A machine learning model exploring the relationship between chronic medication and COVID-19 clinical outcomes
Miró, Berta (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Díaz González, Natalia (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Martínez-Cerdá, Juan-Francisco (Agència de Qualitat i Avaluació Sanitàries de Catalunya)
Viñas-Bardolet, Clara (Agència de Qualitat i Avaluació Sanitàries de Catalunya)
Sánchez, Alex (Universitat de Barcelona. Departament de Genètica, Microbiologia i Estadística)
Sánchez-Montalvá, Adrián (Universitat Autònoma de Barcelona. Departament de Medicina)
Miarons, Marta (Hospital Universitari Vall d'Hebron)

Data:	2025
Resum:	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.
Nota:	Altres ajuts: acords transformatius de la UAB
Drets:	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.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	ACE inhibitors ; ARBs ; COVID-19 ; HMG-CoA reductase ; Machine learning ; Metformin ; Mortality ; Polypharmacy ; Prediction models
Publicat a:	International Journal of Clinical Pharmacy, Vol. 47, Núm. 4 (August 2025) , p. 1075-1086, ISSN 2210-7711

DOI: 10.1007/s11096-025-01955-7
PMID: 40720062

12 p, 1.2 MB

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