Scholarlink

<p>Heart failure after acute myocardial infarction (post-MI HF) has become a major global health problem. Accurate risk prediction is essential for optimising management and preventing post-MI HF. However, existing models rely mainly on resting-state clinical examinations and inadequately reflect the complex pathophysiology of post-MI HF. We aimed to develop and validate a multimodal machine learning (ML) model incorporating cardiopulmonary exercise testing (CPET) data to predict post-MI HF risk and to quantify CPET’s incremental value.This study included 3172 acute myocardial infarction (AMI) patients who underwent CPET at three hospitals from 2018 to 2023. The primary outcome was post-MI HF within 1 year. Thirteen ML algorithms were used to select clinical and CPET variables and to construct multimodal prediction models. The incremental predictive value of CPET was evaluated by the area under the curve (AUC), integrated discrimination improvement index (IDI), and net reclassification improvement index (NRI).After screening, 2221 patients were included, of whom 221 (10.0%) developed post-MI HF. The optimal multimodal ML model achieved an AUC of 0.987 (95% CI: 0.982–0.992) in training set and 0.929 (95% CI: 0.903–0.955) in external validation set. Ablation analyses showed that CPET significantly improved discrimination (AUC: 0.890 vs. 0.929, <i>P</i>=0.003), calibration (IDI=0.135 [95% CI: 0.082–0.189], <i>P</i>&lt;0.001), and reclassification (NRI=0.154 [95% CI: 0.073–0.234], <i>P</i>&lt;0.001). The model effectively stratified low- and high-risk patients (3.1% vs. 54.7%, <i>P</i>&lt;0.001). The multimodal ML model accurately predicted post-MI HF and highlighted the additive value of CPET in risk stratification. The web-based risk calculator derived from this model may support early identification of high-risk patients and facilitate personalised management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and validation of a multimodal machine learning prediction model for heart failure after acute myocardial infarction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月22日

查看全文
  • Xiaojun Wu,
  • Haoning Cui,
  • Shiyu Wang,
  • Xianghui Zheng,
  • Xinyu Hou,
  • Liyan Ran,
  • Jiaqi Liu,
  • Zhuozhong Wang,
  • Meiqiao Ren,
  • Jing Yao,
  • Huan Ma,
  • Yang Zheng,
  • Jian Wu,
  • Bo Yu

摘要

Heart failure after acute myocardial infarction (post-MI HF) has become a major global health problem. Accurate risk prediction is essential for optimising management and preventing post-MI HF. However, existing models rely mainly on resting-state clinical examinations and inadequately reflect the complex pathophysiology of post-MI HF. We aimed to develop and validate a multimodal machine learning (ML) model incorporating cardiopulmonary exercise testing (CPET) data to predict post-MI HF risk and to quantify CPET’s incremental value.This study included 3172 acute myocardial infarction (AMI) patients who underwent CPET at three hospitals from 2018 to 2023. The primary outcome was post-MI HF within 1 year. Thirteen ML algorithms were used to select clinical and CPET variables and to construct multimodal prediction models. The incremental predictive value of CPET was evaluated by the area under the curve (AUC), integrated discrimination improvement index (IDI), and net reclassification improvement index (NRI).After screening, 2221 patients were included, of whom 221 (10.0%) developed post-MI HF. The optimal multimodal ML model achieved an AUC of 0.987 (95% CI: 0.982–0.992) in training set and 0.929 (95% CI: 0.903–0.955) in external validation set. Ablation analyses showed that CPET significantly improved discrimination (AUC: 0.890 vs. 0.929, P=0.003), calibration (IDI=0.135 [95% CI: 0.082–0.189], P<0.001), and reclassification (NRI=0.154 [95% CI: 0.073–0.234], P<0.001). The model effectively stratified low- and high-risk patients (3.1% vs. 54.7%, P<0.001). The multimodal ML model accurately predicted post-MI HF and highlighted the additive value of CPET in risk stratification. The web-based risk calculator derived from this model may support early identification of high-risk patients and facilitate personalised management.