Key result
Supervised artificial intelligence models achieved up to 85% accuracy and an AUC of 0.86 in predicting response to cardiac resynchronization therapy, compared to 70% accuracy using current guidelines.
Why the study?
To summarize literature data on the accuracy and clinical applicability of AI models as an alternative to current guidelines in predicting CRT response and phenotyping eligible patients.
Do artificial intelligence models improve the prediction of response to cardiac resynchronization therapy in patients eligible for CRT?
Systematic Review (n=14,258)
Do artificial intelligence models improve the prediction of response to cardiac resynchronization therapy in patients eligible for CRT?
Artificial intelligence models demonstrate high accuracy (up to 85%) in predicting response to cardiac resynchronization therapy, potentially outperforming current guideline-based selection criteria.
AI models may aid CRT phenotyping but should not yet change practice; leaves open prospective validation trials.
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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Nazar et al. (2023) conducted a systematic review in Heart failure eligible for cardiac resynchronization therapy (n=14,258). Artificial intelligence models vs. Guideline-based criteria was evaluated on Accuracy of predicting response to cardiac resynchronization therapy. Supervised artificial intelligence models achieved up to 85% accuracy and an AUC of 0.86 in predicting response to cardiac resynchronization therapy, compared to 70% accuracy using current guidelines.
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