Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.
Aged
Aged, 80 and over
Aortic Valve Stenosis
/ surgery
Arrhythmias, Cardiac
/ surgery
Female
Forecasting
Humans
Logistic Models
Machine Learning
Male
Pacemaker, Artificial
Postoperative Complications
/ surgery
Prosthesis Implantation
/ statistics & numerical data
Retrospective Studies
Risk Assessment
Transcatheter Aortic Valve Replacement
TAVR
machine learning
pacemaker implantation
prediction
random forest
Journal
Pacing and clinical electrophysiology : PACE
ISSN: 1540-8159
Titre abrégé: Pacing Clin Electrophysiol
Pays: United States
ID NLM: 7803944
Informations de publication
Date de publication:
02 2021
02 2021
Historique:
received:
20
09
2020
revised:
30
11
2020
accepted:
13
12
2020
pubmed:
13
1
2021
medline:
24
12
2021
entrez:
12
1
2021
Statut:
ppublish
Résumé
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. 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. 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). Machine learning using RF methodology is significantly more powerful than traditional logistic regression in predicting PPI risk following TAVR.
Sections du résumé
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
334-340Informations de copyright
© 2021 Wiley Periodicals LLC.
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