RESUMO
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]); P < 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.
RESUMO
Disfunção de prótese valvar cardíaca (PVC) é rara, porém é uma potencial ameaça à vida. Estabelecer o exato mecanismo da disfunção da PVC é desafiador, no entanto é essencial para determinar a estratégia terapêutica apropriada. Na prática clinica, uma abordagem abrangente que integra vários parâmetros de morfologia e função avaliados pelo eco transtorácico 2D/3D e transesofágico são fundamentais para detectar e quantificar a disfunção da PVC. A cinefluoroscopia, a tomografia computadorizada com multidectetores, a ressonância magnética cardíaca, e em menor escala, a imagem nuclear, são ferramentas complementares para o diagnóstico e abordagem das complicações das PVC. Este documento apresenta recomendações para o uso de imagem em multimodalidade para avaliação das PVCs