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

Dimitris Bertsimas (), Alison Borenstein, Luca Mingardi, Omid Nohadani, Agni Orfanoudaki, Bartolomeo Stellato, Holly Wiberg, Pankaj Sarin, Dirk J. Varelmann, Vicente Estrada, Carlos Macaya and Iván J. Núñez Gil
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Dimitris Bertsimas: 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

Health Care Management Science, 2021, vol. 24, issue 2, No 9, 339-355

Abstract: 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.

Keywords: COVID-19; ACE inhibitors; ARBs; Prescriptive analytics; Machine learning (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10729-021-09545-5

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