The validation of four published algorithms to predict the need for pacemaker implantation after TAVR. (14th October 2021)

• Record Type: Journal Article
• Authors: El Garhy, M; Owais, T; Lapp, H; Kuntze, T; Lauten, P
• Is Part Of: European heart journal. Volume 42(2021)Supplement 1
• Publication Date: 2021-10-14
• Subjects: Aortic Valve Intervention; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab724.2206
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: The identification of patients with high risk for PPMI after TAVR might change our decision as regard the type of the prosthesis and allow more patients' acceptance for this complication. Objective: we investigated the predictors of PPMI after TF-TAVR and validated the accuracy of four published algorithms in this group of patients. Methods and results: We retrospectively examined all patients who were in need for pacemaker implantation during the index hospitalisation after TAVR between 2016 and 2019. We searched for the predictors of the new PPMI after TAVR in this group of patient and compared it with a matched group of patients. Moreover, we tested the accuracy of four published algorithms. The first tested algorithm from Kaneko et al had positive predictive value (PPV), negative predictive value (NPV) and accuracy from 50%, 65% and 60% consecutively. The second tested algorithm from Jilaihawi et al had PPV, NPV and accuracy from 13.6%, 100% and 26.9% consecutively. The third tested algorithm from Maeno et al had PPV, NPV and accuracy from 37%, 56% and 45% consecutively. The forth tested algorithm from Fujiti et al had PPV, NPV and accuracy from 42%, 65% and 50% consecutively. In this study, 3 ECG-predictors (RBBB, the presence of AF and LAHB) and 3 CT-predictors (Aortic valve calcification Volume >500mm 3, eccentricity index >0.25, deep valve implantation in relation to the length of membranous septum) were independent predictors of PPMI. Moreover, the rate of preimplantation ballon valivuloplasty was higher in the group with new PPMI. Using these independent predictors, the new 7 points score was developed by assigning 1 point for each one. AUC of the new score in the derivation cohort was 0.809 (95% CI 0.758–0.86), with an optimal cut-off threshold of 4 points. All other scores had AUC from 0.6 or lower. In a validation cohort of 100 patients, the predictive value of the score was confirmed (AUC, 0.72; 95% CI, 0.70–0.87; P<0.001). Conclusion: The four studied score systems had low accuracy to predict new PPMI after TAVR in our cohort of patients. The new score is more complex but might be more accurate. Funding Acknowledgement: Type of funding sources: None.