A Prediction Model for Heart Failure with Improved Ejection Fraction Phenotype Based on Electrocardiogram Images and Clinical Baseline Features
- 1Dalian Medical University, Dalian, Liaoning, 116044, People’s Republic of China
- 2Department of Cardiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, 116001, People’s Republic of China
Abstract
Objective: To develop and externally evaluate machine learning models for predicting the heart failure with improved ejection fraction (HFimpEF) phenotype in patients with ischemic cardiomyopathy, using electrocardiogram (ECG) image features, all eligible clinical baseline features, and their combination.
Methods: This two-center retrospective study included patients hospitalized between March 2018 and February 2025 with baseline left ventricular ejection fraction (LVEF) ≤40%. HFimpEF was defined as an increase in LVEF of at least 10 percentage points and a follow-up LVEF >40%, with at least three months between echocardiograms. The development cohort comprised 415 patients (194 with HFimpEF and 221 with persistent HFrEF); an independent cohort of 100 patients (55 and 45, respectively) was used for external validation. Four classifiers were evaluated using ECG image features alone, all eligible clinical baseline features alone, and their combination. LASSO was used separately to rank clinical variables.
Results: In the reported internal evaluation, the combined random forest model achieved an AUC of 0.825, accuracy of 0.747, precision of 0.753, specificity of 0.791, and F1 score of 0.721. In external validation, the combined-model AUCs (95% confidence intervals) were 0.624 (0.513–0.734) for random forest, 0.607 (0.496–0.718) for logistic regression, 0.653 (0.545–0.761) for SVC-RBF, and 0.565 (0.454–0.676) for k-nearest neighbors. β-Blocker use ranked highest in the exploratory LASSO analysis. Combined models did not consistently outperform single-feature-set models externally.
Conclusion: Models combining ECG image features and clinical baseline features showed promising internal discrimination for identifying subsequent HFimpEF in patients with ischemic cardiomyopathy. However, external discrimination was modest, and the combined models did not consistently outperform single-source models. Further model refinement and validation in larger, independent cohorts are needed before clinical application.
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BibTeX
@article{wei2026prediction,
title = {{A Prediction Model for Heart Failure with Improved Ejection Fraction Phenotype Based on Electrocardiogram Images and Clinical Baseline Features}},
author = {Xianjing Wei and Xingping Xiao and Shulong Zhang and Chenchen Dong and Haihong Yan and Yuncong Ma and Qin Yu and Lu Liu},
journal = {International Journal of General Medicine},
year = {2026},
month = oct,
doi = {10.2147/ijgm.s626451},
url = {https://doi.org/10.2147/ijgm.s626451},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - A Prediction Model for Heart Failure with Improved Ejection Fraction Phenotype Based on Electrocardiogram Images and Clinical Baseline Features
AU - Xianjing Wei
AU - Xingping Xiao
AU - Shulong Zhang
AU - Chenchen Dong
AU - Haihong Yan
AU - Yuncong Ma
AU - Qin Yu
AU - Lu Liu
PY - 2026
DA - 2026/10/30
JO - International Journal of General Medicine
DO - 10.2147/ijgm.s626451
UR - https://doi.org/10.2147/ijgm.s626451
AB - Objective: To develop and externally evaluate machine learning models for predicting the heart failure with improved ejection fraction (HFimpEF) phenotype in patients with ischemic cardiomyopathy, using electrocardiogram (ECG) image features, all eligible clinical baseline features, and their combination. Methods: This two-center retrospective study included patients hospitalized between March 2018 and February 2025 with baseline left ventricular ejection fraction (LVEF) ≤40%. HFimpEF was defined as an increase in LVEF of at least 10 percentage points and a follow-up LVEF >40%, with at least three months between echocardiograms. The development cohort comprised 415 patients (194 with HFimpEF and 221 with persistent HFrEF); an independent cohort of 100 patients (55 and 45, respectively) was used for external validation. Four classifiers were evaluated using ECG image features alone, all eligible clinical baseline features alone, and their combination. LASSO was used separately to rank clinical variables. Results: In the reported internal evaluation, the combined random forest model achieved an AUC of 0.825, accuracy of 0.747, precision of 0.753, specificity of 0.791, and F1 score of 0.721. In external validation, the combined-model AUCs (95% confidence intervals) were 0.624 (0.513–0.734) for random forest, 0.607 (0.496–0.718) for logistic regression, 0.653 (0.545–0.761) for SVC-RBF, and 0.565 (0.454–0.676) for k-nearest neighbors. β-Blocker use ranked highest in the exploratory LASSO analysis. Combined models did not consistently outperform single-feature-set models externally. Conclusion: Models combining ECG image features and clinical baseline features showed promising internal discrimination for identifying subsequent HFimpEF in patients with ischemic cardiomyopathy. However, external discrimination was modest, and the combined models did not consistently outperform single-source models. Further model refinement and validation in larger, independent cohorts are needed before clinical application.
SN - 1178-7074
ER -