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Optimising Patient Selection for Primary Prevention ICD Implantation: Utilising Multimodal Machine Learning to Assess Risk of ICD Non-Benefit.

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Optimising Patient Selection for Primary Prevention ICD Implantation: Utilising Multimodal Machine Learning to Assess Risk of ICD Non-Benefit. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology Kolk, M. Z., Ruiperez-Campillo, S., Deb, B., Bekkers, E., Allaart, C. P., Rogers, A. J., Van Der Lingen, A. C., Alvarez Florez, L., Isgum, I., De Vos, B., Clopton, P., Wilde, A. A., Knops, R. E., Narayan, S. M., Tjong, F. V. 2023

Abstract

BACKGROUND: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalised predictions using complex, multimodal data. This study aimed to determine if risk stratification for implantable cardioverter defibrillator (ICD) implantation can be improved by ML models that combine clinical variables with 12-lead electrocardiograms (ECG) time-series features.METHODS: A multicentre study of 1010 patients (64.9 ±10.8 years, 26.8% female) with ischaemic, dilated, or non-ischaemic cardiomyopathy, and LVEF=35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD in two academic hospitals was performed. For each patient, a raw 12-lead, 10-second ECG obtained within 90 days before ICD implantation and clinical details were collected. Supervised ML models were trained and validated on a development cohort (n=550) from Hospital A to predict ICD non-arrhythmic mortality at 3-year follow-up (i.e. mortality without prior appropriate ICD-therapy). Model performance was evaluated on an external patient cohort from Hospital B (n=460).RESULTS: At 3-year follow-up, 16.0% of patients had died, with 72.8% meeting criteria for non-arrhythmic mortality. Extreme gradient boosting models identified patients with non-arrhythmic mortality with an area under the receiver operating characteristic curve (AUROC) of 0.90 (95% CI 0.80-1.00) during internal validation. In the external cohort, the AUROC was 0.79 (95% CI 0.75-0.84).CONCLUSIONS: ML models combining ECG time-series features and clinical variables were able to predict non-arrhythmic mortality within 3 years after device implantation in a primary prevention population, with robust performance in an independent cohort.

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