Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.
YE, Y., Y, A., S, T., H, İ., B, Ö., H, A., & A, İ. (2026). Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.. Journal of cardiovascular translational research. https://doi.org/10.1007/s12265-026-10804-5
YE Y, Y A, S T, H İ, B Ö, H A, et al. Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.. Journal of cardiovascular translational research. 2026; doi: 10.1007/s12265-026-10804-5
YE Y, Y A, S T, et al. Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.[J]. Journal of cardiovascular translational research. 2026. DOI: 10.1007/s12265-026-10804-5.
@article{ye2026,
author = {Yavuz YE and Alsancak Y and Tatar S and İncekara H and Özyeşi̇l B and Akilli H and İçli̇ A},
title = {Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.},
journal = {Journal of cardiovascular translational research},
year = {2026},
doi = {10.1007/s12265-026-10804-5},
note = {PMID: 42329547},
}
TY - JOUR AU - Yavuz YE AU - Alsancak Y AU - Tatar S AU - İncekara H AU - Özyeşi̇l B AU - Akilli H AU - İçli̇ A TI - Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes. T2 - Journal of cardiovascular translational research PY - 2026 DO - 10.1007/s12265-026-10804-5 AN - PMID:42329547 ER -
Left ventricular thrombus (LVT) in ischemic heart failure carries embolic risk; tools to anticipate persistence are limited. We studied 190 consecutive patients with imaging-confirmed LVT managed with guideline-concordant anticoagulation and serial echocardiography. The primary outcome was 6-month non-regression; 1-year MACE was secondary. We combined classical statistics with explainable machine learning. CatBoost yielded the best discrimination for non-regression (CV-AUC 0.76; test accuracy 0.79). SHAP highlighted left atrial diameter, pulmonary artery pressure, platelet count, and LV end-diastolic diameter as leading predictors. For 1-year outcomes, thrombus size and CHA2DS2-VA were independently associated with MACE (logistic AUC 0.71), whereas "regression vs persistence" alone was not. Baseline remodeling and coagulability markers, captured by an interpretable ML model, stratify early risk of LVT persistence and complement clinical decision-making for imaging follow-up and anticoagulation intensity.