Author + information
- Abhijeet Dhoble1,
- Pimprapa Vejpongsa2,
- Xu Zhang3,
- Viraj Bhise4,
- Danai Kitkungvan2,
- H.V. Anderson2,
- Prakash Balan5,
- Tuyen C. Nguyen6,
- Anthony Estrera7 and
- Richard Smalling8
- 1University of Texas Health Science Center, Houston, Texas, United States
- 2University of Texas McGovern Medical School, Houston, Texas, United States
- 3University of Texas - Center for Clinical and Translational Sciences, Houston, Texas, United States
- 4Swiss Cardiovascular Center Bern, Bern, Switzerland
- 5University of Texas Health and Science Center, Houston, Texas, United States
- 6The University of Texas Health Science Center at Houston, Houston, Texas, United States
- 7University of Texas Medical School at Houston, Houston, Texas, United States
- 8UTHealth/Memorial Hermann Heart and Vascular Institute, Houston, Texas, United States
Atrioventricular conduction disturbance requiring permanent pacemaker (PPM) implantation is the most common complication after transcatheter aortic valve replacement (TAVR). Improved risk stratification prior to TAVR procedures is warranted. The aim of this study was to develop and validate a risk-prediction model for PPM implantation after TAVR, based on pre-procedure clinical data and electrocardiographic (EKG) conduction abnormalities.
This PPM risk assessment model was developed using the 2012 and 2013 National Inpatient Sample (NIS). A logistic regression model was built to identify the predictors of PPM placement. The performance of the model was validated using the NIS 2014 dataset.
Of 18,400 patients in development cohort, 1825 (9.9%) patients required PPM implantation after TAVR. After multivariate analysis, final predictive covariates of PPM implantation included left or right bundle branch block, bradycardia, 2nd-degree AV block and transfemoral approach for TAVR. The estimated regression coefficients associated with these predictors were used to develop a scoring system. The proposed scoring system showed good discrimination in both development and validation cohorts, with c-statistics of 0.754 (95% CI: 0.726-0.782) and 0.746 (95% CI: 0.721-0.772) respectively.
|Estimated regression coefficients|
|Characteristic||Regression coefficient Estimate||Odds Ratio (95% CI)||p-value|
|Transfemoral access||0.505||1.66 (1.23 – 2.24)||0.001|
|LBBB without bradycardia||1.068||2.91 (1.93-4.40)||<0.001|
|Bradycardia without LBBB||1.711||5.53 (4.27-7.18)||<0.001|
|LBBB and bradycardia||2.086||8.05 (5.14-12.60)||<0.001|
|2nd degree AV block||2.365||10.65 (5.80-19.53)||<0.001|
This PPM risk prediction model derived using NIS database is a simple tool that can estimate individual risk of PPM prior to TAVR procedure. The model displayed very good discrimination indices.
STRUCTURAL: Valvular Disease: Aortic