Machine Learning Identifies Clinical Parameters to Predict Mortality in Patients Undergoing Transcatheter MitralValve Repair.

Abstract

The aim of this study was to develop a machine learning (ML)-based risk stratification tool for 1-year mortality in transcatheter mitral valve repair (TMVR) patients incorporating metabolic and hemodynamic parameters. The lack of appropriate, well-validated, and specific means to risk-stratify patients with mitral regurgitation complicates the evaluation of prognostic benefits of TMVR in clinical trials and practice. A total of 1,009 TMVR patients from 3 university hospitals within the HeartFailure Network Rhineland were included; 1 hospital (n=317) served as external validation. The primary endpoint was all-cause 1-year mortality. Model performance was assessed using receiver-operating characteristic curve analysis. In the derivation cohort, different ML algorithms were tested using 5-fold cross-validation. The final model, called MITRALITY (transcatheter mitral valve repair mortality prediction system) was tested in the validation cohort with respect to existing clinical scores. Extreme gradient boosting was selected for the MITRALITY score, using only 6 baseline clinical features for prediction (in order of predictive importance): urea, hemoglobin, N-terminal pro-brain natriuretic peptide, mean arterial pressure, body mass index, and creatinine. In the external validation cohort, the MITRALITY score's area under the curve was 0.783 (95% CI: 0.716-0.849), while existing scores yielded areas under the curve of 0.721 (95%CI: 0.63-0.811) and 0.657 (95%CI: 0.536-0.778) at best. The MITRALITY score is a novel, internally and externally validated ML-based tool for risk stratification of patients prior to TMVR, potentially serving future clinical trials and daily clinical practice.