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Published on: July 18, 2014
A Two-Step Risk Score for Prediction of Permanent Pacemaker Implantation After Transcatheter Aortic Valve
Alexandra Janiszewski1,2, Julia Lueg1,2, Daniel Schulze3
1Department of Cardiology, Angiology and Intensive Care Medicine Deutsches Herzzentrum der Charité Berlin Germany.
Background:
The need for postoperative permanent pacemaker implantation (PPMI) remains one of the most frequent complications after transcatheter aortic valve implantation (TAVI). This study aimed to develop a novel, 2-step risk score to predict PPMI probability after TAVI and implement it into a user-friendly website. Our risk score addresses the data gap on current prosthesis generations and provides a new, clinically motivated approach to calculating PPMI risk.
Methods And Results:
Between January 2019 and December 2020, 1039 patients underwent TAVI at our institution. We retrospectively evaluated clinical, electrocardiographic, echocardiographic, computed tomographic, and periprocedural data. Patients with prior PPMI were excluded. We developed a prediction model for PPMI occurrence, using 55 patient and procedural characteristics. With exclusion criteria applied, 836 patients (mean age 80.3±9.1 years; 50.6% female) were included. Of these, 149 (17.8%) required PPMI within 30 days after TAVI. Fourteen preprocedural parameters, including preexisting right bundle-branch block, atrioventricular block, left bundle-branch block, bradycardia, interventricular septum thickness, New York Heart Association class, and aortic annulus perimeter, were identified as PPMI risk factors and used to calculate the baseline risk in the first step of the TAVI PACER score. The second step includes intraprocedural variables to demonstrate how PPMI risk can vary based on valve type and implantation depth. The TAVI PACER score predicts PPMI with a sensitivity of 76% and specificity of 72% (area under the curve=0.8).
Conclusions:
The TAVI PACER score provides a novel tool for daily clinical practice, predicting individual PPMI risk after TAVI based on various patient and procedural characteristics.

