Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement: The role of machine learning.
World J Cardiol
; 15(3): 95-105, 2023 Mar 26.
Article
em En
| MEDLINE
| ID: mdl-37033682
ABSTRACT
BACKGROUND:
Atrioventricular block requiring permanent pacemaker (PPM) implantation is an important complication of transcatheter aortic valve replacement (TAVR). Application of machine learning could potentially be used to predict pre-procedural risk for PPM.AIM:
To apply machine learning to be used to predict pre-procedural risk for PPM.METHODS:
A retrospective study of 1200 patients who underwent TAVR (January 2014-December 2017) was performed. 964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis. After the exclusion of variables with near-zero variance or ≥ 50% missing data, 167 variables were included in the random forest gradient boosting algorithm (GBM) optimized using 5-fold cross-validations repeated 10 times. The receiver operator curve (ROC) for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year.RESULTS:
Of 964 patients included in the 30-d analysis without prior PPM, 19.6% required PPM post-TAVR. The mean age of patients was 80.9 ± 8.7 years. 42.1 % were female. Of 657 patients included in the 1-year analysis, the mean age of the patients was 80.7 ± 8.2. Of those, 42.6% of patients were female and 26.7% required PPM at 1-year post-TAVR. The area under ROC to predict 30-d and 1-year risk of PPM for the GBM model (0.66 and 0.72) was superior to that of the PPM risk score (0.55 and 0.54) with a P value < 0.001.CONCLUSION:
The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article