registry features, and data acquisition

International Journal of Clinical Pharmacy (2025) 47:1075–1086 [https://doi.org/10.1007/s1](https://doi.org/10.1007/s1) 1096-025-01955-7 A machine learning model exploring the relationship between chronic medication and COVID‑19 clinical outcomes Berta Miró1 · Natalia Díaz González1 · Juan‑Francisco Martínez‑Cerdá2 · Clara Viñas‑Bardolet2 · Alex Sánchez‑Pla3,4 · Adrián Sánchez‑Montalvá5,6,7 · Marta Miarons8,9 Received: 20 February 2025 / Accepted: 25 May 2025 / Published online: 28 July 2025 © The Author(s) 2025 Abstract Background The impact of chronic medication on COVID-19 outcomes has been a topic of ongoing debate since the onset of the pandemic. Investigating how specific long-term treatments influence infection severity and prognosis is essential for optimising patient management and care. Aim This study aimed to investigate the association between chronic medication and COVID-19 outcomes, using machine learning to identify key medication-related factors. Method We analysed 137,835 COVID-19 patients in Catalonia (February–September 2020) using eXtreme Gradient Boosting to predict hospitalisation, ICU admission, and mortality. This was complemented by univariate logistic regression analyses and a sensitivity analysis focusing on diabetes, hypertension, and lipid disorders. Results Participants had a mean age of 53 (SD 20) years, with 57% female. The best model predicted mortality risk in 18 to 65-year-olds (AUCROC 0.89, CI 0.85–0.92) . Key features identified included the number of prescribed drugs, systemic corticoids, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase, and hypertension drugs. A sensitivity analysis identified that hypertensive participants over 65 taking angiotensin-converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs) had lower mortality risk (OR 0.78 CI 0.68–0.92) compared to those on other antihypertensive medication (OR 0.8 CI 0.68–0.95). Treatment with inhibitors of dipeptidyl peptidase 4 was associated to higher mortality in participants aged 18–65, while metformin showed a protective effect in those over 65 (OR 0.79, 95% CI 0.68–0.92) . Conclusion Machine learning models effectively distinguished COVID-19 outcomes. Patients under ACEi or ARBs orbiguanides should continue their prescribed medications, which may offer protection over alternative treatments. Keywords ACE inhibitors · ARBs · COVID-19 · HMG-CoA reductase · Machine learning · Metformin · Mortality · Polypharmacy · Prediction models * Adrián Sánchez-Montalvá [Adrian.Sanchez.Montalva@uab.cat](Adrian.Sanchez.Montalva@uab.cat) 1 Statistics and Bioinformatics Unit, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain 2 Agency for Health Quality and Assessment of Catalonia (AQuaS), Barcelona, Spain 3 Genetics, Microbiology and Statistics Department, Universitat de Barcelona, Barcelona, Spain 4 Centro de Investigación Biomédica en Red Fragilidad y Envejecimiento Saludable, Instituto de Salud Carlos III, 28029 Madrid, Spain 5 Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain 6 International Health Unit Vall d’Hebron (PROSICS), Infectious Diseases Department, Vall d’Hebron University Hospital, Vall d’Hebron Institute of Research, Barcelona, Spain 7 Centre for Biomedical Research in Infectious Diseases Network (CIBERINFEC), Institute of Health Carlos III, Madrid, Spain 8 Pharmacy Department, Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain 9 Pharmacy Department, Consorci Hospitalari de Vic, Barcelona, Spain Impact statements • Machine learning-driven models effectively predict hospitalisation, ICU admission, and death in COVID- 19 patients. • ACEi, ARBs, and biguanides (metformin) demonstrated potential protective effects. • Findings support continued use of these medications without switching to alternatives. • The study methodology is scalable and can be applied at a low additional cost

FAQs

What question does the study investigate about chronic medication and COVID-19?

It examines the association between chronic medication and COVID-19 outcomes, identifying key medication-related factors using machine learning.

How was the prediction model built and validated?

The analysis used 137,835 COVID-19 patients from Catalonia (Feb–Sep 2020) and applied eXtreme Gradient Boosting to predict hospitalization, ICU admission, and mortality, with logistic regression and sensitivity analyses.

Which chronic medication classes were highlighted as influential for outcomes?

The study identified features such as systemic corticoids and HMG-CoA reductase, and found associations involving ACE inhibitors/ARBs, dipeptidyl peptidase 4 inhibitors, and metformin across specific age groups.

Aug 5, 2026

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