Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis

Abstract

Background: Accurate mortality prediction following transcatheter aortic valve implantation (TAVI) is essential for mitigating risk, shared decision-making and periprocedural planning. Surgical risk models have demonstrated modest discriminative value for patients undergoing TAVI and are typically poorly calibrated, with incremental improvements seen in TAVI-specific models. Machine learning (ML) models offer an alternative risk stratification that may offer improved predictive accuracy.

Methods: PubMed, EMBASE, Web of Science and Cochrane databases were searched until 16 December 2023 for studies comparing ML models with traditional statistical methods for event prediction after TAVI. The primary outcome was comparative discrimination measured by C-statistics with 95% CIs between ML models and traditional methods in estimating the risk of all-cause mortality at 30 days and 1 year.

Results: Nine studies were included (29 608 patients). The summary C-statistic of the top performing ML models was 0.79 (95% CI 0.71 to 0.86), compared with traditional methods 0.68 (95% CI 0.61 to 0.76). The difference in C-statistic between all ML models and traditional methods was 0.11 (p<0.00001). Of the nine studies, two studies provided externally validated models and three studies reported calibration. Prediction Model Risk of Bias Assessment Tool tool demonstrated high risk of bias for all studies.

Conclusion: ML models outperformed traditional risk scores in the discrimination of all-cause mortality following TAVI. While integration of ML algorithms into electronic healthcare systems may improve periprocedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.

Keywords: Aortic Valve Stenosis; Meta-Analysis; Systematic Reviews as Topic; Transcatheter Aortic Valve Replacement; Translational Medical Research.

Figure 1. PRISMA flow chart. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; TAVI, transcatheter aortic valve implantation.

Figure 2. Summary C-statistic (95% CI) for top-performing ML models versus top-performing traditional risk score for all-cause mortality. ACC, American College of Cardiology; GBM, Gradient Boosting Machine; LR, logistic Regression; ML, machine learning; MLP, multilayer perceptron; NIS, National Inpatient Sample; OBSERVANT, Observatoire Francais du Remplacement Valvulaire Aortique par Voie Percutanée; RF, random forest; STS, Society of Thoracic Surgeons; TAVI, transcatheter aortic valve implantation; TAVR, transcatheter aortic valve replacement; XGBoost, Extreme Gradient Boosting.

Figure 3. Central graphical abstract. PROBAST, Prediction Model Risk of Bias Assessment Tool; TRIPOD, Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis.