Machine learning identifies clinical parameters to predict mortality in patients undergoing transcatheter mitral valve repair

Background Transcatheter Mitral Valve Repair (TMVR) with MitraClip is an important treatment option for patients with severe mitral regurgitation. The lack of appropriate, validated and specific means to risk stratify TMVR patients complicates the evaluation of prognostic benefits of TMVR in clinical trials and practice. Purpose We aimed to develop an optimized risk stratification model for TMVR patients using machine learning (ML). Methods We included a total of 1009 TMVR patients from three large university hospitals, of which one (n=317) served as an external validation cohort. The primary endpoint was all-cause 1-year mortality, which was known in 95% of patients. Model performance was assessed using receiver operating characteristics. In the derivation cohort, different ML algorithms, including random forest, logistic regression, support vectors machines, k nearest neighbors, multilayer perceptron, and extreme gradient boosting (XGBoost) were tested using 5-fold cross-validation in the derivation cohort. The final model (Transcatheter MITral Valve Repair MortALIty PredicTion SYstem; MITRALITY) was tested in the validation cohort with respect to existing clinical scores. Results XGBoost was selected as the final algorithm for the MITRALITY Score, using only six baseline clinical features for prediction (in order of predictive importance): blood urea nitrogen, hemoglobin, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), mean arterial pressure, body mass index, and creatinine. In the external validation cohort, the MITRALITY Score's area under the curve (AUC) was 0.783, outperforming existing scores which yielded AUCs of 0.721 and 0.657 at best. 1-year mortality in the MITRALITY Score quartiles across the total cohort was 0.8%, 1.3%, 10.5%, and 54.6%, respectively. Odds of mortality in MITRALITY Score quartile 4 as compared to quartile 1 were 143.02 [34.75; 588.57]. Survival analyses showed that the differences in outcomes between the MITRALITY Score quartiles remained even over a timeframe of 3 years post intervention (log rank: p<0.005). With each increase by 1% in the MITRALITY score, the respective proportional hazard ratio for 3-year survival was 1.06 [1.05, 1.07] (Cox regression, p<0.05). Conclusion The MITRALITY Score is a novel, internally and externally validated ML-based tool for risk stratification of patients prior to TMVR. These findings may potentially allow for more precise design of future clinical trials, may enable novel treatment strategies tailored to populations of specific risk and thereby serve future daily clinical practice. FUNDunding Acknowledgement Type of funding sources: None. Summary Figure