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Open Access 20-10-2023 | Cardiac Resynchronization Therapy

Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review

Authors: Wojciech Nazar, Stanisław Szymanowicz, Krzysztof Nazar, Damian Kaufmann, Elżbieta Wabich, Rüdiger Braun-Dullaeus, Ludmiła Daniłowicz-Szymanowicz

Published in: Heart Failure Reviews | Issue 1/2024

Abstract

The aim of the presented review is to summarize the literature data on the accuracy and clinical applicability of artificial intelligence (AI) models as a valuable alternative to the current guidelines in predicting cardiac resynchronization therapy (CRT) response and phenotyping of patients eligible for CRT implantation. This systematic review was performed according to the PRISMA guidelines. After a search of Scopus, PubMed, Cochrane Library, and Embase databases, 675 records were identified. Twenty supervised (prediction of CRT response) and 9 unsupervised (clustering and phenotyping) AI models were analyzed qualitatively (22 studies, 14,258 patients). Fifty-five percent of AI models were based on retrospective studies. Unsupervised AI models were able to identify clusters of patients with significantly different rates of primary outcome events (death, heart failure event). In comparison to the guideline-based CRT response prediction accuracy of 70%, supervised AI models trained on cohorts with > 100 patients achieved up to 85% accuracy and an AUC of 0.86 in their prediction of response to CRT for echocardiographic and clinical outcomes, respectively. AI models seem to be an accurate and clinically applicable tool in phenotyping of patients eligible for CRT implantation and predicting potential responders. In the future, AI may help to increase CRT response rates to over 80% and improve clinical decision-making and prognosis of the patients, including reduction of mortality rates. However, these findings must be validated in randomized controlled trials.

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Title: Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review
Authors: Wojciech Nazar
Stanisław Szymanowicz
Krzysztof Nazar
Damian Kaufmann
Elżbieta Wabich
Rüdiger Braun-Dullaeus
Ludmiła Daniłowicz-Szymanowicz
Publication date: 20-10-2023
Publisher: Springer US
Keywords: Cardiac Resynchronization Therapy
Artificial Intelligence
Heart Failure
Cardiac Resynchronization Therapy
Cardiac Resynchronization Therapy
Cardiac Resynchronization Therapy
Published in: Heart Failure Reviews / Issue 1/2024
Print ISSN: 1382-4147
Electronic ISSN: 1573-7322
DOI: https://doi.org/10.1007/s10741-023-10357-8

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Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review

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