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Background <p>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.</p> Aim <p>This study aimed to investigate the association between chronic medication and COVID-19 outcomes, using machine learning to identify key medication-related factors.</p> Method <p>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.</p> Results <p>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).</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月28日

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  • Berta Miró,
  • Natalia Díaz González,
  • Juan-Francisco Martínez-Cerdá,
  • Clara Viñas-Bardolet,
  • Alex Sánchez-Pla,
  • Adrián Sánchez-Montalvá,
  • Marta Miarons

摘要

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.