Improved patient selection for primary prevention ICD implantation by predicting ICD non-benefit using artificial intelligence

Abstract

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NWO Rubicon (452019308) Amsterdam Cardiovascular Sciences Background Left Ventricular Ejection Fraction (LVEF) is suboptimal as a sole marker for predicting sudden cardiac death (SCD) or benefit from an ICD. Machine (ML) and deep (DL) learning provide new opportunities for personalised predictions using complex, multi-modal physiological data. Objective We hypothesise that risk stratification for ICD implantation can be improved by ML and DL models that combine clinical variables with time series features from 12-lead electrocardiograms (ECG). Methods We present a multicentre study of 1010 patients with an ischaemic, dilated or non-ischaemic cardiomyopathy and LVEF≤35% implanted with an ICD between 2007 and 2021 for primary prevention of SCD (64.9 ±10.8 years, 73.2% male) in two academic hospitals. For each patient, raw 12-lead, 10-second ECG-recordings obtained <90 days before ICD implantation and clinical details were collected. Supervised ML and DL models were trained and validated using stratified k-fold cross-validation (5 repeats, k=10) on a development cohort (n=550) from Hospital A. We used this model to predict ICD non-benefit defined as mortality without prior appropriate ICD-therapy on an external dataset from Hospital B (n=460). Kaplan-Meier survival analysis stratified by high vs. low predicted probability using Youden's J statistic as cut-off was performed. Results At 3-year follow-up, 16.0% of patients had died of whom 72.8% met the criteria for ICD non-benefit. Extreme gradient boosting models identified subjects with ICD non-benefit with an area under the receiver operator curve (AUROC) of 0.897 ±0.05 during internal validation (Figure 1, solid line). In the external cohort, the AUROC was 0.793 (95% CI 0.75-0.84) (Figure 1, dashed line). Survival analysis for low vs. high predicted risk indicated ICD non-benefit rates of 6.0% versus 30.3% at 3-year follow-up, respectively (Figure 2). Conclusion A ML model that combined clinical with ECG features better predicted ICD non-benefit at 3-years in a primary prevention population than currently available risk scores such as the MADIT-ICD score. This approach may provide new tools to support personalized decision making for ICD therapy. Prospective validation is needed to assess the real-world clinical performance of the model.