Machine learning models using clinical variables, most notably frailty score, achieved 93% to 98% accuracy in predicting the selection of SAVR versus TAVR in patients with severe aortic stenosis.
Cohort (n=415)
Can machine learning-based predictive models accurately select the modality of aortic valve replacement (SAVR vs TAVR) in adult patients with severe aortic stenosis?
Machine learning models using clinical variables, particularly frailty score, can predict the selection between SAVR and TAVR with high accuracy (93-98%) in patients with severe aortic stenosis.
The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). We evaluated the performance of a machine learning-based predictive model using existing periprocedural variables for valve replacement modality selection. We analyzed 415 patients in a retrospective longitudinal cohort of adult patients undergoing aortic valve replacement for aortic stenosis. A total of 72 clinical variables including demographic data, patient comorbidities, and preoperative investigation characteristics were collected on each patient. We fit models using LASSO (least absolute shrinkage and selection operator) and decision tree techniques. The accuracy of the prediction on confusion matrix was used to assess model performance. The most predictive independent variable for valve selection by LASSO regression was frailty score. Variables that predict SAVR consisted of low frailty score (value at or below 2) and complex coronary artery diseases (DVD/TVD). Variables that predicted TAVR consisted of high frailty score (at or greater than 6), history of coronary artery bypass surgery (CABG), calcified aorta, and chronic kidney disease (CKD). The LASSO-generated predictive model achieved 98% accuracy on valve replacement modality selection from testing data. The decision tree model consisted of fewer important parameters, namely frailty score, CKD, STS score, age, and history of PCI. The most predictive factor for valve replacement selection was frailty score. The predictive models using different statistical learning methods achieved an excellent concordance predictive accuracy rate of between 93% and 98%.
Chokesuwattanaskul et al. (Fri,) conducted a cohort in Severe aortic stenosis (n=415). Machine learning-based predictive models (LASSO and decision tree) was evaluated on Accuracy of prediction on confusion matrix for valve replacement modality selection. Machine learning models using clinical variables, most notably frailty score, achieved 93% to 98% accuracy in predicting the selection of SAVR versus TAVR in patients with severe aortic stenosis.