Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No.2, Anzhen Road, Chaoyang District, Beijing, 100029, China.
School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China.
Eur Radiol. 2023 Nov;33(11):8203-8213. doi: 10.1007/s00330-023-09785-9. Epub 2023 Jun 7.
To evaluate the performance of a deep learning-based multi-source model for survival prediction and risk stratification in patients with heart failure.
Patients with heart failure with reduced ejection fraction (HFrEF) who underwent cardiac magnetic resonance between January 2015 and April 2020 were retrospectively included in this study. Baseline electronic health record data, including clinical demographic information, laboratory data, and electrocardiographic information, were collected. Short-axis non-contrast cine images of the whole heart were acquired to estimate the cardiac function parameters and the motion features of the left ventricle. Model accuracy was evaluated using the Harrell's concordance index. All patients were followed up for major adverse cardiac events (MACEs), and survival prediction was assessed using Kaplan-Meier curves.
A total of 329 patients were evaluated (age 54 ± 14 years; men, 254) in this study. During a median follow-up period of 1041 days, 62 patients experienced MACEs and their median survival time was 495 days. When compared with conventional Cox hazard prediction models, deep learning models showed better survival prediction performance. Multi-data denoising autoencoder (DAE) model reached the concordance index of 0.8546 (95% CI: 0.7902-0.8883). Furthermore, when divided into phenogroups, the multi-data DAE model could significantly discriminate between the survival outcomes of the high-risk and low-risk groups compared with other models (p < 0.001).
The proposed deep learning (DL) model based on non-contrast cardiac cine magnetic resonance imaging could independently predict the outcome of patients with HFrEF and showed better prediction efficiency than conventional methods.
The proposed multi-source deep learning model based on cardiac magnetic resonance enables survival prediction in patients with heart failure.
• A multi-source deep learning model based on non-contrast cardiovascular magnetic resonance (CMR) cine images was built to make robust survival prediction in patients with heart failure. • The ground truth definition contains electronic health record data as well as DL-based motion data, and cardiac motion information is extracted by optical flow method from non-contrast CMR cine images. • The DL-based model exhibits better prognostic value and stratification performance when compared with conventional prediction models and could aid in the risk stratification in patients with HF.
评估基于深度学习的多源模型在心力衰竭患者生存预测和风险分层中的性能。
回顾性纳入 2015 年 1 月至 2020 年 4 月期间接受心脏磁共振成像的射血分数降低的心力衰竭（HFrEF）患者。收集基线电子健康记录数据，包括临床人口统计学信息、实验室数据和心电图信息。采集整个心脏的短轴对比增强电影图像，以估计心脏功能参数和左心室运动特征。使用 Harrell 的一致性指数评估模型准确性。所有患者均进行主要不良心脏事件（MACE）随访，并使用 Kaplan-Meier 曲线评估生存预测。
本研究共评估了 329 例患者（年龄 54±14 岁；男性 254 例）。在中位随访 1041 天期间，62 例患者发生 MACE，中位生存时间为 495 天。与传统 Cox 风险预测模型相比，深度学习模型具有更好的生存预测性能。多数据去噪自动编码器（DAE）模型达到了 0.8546 的一致性指数（95%CI：0.7902-0.8883）。此外，当按表型分组时，与其他模型相比，多数据 DAE 模型可以显著区分高危和低危组的生存结局（p<0.001）。
基于非对比心脏磁共振电影成像的深度学习（DL）模型可独立预测 HFrEF 患者的结局，且预测效率优于传统方法。
基于心脏磁共振的多源深度学习模型可用于预测心力衰竭患者的生存情况。
基于非对比心血管磁共振（CMR）电影图像的多源深度学习模型被构建以实现心力衰竭患者的稳健生存预测。
真实定义包含电子健康记录数据以及基于深度学习的运动数据，并且从非对比 CMR 电影图像中使用光流方法提取心脏运动信息。
与传统预测模型相比，基于深度学习的模型具有更好的预后价值和分层性能，有助于心力衰竭患者的风险分层。
