Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement. Issue 2 (28th January 2021)

• Record Type: Journal Article
• Authors: Truong, Vien T.; Beyerbach, Daniel; Mazur, Wojciech; Wigle, Matthew; Bateman, Emma; Pallerla, Akhil; Ngo, Tam N.M.; Shreenivas, Satya; Tretter, Justin T.; Palmer, Cassady; Kereiakes, Dean J.; Chung, Eugene S.
• Is Part Of: Pacing and clinical electrophysiology. Volume 44:Issue 2(2021)
• Page Start: 334
• Page End: 340
• Publication Date: 2021-01-28
• Subjects: machine learning -- pacemaker implantation -- prediction -- random forest -- TAVR; Cardiac pacing -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1111/pace.14163
• Languages: English
• ISSNs: 0147-8389

Abstract

Abstract: Background: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utility of pre‐ and post‐TAVR ECG data and compare machine learning approaches with traditional logistic regression in predicting pacemaker risk following TAVR. Methods: Five hundred fifity seven patients in sinus rhythm undergoing TAVR for severe aortic stenosis (AS) were included in the analysis. Baseline demographics, clinical, pre‐TAVR ECG, post‐TAVR data, post‐TAVR ECGs (24 h following TAVR and before PPI), and echocardiographic data were recorded. A Random Forest (RF) algorithm and logistic regression were used to train models for assessing the likelihood of PPI following TAVR. Results: Average age was 80 ± 9 years, with 52% male. PPI after TAVR occurred in 95 patients (17.1%). The optimal cutoff of delta PR (difference between post and pre TAVR PR intervals) to predict PPI was 20 ms with a sensitivity of 0.82, a specificity of 0.66. With regard to delta QRS, the optimal cutoff was 13 ms with a sensitivity of 0.68 and a specificity of 0.59. The RF model that incorporated post‐TAVR ECG data (AUC 0.81) more accurately predicted PPI risk compared to the RF model without post‐TAVR ECG data (AUC 0.72). Moreover, the RF model performed better than logistic regression model in predicting PPI risk (AUC: 0.81 vs. 0.69). Conclusions: Machine learning using RF methodology is significantly more powerful than traditional logistic regression in predicting PPI risk following TAVR.