Machine learning-based prediction of clinical outcomes and left ventricular ejection fraction improvement after percutaneous coronary intervention in patients with reduced ejection fraction.
- 1Department of Medicine B, Rabin Medical Center, Beilinson Campus, Petah-Tikva, and Gray Faculty of Medicine, Tel Aviv University, 39 Jabotinsky Street, Petah Tikva 49100, and 30 Haim Levanon Street, Tel Aviv 6997801, Israel.
- 2Department of Cardiology, Rabin Medical Center, Beilinson Campus, Petah-Tikva, and Gray Faculty of Medicine, Tel Aviv University, 39 Jabotinsky Street, Petah Tikva 49100, and 30 Haim Levanon Street, Tel Aviv 6997801, Israel.
- 3Beilinson Medical Center Innovation, Artificial Intelligence Center, Rabin Medical Center, Petah-Tikva, Israel.
Abstract
Aims
The expected clinical and functional trajectory after percutaneous coronary intervention (PCI) in patients with left ventricular dysfunction remains uncertain. Recent trials have highlighted substantial heterogeneity in both clinical outcomes and left ventricular ejection fraction (LVEF) improvement, underscoring the need for improved post-PCI prognostic risk stratification in this high-risk population. This study aimed to develop machine learning models predicting clinical outcomes and LVEF improvement after PCI in patients with reduced LVEF.Methods and results
This retrospective single-centre study included adults undergoing PCI between 2004 and 2025 with baseline LVEF <45%. Two CatBoost models were developed using 53 routinely available pre- and intra-procedural predictors. The primary endpoint was a 1-year composite of death, myocardial infarction, target vessel revascularization, or coronary artery bypass grafting. The secondary endpoint was an absolute LVEF increase of ≥10 percentage points. Performance was evaluated on a held-out test set using area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (PR-AUC), and enrichment analysis. The dataset included 3336 procedures from 2793 patients (median LVEF 35%). For the primary endpoint (n = 2152; event rate 37.5%), the model achieved an AUC of 0.78 (95% CI: 0.73-0.83) and a PR-AUC of 0.70 (95% CI: 0.63-0.77), with event rates of 83.7% in the highest-risk decile and 9.3% in the lowest-risk decile. For LVEF improvement (n = 2551; 34.7% improved), performance was moderate (AUC 0.73), with enrichment to 76.9% in the highest-probability decile.Conclusion
Machine learning models using routinely available patient and procedural data enabled meaningful prognostic stratification in patients with left ventricular dysfunction undergoing PCI, supporting individualized estimation of clinical and functional trajectories after revascularization.
Related
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- Deep dynamic coronary roadmap guidance in percutaneous coronary intervention: a deep learning approach
- Definition and prediction of periprocedural myocardial infarction in elective percutaneous coronary intervention: the ALPHEUS-MI study
- A machine learning model for predicting ischemic and bleeding risk after percutaneous coronary intervention: Development and external validation
- Interpretable machine learning analysis of immune-inflammatory biomarkers for predicting no-reflow in patients with acute myocardial infarction after percutaneous coronary intervention.
BibTeX
@article{steinbuch2026machine,
title = {{Machine learning-based prediction of clinical outcomes and left ventricular ejection fraction improvement after percutaneous coronary intervention in patients with reduced ejection fraction.}},
author = {Lioz Steinbuch and Edward Itelman and Yuval Altman and Bar Yacobi and Osnat Goren-Peyser and Tamir Bental and Yael Shostak and Pablo Codner and Chen Gurevitz and Amos Levi and Guy Witberg and Yeela Talmor Barkan and Ran Kornowski and Leor Perl},
journal = {European heart journal. Digital health},
year = {2026},
month = oct,
doi = {10.1093/ehjdh/ztag156},
url = {https://europepmc.org/article/MED/42948636},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning-based prediction of clinical outcomes and left ventricular ejection fraction improvement after percutaneous coronary intervention in patients with reduced ejection fraction.
AU - Lioz Steinbuch
AU - Edward Itelman
AU - Yuval Altman
AU - Bar Yacobi
AU - Osnat Goren-Peyser
AU - Tamir Bental
AU - Yael Shostak
AU - Pablo Codner
AU - Chen Gurevitz
AU - Amos Levi
AU - Guy Witberg
AU - Yeela Talmor Barkan
AU - Ran Kornowski
AU - Leor Perl
PY - 2026
DA - 2026/10/27
JO - European heart journal. Digital health
DO - 10.1093/ehjdh/ztag156
UR - https://europepmc.org/article/MED/42948636
AB - Aims The expected clinical and functional trajectory after percutaneous coronary intervention (PCI) in patients with left ventricular dysfunction remains uncertain. Recent trials have highlighted substantial heterogeneity in both clinical outcomes and left ventricular ejection fraction (LVEF) improvement, underscoring the need for improved post-PCI prognostic risk stratification in this high-risk population. This study aimed to develop machine learning models predicting clinical outcomes and LVEF improvement after PCI in patients with reduced LVEF.Methods and results This retrospective single-centre study included adults undergoing PCI between 2004 and 2025 with baseline LVEF <45%. Two CatBoost models were developed using 53 routinely available pre- and intra-procedural predictors. The primary endpoint was a 1-year composite of death, myocardial infarction, target vessel revascularization, or coronary artery bypass grafting. The secondary endpoint was an absolute LVEF increase of ≥10 percentage points. Performance was evaluated on a held-out test set using area under the receiver operating characteristic curve (AUC), area under the precision-recall curve (PR-AUC), and enrichment analysis. The dataset included 3336 procedures from 2793 patients (median LVEF 35%). For the primary endpoint (n = 2152; event rate 37.5%), the model achieved an AUC of 0.78 (95% CI: 0.73-0.83) and a PR-AUC of 0.70 (95% CI: 0.63-0.77), with event rates of 83.7% in the highest-risk decile and 9.3% in the lowest-risk decile. For LVEF improvement (n = 2551; 34.7% improved), performance was moderate (AUC 0.73), with enrichment to 76.9% in the highest-probability decile.Conclusion Machine learning models using routinely available patient and procedural data enabled meaningful prognostic stratification in patients with left ventricular dysfunction undergoing PCI, supporting individualized estimation of clinical and functional trajectories after revascularization.
SN - 2634-3916
ER -