TY - JOUR
T1 - Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation
T2 - utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit
AU - Kolk, Maarten Z. H.
AU - Ruipérez-Campillo, Samuel
AU - Deb, Brototo
AU - Bekkers, Erik J.
AU - Allaart, Cornelis P.
AU - Rogers, Albert J.
AU - van der Lingen, Anne-Lotte C. J.
AU - Alvarez Florez, Laura
AU - Isgum, Ivana
AU - de Vos, Bob D.
AU - Clopton, Paul
AU - Wilde, Arthur A. M.
AU - Knops, Reinoud E.
AU - Narayan, Sanjiv M.
AU - Tjong, Fleur V. Y.
N1 - Publisher Copyright: © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.
PY - 2023/8/2
Y1 - 2023/8/2
N2 - AIMS: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized 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 AND RESULTS: 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-s ECG was 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 three-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). At three-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% confidence intervals (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 three years after device implantation in a primary prevention population, with robust performance in an independent cohort.
AB - AIMS: Left ventricular ejection fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD). Machine learning (ML) provides new opportunities for personalized 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 AND RESULTS: 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-s ECG was 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 three-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). At three-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% confidence intervals (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 three years after device implantation in a primary prevention population, with robust performance in an independent cohort.
KW - Artificial intelligence
KW - Implantable cardioverter-defibrillator
KW - Machine learning
KW - Ventricular arrhythmia
UR - http://www.scopus.com/inward/record.url?scp=85172424153&partnerID=8YFLogxK
U2 - https://doi.org/10.1093/europace/euad271
DO - https://doi.org/10.1093/europace/euad271
M3 - Article
C2 - 37712675
SN - 1099-5129
VL - 25
JO - Europace
JF - Europace
IS - 9
ER -