Why the study?
Secondary mitral regurgitation in heart failure significantly impacts outcomes, making the identification of high-risk cohorts essential to understand disease trajectories and provide structured risk stratification.
Supervised machine learning using survival tree analysis can identify distinct high-risk subgroups for mortality in patients with severe secondary mitral regurgitation and heart failure, enabling tailored risk stratification.
May refine risk stratification in severe secondary MR with HF; hypothesis-generating pending prospective validation.
Background: Secondary mitral regurgitation (sMR) in the setting of heart failure (HF) has considerable impact on quality of life, HF rehospitalizations, and mortality. Identification of high-risk cohorts is essential to understand disease trajectories and for risk stratification. Objectives: This study aimed to provide a structured decision tree-like approach to risk stratification in patients with severe sMR and HF. Methods: This observational study included 1,317 patients with severe sMR from the entire HF spectrum. Clinical, echocardiographic, and laboratory data were extracted for all patients. The primary end point was all-cause mortality. Survival tree analysis, a supervised learning technique, was applied to identify patient subgroups at risk of mortality and further stratified by HF subtype (preserved, mildly reduced, and reduced ejection fraction). Results: < 0.001 and had older age (>68 years), low serum albumin (≤40.6 g/L), and higher NT-proBNP levels (≥9,750 pg/mL). Unique subgroups were further identified for each type of HF subtypes. Conclusions: Supervised machine learning reveals heterogeneity in the sMR risk spectrum, highlighting the clinical variability in the population. A decision tree-like model can help identify differences in outcomes among subgroups and can help provide tailored risk stratification.
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Heitzinger et al. (2022) studied this question.
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