Do machine learning models using LASSO-selected features improve the prediction of cardiovascular death compared to STS-PROM alone in patients with severe AS who underwent TAVI?
Machine learning models incorporating specific clinical and laboratory features provide superior prediction of cardiovascular death after TAVI compared to the traditional STS-PROM score alone.
Background: Prognostic models for cardiovascular death, but not all-cause death, after transcatheter aortic valve implantation (TAVI) have not been established yet. Methods and Results: In 252 patients with aortic stenosis (AS) who underwent TAVI (men/women 83/169; mean age 85 years), we explored predictive models by machine learning for cardiovascular death using 62 candidates. During the follow-up period (mean 1,135 days), 13 (5.2%) patients died of cardiovascular disease. The least absolute shrinkage and selection operator (LASSO) feature selection identified 8 features as important candidates, including old myocardial infarction, triglycerides/high-density lipoprotein cholesterol (TG/HDL-C) ratio, Society of Thoracic Surgeons predicted risk of mortality score (STS-PROM), pulse rate, left atrium volume index, stroke volume index, estimated glomerular filtration rate, and albumin. Cox regression analyses with adjustment for age and sex showed that old myocardial infarction, high levels of TG/HDL-C, STS-PROM, and pulse rate, as well as low levels of glomerular filtration rate and albumin, were independent risk factors for cardiovascular death. Models of logistic regression (LR) and random survival forest (RSF) using the LASSO-selected features, except for STS-PROM, significantly improved predictive abilities for cardiovascular death compared with LR analysis using STS-PROM alone. Conclusions: Machine learning models of prediction for cardiovascular death of LR and RSF using the LASSO-selected features are superior to a LR model using STS-PROM alone in patients with severe AS who underwent TAVI.
Otomo et al. (2025) studied this question.