4.3 Article

Risk stratification using lean body mass in patients undergoing transcatheter aortic valve replacement

Journal

CATHETERIZATION AND CARDIOVASCULAR INTERVENTIONS
Volume 92, Issue 7, Pages 1365-1373

Publisher

WILEY
DOI: 10.1002/ccd.27547

Keywords

prognosis; transcatheter aortic valve replacement

Funding

  1. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Funding Source: Medline

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Background The prognostic impact of skeletal muscle mass, assessed using lean body mass (LBM), remain unclear in patients who underwent transcatheter aortic valve replacement (TAVR). The aim of this study to assess prognostic impact of LBM on mortality after TAVR. Methods We assessed 1,613 patients (median age 85 years, 70% female) who underwent TAVI from October 2013 to April 2016 using OCEAN (Optimized transCathEter vAlvular interveNtion)-TAVI registry data. LBM was calculated using the James formula. The primary endpoint was all-cause death after TAVR. Results Median follow-up period was 287 days (interquartile range 110-462). The Kaplan-Meier analysis demonstrated that patients with low LBM had significantly higher incidence of all-cause death than those with high LBM in male (32.3% vs. 9.9%, log rank P < 0.001) and female (15.8% vs. 9.2%, log-rank P = 0.011). On contrary, the risk stratification using body mass index (BMI) could not validate into female patients who underwent TAVR. The multivariate analysis showed that the LBM was an independent predictor of all-cause death in male (Hazard ratio [HR] 0.93; 95% confidence interval [CI] 0.89-0.98) and female (HR 0.94; 95% CI 0.89-0.99). Inversely, the assessment using BMI could not identify the high-risk population in a female. Conclusions The patients with low LBM had the higher incidence of all-cause death after TAVR than those with high LBM, regardless of gender. Thus, the risk stratification using LBM might provide further insight to identify the high-risk TAVR population, compared to conventional risk stratification using BMI.

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