Left bundle branch block (LBBB) is associated with specific septal-to-lateral wall activation patterns which are strongly influenced by the intrinsic left ventricular (LV) contractility and myocardial scar localization. The objective of this study was to propose a computational-model-based interpretation of the different patterns of LV contraction observed in the case of LBBB and preserved contractility or myocardial scarring. Two-dimensional transthoracic echocardiography was used to obtain LV volumes and deformation patterns in three patients with LBBB: (1) a patient with non-ischemic dilated cardiomyopathy, (2) a patient with antero-septal myocardial scar, and (3) a patient with lateral myocardial scar. Scar was confirmed by the distribution of late gadolinium enhancement with cardiac magnetic resonance imaging (cMRI). Model parameters were evaluated manually to reproduce patient-derived data such as strain curves obtained from echocardiographic apical views. The model was able to reproduce the specific strain patterns observed in patients. A typical septal flash with pre-ejection shortening, rebound stretch, and delayed lateral wall activation was observed in the case of non-ischemic cardiomyopathy. In the case of lateral scar, the contractility of the lateral wall was significantly impaired and septal flash was absent. In the case of septal scar, septal flash and rebound stretch were also present as previously described in the literature. Interestingly, the model was also able to simulate the specific contractile properties of the myocardium, providing an excellent localization of LV scar in ischemic patients. The model was able to simulate the electromechanical delay and specific contractility patterns observed in patients with LBBB of ischemic and non-ischemic etiology. With further improvement and validation, this technique might be a useful tool for the diagnosis and treatment planning of heart failure patients needing CRT.

Introduction: : The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a novel hybrid approach, integrating machine-learning and personalized models, to identify explainable phenogroups of HF patients and predict their CRT response. Methods: The paper proposes the creation of a complete personalized model population based on preoperative CRT patient strain curves. Based on the parameters and features extracted from these personalized models, phenotypes of patients are identified thanks to a clustering algorithm and a random forest classification is provided. Results: A close match was observed between the 162 experimental and simulated myocardial strain curves, with a mean RMSE of 4.48% ($\pm$1.08) for the 162 patients. Five phenogroups of personalized models were identified from the clustering, with response rates ranging from 52% to 94%. The classification results show a mean area under the curves (AUC) of 0.86 ± 0.06 and provided a feature importance analysis with 22 features selected. Results show both regional myocardial contractility (from 22.5% to 33.0%), tissue viability and electrical activation delays importance on CRT response for each HF patient (from 55.8 ms to 88.4 ms). Discussion: The patient-specific model parameters’ analysis provides an explainable interpretation of HF patient phenogroups in relation to physiological mechanisms that seem predictive of the CRT response. These novel combined approaches appear as promising tools to improve understanding of LV mechanical dyssynchrony for HF patient characterization and CRT selection.

Goal: Despite the high incidence of left ventricular hypertrophy (LVH), clinical LVH-electrocardiography (ECG) criteria remain unsatisfactory due to low sensitivity. We propose an automatic LVH detection method based on ECG-extracted features and machine learning. Methods: ECG features were automatically extracted from two publicly available databases: PTB-XL with 2181 LVH and 9001 controls, and Georgia with 1012 LVH and 1387 controls. After preprocessing and feature extraction, the most relevant features from PTB-XL were selected to train three models: logistic regression, random forest (RF), and support vector machine (SVM). These classifiers, trained with selected features and a reduced set of five features, were evaluated on the Georgia database and compared with clinical LVH-ECG criteria. Results: RF and SVM models showed accuracies above 90% and increased sensitivity to above 86%, compared to clinical criteria achieving 38% at maximum. Conclusions: Automatic ECG-based LVH detection using machine learning outperforms conventional diagnostic criteria, benefiting clinical practice.

Hypertrophic cardiomyopathy (HCM) produces heterogeneous electrical phenotypes whose continuous organization, longitudinal evolution and clinical relevance remain poorly defined. We developed complementary variational-autoencoder representations to map HCM ECG morphology relative to physiological variation and characterize within-subject change. Using 5,194 normal-reference ECGs and 2,594 longitudinal visits from 650 individuals with HCM, we found that HCM overlapped the physiological manifold but extended towards its periphery, while an HCM-oriented manifold largely reorganized shared electrical modes. Mixed-effects models identified longitudinal drift in selected phenotypic axes. A lateral/frontal QRS-force coordinate remained time-dependent after censoring septal reduction therapy, atrial fibrillation and pacing, and lower values were associated with subsequent heart-failure risk in baseline and time-updated analyses. A time-conditioned change VAE identified four interpretable within-subject modes aligned with longer-term trajectories. Sarcomeric genotype was associated with phenotype position but not trajectory rate.

The development of atrial fibrillation (AF) during hospitalization for acute myocardial infarction (AMI) is common and associated with worse prognosis. We aimed to train and, for the first time, cross-nationally validate a machine learning model to predict AF developing during the cardiac intensive care unit (CICU) stay in post-AMI patients admitted in sinus rhythm, using only routine, non-electrocardiographic clinical parameters available at CICU admission. We developed and tested classification pipelines on two independent cohorts of post-AMI patients from Italy (ITA, Sinus Rhythm = 2,200, AF = 240) and Finland (FIN, Sinus Rhythm = 3,631, AF = 452). Models were first evaluated via nested cross-validation on held out samples of the training cohort (internal testing) and then cross-cohort (external testing). An L2-regularized logistic regression model performed optimally in each case, achieving comparable performances in internal (AUROC: 0.740 ITA, 0.755 FIN) and external (AUROC: 0.735 ITA-on-FIN, 0.723 FIN-on-ITA) testing. A combined-cohort model achieved an AUROC of 0.749. A posteriori feature relevance analysis confirmed the predictive value of established risk factors including advanced age, lower left ventricular ejection fraction and hypertension in both cohorts. Ongoing aspirin therapy and non-ST-elevation myocardial infarction emerged as additional protective factors exclusive to cohort ITA, whereas a higher high-sensitivity C-reactive protein (risk factor) and the current smoking status (protective factor) were specific to cohort FIN. Our study demonstrates that machine learning models using routinely collected clinical parameters could help predict in-hospital AF in post-AMI patients across different populations and national healthcare systems. Notably, a simple and fully interpretable logistic regression model achieved robust performance in the first cross-national validation for this prediction task, suggesting that more complex, black-box approaches may offer limited additional benefit for this specific task. Further improvements are needed to reach real-world application in clinical contexts for early risk stratification.