Machine learning models using clinical and echocardiographic features predicted 1-year mortality in patients with ≥moderate tricuspid regurgitation with good performance (c-statistic 0.74-0.75).
Cohort (n=13,312)
Does machine learning of clinical and echocardiographic features predict mortality in patients with ≥moderate tricuspid regurgitation?
Machine learning models incorporating clinical and echocardiographic features can effectively predict mortality risk in patients with significant tricuspid regurgitation, though performance varies by comorbidity burden.
Effect estimate: c-statistic 0.74-0.75
OBJECTIVE: Tricuspid regurgitation (TR) is a prevalent valve disease associated with significant morbidity and mortality. We aimed to apply machine learning (ML) to assess risk stratification in patients with ≥moderate TR. METHODS: Patients with ≥moderate TR on echocardiogram between January 2005 and December 2016 were retrospectively included. We used 70% of data to train ML-based survival models including 27 clinical and echocardiographic features to predict mortality over a 3-year period on an independent test set (30%). To account for differences in baseline comorbidities, prediction was performed in groups stratified by increasing Charlson Comorbidity Index (CCI). Permutation feature importance was calculated using the best-performing model separately in these groups. RESULTS: Of 13 312 patients, mean age 72 ± 13 years and 7406 (55%) women, 7409 (56%) had moderate, 2646 (20%) had moderate-severe and 3257 (24%) had severe TR. The overall performance for 1-year mortality by 3 ML models was good, c-statistic 0.74-0.75. Interestingly, performance varied between CCI groups, (c-statistic = 0.774 in lowest CCI group and 0.661 in highest CCI group). The performance decreased over 3-year follow-up (average c-index 0.78). Furthermore, the top 10 features contributing to these predictions varied slightly with the CCI group, the top features included heart rate, right ventricular systolic pressure, blood pressure, diuretic use and age. CONCLUSIONS: Machine learning of common clinical and echocardiographic features can evaluate mortality risk in patients with TR. Further refinement of models and validation in prospective studies are needed before incorporation into the clinical practice.
Deb et al. (Wed,) conducted a cohort in Tricuspid regurgitation (n=13,312). Machine learning-based survival models was evaluated on Mortality over a 3-year period (c-statistic 0.74-0.75). Machine learning models using clinical and echocardiographic features predicted 1-year mortality in patients with ≥moderate tricuspid regurgitation with good performance (c-statistic 0.74-0.75).