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

OBJECTIVES: 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.

BACKGROUND: 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.

METHODS: A total of 1,009 TMVR patients from 3 university hospitals within the Heart Failure 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.

RESULTS: 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.

CONCLUSIONS: 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.

Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

PMID: 34556277