Contemporary Applications of Machine Learning for Device Therapy in Heart Failure.

Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of "big data" and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.

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Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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Funding Support and Author Disclosures Dr Greene has received research support from the Duke University Department of Medicine Chair’s Research Award, American Heart Association, Amgen, AstraZeneca, Bristol Myers Squibb, Cytokinetics, Merck, Novartis, Pfizer, and Sanofi; has served on advisory boards for Amgen, AstraZeneca, Bristol Myers Squibb, Cytokinetics, Roche Diagnostics, and Sanofi; has received speaker fees from Boehringer Ingelheim; and serves as a consultant for Amgen, Bayer, Bristol Myers Squibb, Merck, PharmaIN, Sanofi, Tricog Health, Urovant Pharmaceuticals, and Vifor. Dr Al’Aref is supported by National Institutes of Health 2R01 HL12766105 and 1R21 EB030654; and has received royalty fees from Elsevier. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.