Wu X; Harbin Medical University, Heilongjiang, China.
Cui H; Wang S; Zheng X; et al

Clinical & Experimental Medicine. 26(1), 2026 Jun 22.

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.