Predicting permanent pacemaker implantation after transcatheter aortic valve replacement in self-expandable cohorts

Background Permanent Pacemaker Implantation (PPI) is a common complication following transcatheter aortic valve replacement (TAVR). Identifying predictors, particularly immune-inflammatory markers, can enhance pre-procedural risk stratification. Methods In this retrospective cohort study, we analyzed 587 patients undergoing TAVR with self-expandable valves. We assessed clinical, anatomical, and inflammatory parameters, focusing on five complete blood count-derived ratios: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), and eosinophil-to-lymphocyte ratio (ELR). Multivariable logistic regression models were developed to identify predictors of PPI. Receiver operating characteristic (ROC) curves evaluated the discriminative ability of these markers. Results In our cohort of 587 patients undergoing TAVR with self-expandable valves, multivariable logistic regression identified several significant predictors for PPI. Elevated pre-procedural NLR was associated with increased PPI risk (odds ratio [OR] 1.18; p = 0.008), as was the SII (OR 1.0012; p = 0.001). Anatomical and procedural factors also contributed: septum thickness (OR 1.24; p = 0.025), valve size (OR 1.10; p = 0.025), and longer procedure time (OR 1.013; p = 0.003). Post-dilatation was associated with reduced PPI risk (OR 0.54; p = 0.031). Electrical conduction parameters, including QRS duration (OR 1.015; p = 0.018) and presence of right bundle branch block (RBBB) (OR 4.64; p = 0.027), were significant predictors. The model demonstrated strong discriminative ability with an area under the curve (AUC) of 0.8628, indicating high accuracy in distinguishing patients at risk for PPI post-TAVR. Conclusions We developed a predictive equation combining inflammatory, anatomical, and procedural factors to estimate PPI risk after TAVR. This tool demonstrated high accuracy and clinical utility, supporting personalized risk assessment, procedural planning, and improved patient outcomes in TAVR populations.