PDF(Pubmed)
Abstract：
The efficacy of an implantable cardioverter-defibrillator (ICD) in patients with a non-ischaemic cardiomyopathy for primary prevention of sudden cardiac death is increasingly debated. We developed a multimodal deep learning model for arrhythmic risk prediction that integrated late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI), electrocardiography (ECG) and clinical data. Short-axis LGE-MRI scans and 12-lead ECGs were retrospectively collected from a cohort of 289 patients prior to ICD implantation, across two tertiary hospitals. A residual variational autoencoder was developed to extract physiological features from LGE-MRI and ECG, and used as inputs for a machine learning model (DEEP RISK) to predict malignant ventricular arrhythmia onset. In the validation cohort, the multimodal DEEP RISK model predicted malignant ventricular arrhythmias with an area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval (CI) 0.71-0.96), a sensitivity of 0.98 (95% CI 0.75-1.00) and a specificity of 0.73 (95% CI 0.58-0.97). The models trained on individual modalities exhibited lower AUROC values compared to DEEP RISK [MRI branch: 0.80 (95% CI 0.65-0.94), ECG branch: 0.54 (95% CI 0.26-0.82), Clinical branch: 0.64 (95% CI 0.39-0.87)]. These results suggest that a multimodal model achieves high prognostic accuracy in predicting ventricular arrhythmias in a cohort of patients with non-ischaemic systolic heart failure, using data collected prior to ICD implantation.
摘要：
植入式心脏复律除颤器（ICD）对非缺血性心肌病患者的一级预防心源性猝死的疗效越来越多。我们开发了一种用于心律失常风险预测的多模式深度学习模型，该模型集成了晚期钆增强（LGE）心脏磁共振成像（MRI），心电图（ECG）和临床资料。在ICD植入前，回顾性收集了289例患者的短轴LGE-MRI扫描和12导联心电图。横跨两家三级医院。开发了一种残差变分自动编码器，用于从LGE-MRI和ECG中提取生理特征，并用作机器学习模型（DEEPRISK）的输入，以预测恶性室性心律失常的发作。在验证队列中，多模式DEEPRISK模型预测恶性室性心律失常，受试者工作特征曲线下面积（AUROC）为0.84（95％置信区间（CI）0.71-0.96），敏感性为0.98（95%CI0.75-1.00），特异性为0.73（95%CI0.58-0.97）。与深度风险[MRI分支：0.80（95％CI0.65-0.94）相比，在单个模式上训练的模型显示出更低的AUROC值，心电图分支：0.54（95%CI0.26-0.82），临床分支：0.64（95%CI0.39-0.87）]。这些结果表明，在非缺血性收缩性心力衰竭患者队列中，多模式模型在预测室性心律失常方面具有很高的预后准确性。使用ICD植入前收集的数据。
