Machine learning for pacemaker implantation prediction after TAVI using multimodal imaging data.

ABSTRACT: Pacemaker implantation (PMI) after transcatheter aortic valve implantation (TAVI) is a common complication. While computed tomography (CT) scan data are known predictors of PMI, no machine learning (ML) model integrating CT with clinical, ECG, and transthoracic echocardiography (TTE) data has been proposed. This study investigates the contribution of ML methods to predict PMI after TAVI, with a focus on the role of CT imaging data. A retrospective analysis was conducted on a cohort of 520 patients who underwent TAVI. Recursive feature elimination with SHAP values was used to select key variables from clinical, ECG, TTE, and CT data. Six ML models, including Support Vector Machines (SVM), were trained using these selected variables. The model's performance was evaluated using AUC-ROC, F1 sc

SUBMITTER: El Ouahidi A 

PROVIDER: S-EPMC11500093 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature