Department of Cardiovascular Medicine, University of Tokyo, Tokyo, Japan.
Department of Planning, Information and Management, University of Tokyo, Tokyo, Japan.
PLoS One. 2024 Jun 18;19(6):e0304423. doi: 10.1371/journal.pone.0304423. eCollection 2024.
Ischemic heart disease is a leading cause of death worldwide, and its importance is increasing with the aging population. The aim of this study was to evaluate the accuracy of SurvTrace, a survival analysis model using the Transformer-a state-of-the-art deep learning method-for predicting recurrent cardiovascular events and stratifying high-risk patients. The model's performance was compared to that of a conventional scoring system utilizing real-world data from cardiovascular patients.
This study consecutively enrolled patients who underwent percutaneous coronary intervention (PCI) at the Department of Cardiovascular Medicine, University of Tokyo Hospital, between 2005 and 2019. Each patient's initial PCI at our hospital was designated as the index procedure, and a composite of major adverse cardiovascular events (MACE) was monitored for up to two years post-index event. Data regarding patient background, clinical presentation, medical history, medications, and perioperative complications were collected to predict MACE. The performance of two models-a conventional scoring system proposed by Wilson et al. and the Transformer-based model SurvTrace-was evaluated using Harrell's c-index, Kaplan-Meier curves, and log-rank tests.
A total of 3938 cases were included in the study, with 394 used as the test dataset and the remaining 3544 used for model training. SurvTrace exhibited a mean c-index of 0.72 (95% confidence intervals (CI): 0.69-0.76), which indicated higher prognostic accuracy compared with the conventional scoring system's 0.64 (95% CI: 0.64-0.64). Moreover, SurvTrace demonstrated superior risk stratification ability, effectively distinguishing between the high-risk group and other risk categories in terms of event occurrence. In contrast, the conventional system only showed a significant difference between the low-risk and high-risk groups.
This study based on real-world cardiovascular patient data underscores the potential of the Transformer-based survival analysis model, SurvTrace, for predicting recurrent cardiovascular events and stratifying high-risk patients.
缺血性心脏病是全球范围内主要的死亡原因，随着人口老龄化，其重要性日益增加。本研究旨在评估 SurvTrace 的准确性，这是一种使用 Transformer（一种最先进的深度学习方法）的生存分析模型，用于预测复发性心血管事件并对高危患者进行分层。该模型的性能与利用心血管患者的真实世界数据的传统评分系统进行了比较。
本研究连续纳入了 2005 年至 2019 年在东京大学医院心血管医学系接受经皮冠状动脉介入治疗（PCI）的患者。每位患者在我院的首次 PCI 被指定为索引手术，并监测复合主要不良心血管事件（MACE），最长达索引事件后两年。收集了患者背景、临床表现、病史、药物治疗和围手术期并发症的数据，以预测 MACE。使用 Harrell 的 c 指数、Kaplan-Meier 曲线和对数秩检验评估了两种模型——Wilson 等人提出的传统评分系统和基于 Transformer 的模型 SurvTrace——的性能。
该研究共纳入 3938 例患者，其中 394 例用于测试数据集，其余 3544 例用于模型训练。SurvTrace 的平均 c 指数为 0.72（95%置信区间（CI）：0.69-0.76），这表明其预后准确性高于传统评分系统的 0.64（95% CI：0.64-0.64）。此外，SurvTrace 表现出更好的风险分层能力，能够有效地将高危组与其他风险类别区分开来，从而预测事件的发生。相比之下，传统系统仅在低危组和高危组之间显示出显著差异。
本研究基于真实世界的心血管患者数据，强调了基于 Transformer 的生存分析模型 SurvTrace 预测复发性心血管事件和对高危患者进行分层的潜力。
