Prospective Observational Study on the Accuracy of Predictors of Permanent Pacemaker Secondary to High-Grade Atrioventricular Conduction Block After TAVI (CONDUCT-TAVI)

The incidence of permanent pacemaker implantation (PPMI) due to high-grade atrioventricular block after transcatheter aortic valve implantation (TAVI) is 10% to 15% at 1-year, and current prediction algorithms remain unreliable. CONDUCT-TAVI is a prospective observational study of 200 patients undergoing TAVI across 2 centers. Baseline demographic, anatomic and procedural characteristics were recorded, followed by targeted electrophysiology studies and continuous rhythm monitoring using implantable loop recorders for 1 year. The primary outcome was PPMI secondary to high-grade atrioventricular block, and secondary outcomes included early (≤48 hours) and late (>48 hours) PPMI, new-onset persistent left bundle branch block, and new-onset atrial fibrillation. Predictors were assessed using multivariable logistic regression. PPMI due to high-grade atrioventricular block occurred in 21.0% of patients (early PPMI: 13.5%, late PPMI: 7.5%). Key predictors included preexisting right bundle branch block (adjusted odds ratio, 5.45 [95% CI, 1.67-17.84]; P=0.005), ΔHV interval >10 ms (adjusted odds ratio, 3.62 [95% CI, 1.23-10.67]; P=0.020), and pre-TAVI rapid atrial pacing-induced atrioventricular Wenckebach (adjusted odds ratio, 3.70 [95% CI, 1.37-9.98]; P=0.010). The CONDUCT-TAVI score combined these variables with high predictive accuracy (area under the curve=0.794) and negative predictive value (98%). New-onset persistent left bundle branch block (>24 hours) was observed in 19.1%, and new-onset atrial fibrillation in 21.7% at 1 year. The incidence of conduction abnormalities remains high after TAVI, and after factoring in anatomic, procedural, and electrophysiological factors, a baseline right bundle branch block and electrophysiology study-derived measures of AV conduction were the most significant predictors of PPMI. The CONDUCT-TAVI score incorporates these findings to help implanters stratify low-risk patients and tailor follow-up care.