Personalized planning of cardiac resynchronization therapy through integration of coronary sinus geometry, clinical data, digital twins, and machine learning: visualization, stratification, and optimization

Background: Cardiac resynchronization therapy (CRT) fails in 30% of patients, often due to suboptimal left ventricular pacing site (LVPS) selection. Current practice lacks tools for pre-procedural, patient-specific LVPS optimization within the accessible coronary sinus (CS) tributaries. This study aimed to develop a digital twin and an explainable ML-based clinical decision support framework to address this issue. Methods: Personalized 3D cardiac models incorporating ventricular anatomy, myocardial fibrosis, and CS anatomy were constructed from CT and LGE-MRI for 74 CRT candidates. Finite-element Eikonal simulations of biventricular pacing generated patient-specific electrophysiological features at candidate LVPS. A Machine Learning (ML) classifier was trained on a hybrid feature set of pre-procedural clinical variables and model-derived indices, validated by leave-one-out cross-validation. SHAP analysis provided a physiologically interpretable rationale for each prediction. The framework was applied to a pilot cohort of 19 patients with reconstructed 3D CS anatomy to generate a spatial likelihood map of CRT response across all clinically implantable pacing sites within each patient's CS. Results: The ML classifier outperformed the reference Feeny clinical calculator under LOO-CV (accuracy 0.78 vs 0.58; F1-score 0.75 vs 0.43), AUC=0.78, sensitivity=0.80, specificity=0.77. Bootstrap analysis yielded mean AUC=0.85 (95% CI 0.70-0.95). In the pilot CS cohort, the framework identified that 8 of 13 clinical non-responders had no accessible CS site predicted to yield a positive response, supporting redirection towards alternative pacing strategies. In the remaining 5, alternative implantable sites with high predicted response probability were identified. SHAP analysis confirmed that dominant predictors were patient-specific in their relative contributions, supporting individualized over heuristic-based LVPS selection. Conclusion: This pilot study demonstrates the feasibility of a digital twin and explainable ML framework as a pre-procedural clinical decision support tool for CRT planning, stratifying patients and identifying optimal implantable sites with transparent anatomical rationale. Prospective validation and regulatory evaluation are required before clinical deployment.