Tailored Risk Stratification in Severe Mitral Regurgitation and Heart Failure Using Supervised Learning Techniques.

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

Using supervised learning (survival tree method), 8 distinct subgroups were identified that differed significantly in long-term survival. Subgroup 7, characterized by younger age (≤66 years), higher hemoglobin (>12.7 g/dL), and higher albumin levels (>40.6 g/L) had the best survival. In contrast, subgroup 5 displayed a 20-fold risk of mortality (hazard ratio: 20.38 [95% CI: 10.78-38.52]);

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.

Informations de copyright

© 2022 The Authors.

Déclaration de conflit d'intérêts

This work was supported by a grant of the 10.13039/501100002428Austrian Science Fund (FWF – identification number: KLI-818B). The authors have reported that they have no relationships relevant to the contents of this paper to disclose.PERSPECTIVESCOMPETENCY IN PATIENT CARE AND PROCEDURAL SKILLS: In patients with severe sMR and HF, a heterogenous risk spectrum was identified by supervised learning techniques. Patients with younger age, better renal function, and higher hemoglobin values had the most favorable survival, whereas older patients with low serum albumin and higher NT-proBNP values experience a 20-fold risk increase in mortality. TRANSLATIONAL OUTLOOK: Further studies are needed to refine the therapeutic management for sMR in every HF subtype, taking into account the complex underlying heterogeneity in this population.