Sex-specific differences in outcome and risk stratification of ventricular arrhythmias in implantable cardioverter defibrillator patients

Aims: Risk stratification models of sudden cardiac death (SCD) are based on the assumption that risk factors of SCD affect risk to a similar extent in both sexes. The aim of the study is to evaluate differences in clinical outcomes between sexes and evaluate whether risk factors associated with appropriate device therapy (ADT) differ between men and women. Methods and results: We performed a cohort study of implantable cardioverter defibrillator (ICD) patients referred for primary or secondary prevention of SCD between 2009 and 2018. Multivariable Cox regression models for prediction of ADT were constructed for men and women separately. Of 2300 included patients, 571 (25%) were women. Median follow-up was 4.6 (inter-quartile range: 4.4–4.9) years. Time to ADT was shorter for men compared with women [hazard ratio (HR) 1.71, P < 0.001], as was time to mortality (HR 1.37, P = 0.003). In women, only secondary prevention ICD therapy (HR 1.82, P < 0.01) was associated with ADT, whereas higher age (HR 1.20, P < 0.001), absence of left bundle branch block (HR 0.72, P = 0.01), and secondary prevention therapy (HR 1.80, P < 0.001) were independently associated with ADT in men. None of the observed parameters showed a distinctive sex-specific pattern in ADT. Conclusions: Male ICD patients were at higher risk of ADT and death compared with female ICD patients, irrespective of an ischaemic or non-ischaemic underlying cardiomyopathy. Our study highlights the importance to stratify outcomes of ICD trials by sex, as study results differ between men and women. However, none of the available clinical parameters showed a clear sex-specific relation to ventricular arrhythmias. As a consequence, sex-specific risk stratification models of SCD using commonly available clinical parameters could not be derived

Sex-specific differences in outcome and risk stratification of ventricular arrhythmias in implantable cardioverter defibrillator patients

Aims: Risk stratification models of sudden cardiac death (SCD) are based on the assumption that risk factors of SCD affect risk to a similar extent in both sexes. The aim of the study is to evaluate differences in clinical outcomes between sexes and evaluate whether risk factors associated with appropriate device therapy (ADT) differ between men and women. Methods and results: We performed a cohort study of implantable cardioverter defibrillator (ICD) patients referred for primary or secondary prevention of SCD between 2009 and 2018. Multivariable Cox regression models for prediction of ADT were constructed for men and women separately. Of 2300 included patients, 571 (25%) were women. Median follow-up was 4.6 (inter-quartile range: 4.4–4.9) years. Time to ADT was shorter for men compared with women [hazard ratio (HR) 1.71, P &lt; 0.001], as was time to mortality (HR 1.37, P = 0.003). In women, only secondary prevention ICD therapy (HR 1.82, P &lt; 0.01) was associated with ADT, whereas higher age (HR 1.20, P &lt; 0.001), absence of left bundle branch block (HR 0.72, P = 0.01), and secondary prevention therapy (HR 1.80, P &lt; 0.001) were independently associated with ADT in men. None of the observed parameters showed a distinctive sex-specific pattern in ADT. Conclusions: Male ICD patients were at higher risk of ADT and death compared with female ICD patients, irrespective of an ischaemic or non-ischaemic underlying cardiomyopathy. Our study highlights the importance to stratify outcomes of ICD trials by sex, as study results differ between men and women. However, none of the available clinical parameters showed a clear sex-specific relation to ventricular arrhythmias. As a consequence, sex-specific risk stratification models of SCD using commonly available clinical parameters could not be derived.</p

Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillatorResearch in context

Summary: Background: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. Methods: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. Findings: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. Interpretation: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. Funding: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T)

Optimizing patient selection for primary prevention implantable cardioverter-defibrillator implantation: utilizing multimodal machine learning to assess risk of implantable cardioverter-defibrillator non-benefit

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.ISSN:1099-5129ISSN:1532-209