2026-04-14T02:07:55Zhttps://recercat.cat/oai/request

oai:recercat.cat:2072/4868112025-09-17T08:40:46Zcom_2072_98col_2072_378192

A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes Miró, Berta Díaz González, Natalia Martínez-Cerdá, Juan-Francisco Viñas-Bardolet, Clara Sánchez, Alex Sánchez-Montalvá, Adrián Miarons, Marta ACE inhibitors ARBs COVID-19 HMG-CoA reductase Machine learning Metformin Mortality Polypharmacy Prediction models Altres ajuts: acords transformatius de la UAB 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 or biguanides should continue their prescribed medications, which may offer protection over alternative treatments. 2025 Article https://ddd.uab.cat/record/318583 urn:10.1007/s11096-025-01955-7 urn:oai:ddd.uab.cat:318583 urn:scopus_id:105012031583 urn:articleid:22107711v47n4p1075 urn:pmid:40720062 urn:pmc-uid:12335402 urn:pmcid:PMC12335402 urn:oai:pubmedcentral.nih.gov:12335402 http://hdl.handle.net/2072/486811 eng International Journal of Clinical Pharmacy ; Vol. 47, Núm. 4 (August 2025), p. 1075-1086 open access Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. https://creativecommons.org/licenses/by/4.0/ application/pdf