Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients
Name
10729_2021_9545_ReferencePDF.pdf
Size
638.08 KB
Format
Adobe PDF
Checksum (MD5)
4ae06eacc53e231f69dca6f2e5aedcb3
Author(s)
Bertsimas, Dimitris
Borenstein, Alison
Mingardi, Luca
Nohadani, Omid
Orfanoudaki, Agni
Stellato, Bartolomeo
Wiberg, Holly
Sarin, Pankaj
Varelmann, Dirk J.
Estrada, Vicente
Date Issued
March 15, 2021
Publisher
Springer US
Version
Author's final manuscript
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.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10729-021-09545-5
