Salwa, Najiyah

Introduction: Conduction disturbance after transcatheter aortic valve replacement (TAVR) remains a common complication. Predictive tools to identify conduction disturbance requiring pacing are limited. Our main objective was to develop a deep learning model to predict the need for a permanent pacemaker. Hypothesis: An electrocardiogram (ECG) based deep learning algorithm can predict the need for permanent pacemaker post TAVR. Methods: We evaluated patients who had TAVR at Mayo Clinic between June 2010 and May 2021. Digital ECGs done on the TAVR date or within 24 hours post procedure were partitioned into training, validation, and testing datasets. We trained a deep learning algorithm on the training data and optimized hyperparameters against the validation data. All model performance metrics are based on the reserved test data that were estimated once model architecture was optimized. Need for permanent pacemaker, defined as pacemaker placement due to development of conduction disturbance post TAVR and at least 1% ventricular pacing on device interrogation done at least 2 weeks post pacemaker implantation, was the training target. Results: A total of 3021 patients were evaluated for inclusion and 2,407 cases were available for modeling (exclusions: prior pacemaker (n=419), unsuccessful TAVR (n=27) and no ECG within the pre-specified period (n=168)). Overall, the median age was 82 (Q1: 76, Q3: 86) and 41% were female. In the withheld test data, 15% had a permanent pacemaker with at least 1% ventricular pacing. The algorithm detected the need for pacemaker in the test set with an AUC of 0.83 (95% CI: 0.73 to 0.92). Accuracy, sensitivity, specificity, positive and negative predictive were 80%, 72%, 81%, 41% and 94%, respectively. Conclusions: This study demonstrates the ability of a deep learning model based on ECG data to assist in making the decision to implant a pacemaker following TAVR with a high negative predictive value. Larger validation studies are warranted.

Background: Transcatheter aortic valve replacement (TAVR) is an approved alternative to surgical aortic valve replacement. However, as the clinical indication and adoption of TAVR expands, it remains important to identify predictors of mortality after TAVR. We aimed to study the impact of aortic valve calcium score on long term mortality following TAVR in a large patient population. Methods: We included consecutive patients who successfully underwent TAVR from June 2010 – June 2019 across all Mayo Clinic sites. Patients were followed through February 2021. Aortic valve calcium scores were obtained from preoperative computed tomography angiography of the chest. Additional data was abstracted from medical records. Cox-proportional hazard regression models were employed to evaluate the independent effect of aortic valve calcium score on mortality. Results: A total of 1988 patients were evaluated in the final analysis. Forty-two percent were women, median age was 83 years (Q1: 77, Q3: 87), 16% received a permanent pacemaker following TAVR and 83% received an Edwards Sapien valve. We demonstrate an incremental increase in mortality risk as aortic valve calcium score values increased after adjusting for age, gender, pacemaker implantation, left bundle branch block, and Charlson Comorbidity Index. Selected calcium score cut-points with adjusted hazard ratios (aHR) are displayed: 1200 AU (aHR: 1.13, 95% CI 1.06, 1.20); 2000 AU (aHR: 1.22, 95% CI 1.09, 1.35); 3000 AU (aHR: 1.34, 95% CI: 1.14, 1.58). Conclusion: This study demonstrates an association between aortic valve calcium score and long-term mortality in a large patient population. This highlights the potential predictive value of the calcium score in preoperative planning and may provide clinicians with an additional measure for risk stratification prior to TAVR.