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Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients

Author

Listed:
  • Dimitris Bertsimas

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Alison Borenstein

    (Massachusetts Institute of Technology)

  • Luca Mingardi

    (Massachusetts Institute of Technology)

  • Omid Nohadani

    (Benefits Science Technologies)

  • Agni Orfanoudaki

    (Massachusetts Institute of Technology)

  • Bartolomeo Stellato

    (Princeton University)

  • Holly Wiberg

    (Massachusetts Institute of Technology)

  • Pankaj Sarin

    (Brigham and Women’s Hospital)

  • Dirk J. Varelmann

    (Brigham and Women’s Hospital)

  • Vicente Estrada

    (Hospital Clínico San Carlos)

  • Carlos Macaya

    (Hospital Clínico San Carlos)

  • Iván J. Núñez Gil

    (Hospital Clínico San Carlos)

Abstract

The COVID-19 pandemic has prompted an international effort to develop and repurpose medications and procedures to effectively combat the disease. Several groups have focused on the potential treatment utility of angiotensin-converting–enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients, with inconclusive evidence thus far. We couple electronic medical record (EMR) and registry data of 3,643 patients from Spain, Italy, Germany, Ecuador, and the US with a machine learning framework to personalize the prescription of ACEIs and ARBs to hypertensive COVID-19 patients. Our approach leverages clinical and demographic information to identify hospitalized individuals whose probability of mortality or morbidity can decrease by prescribing this class of drugs. In particular, the algorithm proposes increasing ACEI/ARBs prescriptions for patients with cardiovascular disease and decreasing prescriptions for those with low oxygen saturation at admission. We show that personalized recommendations can improve patient outcomes by 1.0% compared to the standard of care when applied to external populations. We develop an interactive interface for our algorithm, providing physicians with an actionable tool to easily assess treatment alternatives and inform clinical decisions. This work offers the first personalized recommendation system to accurately evaluate the efficacy and risks of prescribing ACEIs and ARBs to hypertensive COVID-19 patients.

Suggested Citation

  • Dimitris Bertsimas & Alison Borenstein & Luca Mingardi & Omid Nohadani & Agni Orfanoudaki & Bartolomeo Stellato & Holly Wiberg & Pankaj Sarin & Dirk J. Varelmann & Vicente Estrada & Carlos Macaya & Iv, 2021. "Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients," Health Care Management Science, Springer, vol. 24(2), pages 339-355, June.
    • Handle: RePEc:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-021-09545-5
    DOI: 10.1007/s10729-021-09545-5
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    References listed on IDEAS

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    1. Dimitris Bertsimas & Agni Orfanoudaki & Rory B. Weiner, 2020. "Personalized treatment for coronary artery disease patients: a machine learning approach," Health Care Management Science, Springer, vol. 23(4), pages 482-506, December.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
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