Identifying predictive risk factors for permanent pacemaker implantation up to 30 days post-TAVI

Abstract Background Conduction system abnormalities, including AV block, are amongst the most common complications of transcatheter aortic valve implantation (TAVI). Post-TAVI high degree AV block necessitates permanent pacemaker (PPM) implantation. Purpose To assess the ability of standardly available pre-, intra- and post-TAVI factors to predict PPM implantation within 30-days post procedure. Methods Demographic and clinical (pre-, intra-, and post-procedural) data including ECG parameters were collected from all patients who underwent TAVI at our centre from August 2017 to November 2020. Patients with pre-existing PPM were excluded from the study. Predictive factors were selected through univariate analysis, and selected characteristics were incorporated into a multivariate binomial logistic regression model, in order to create a 30-day PPM risk-prediction model. The Akaike information criterion (AIC) and area under receiver operating curve (AUC/C-statistic) were used to assess discriminative performance. Results In total, data from a total of 446 patients were analysed. Of these, 40 (8.97%) received PPM implantation within 30 days of the procedure. The mean age of the patients was 81.5 (±7.3 SD) years; 99 (22.2%) had pre-existing first degree AV block, 55 (12.3%) had pre-existing left bundle branch block (LBBB) and 50 (11.2%) had pre-existing right bundle branch block (RBBB). Intra-procedurally 40 (9.0%) developed LBBB, 21 (4.7%) developed 3rd degree AV block, and 95 (21.3%) patients required temporary pacing wire (TPW) pacing. Post-procedurally, 138 (30.9%) exhibited AV block, 107 (24.0%) LBBB and 50 (11.2%) RBBB. The following factors met significance at multivariate logistic regression analysis: pre-TAVI RBBB (OR 6.62 [95% CI, 1.37–36.51]), intra-TAVI 3rd degree AV block (OR 12.80 [95% CI, 3.44–53.34]), intra-TAVI LBBB (OR 4.02 [95% CI, 1.28–12.53]), use of TPW pacing (OR 8.58 [95% CI, 3.19–25.12]) and post-TAVI LBBB (OR 7.84 [95% CI, 2.75–24.46]) (Table). Finally, variables were incorporated into a multivariate logistic regression model with the outcome variable of 30-day PPM implantation (Figure 1a). A model incorporating five factors (pre-TAVI RBBB, intra-TAVI 3rd degree AV block, intra-TAVI LBBB, use of TPW pacing and post-TAVI LBBB) demonstrated excellent discriminative ability (accuracy 0.925 and an AUC of 0.952) at predicting PPM implantation (Figure 1b). Conclusions Following variable selection, the best performing model incorporated five fa