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Published on: March 21, 2021
Prognostic role of myosteatosis in predicting MACE and mortality after TAVR: insights from CT-based body composition
Zhuofan Li1, Yao Zhao1, Jun Shu1
1Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
Background:
This study evaluated the prognostic value of computed tomography (CT)-defined myosteatosis (fat infiltration within muscle tissue) and low muscle mass (loss of muscle mass) for predicting major adverse cardiac events (MACE) and all-cause mortality among patients undergoing transcatheter aortic valve replacement (TAVR).
Methods:
Myosteatosis was defined as intramuscular adipose tissue (IMAT) exceeding 8.72 cm² in men and 4.58 cm² in women at the 12th thoracic vertebra level, while low muscle mass was defined as the sex-specific first tertile of the paravertebral muscle index (PMI). The primary endpoint was MACE (cardiac death, unstable angina, nonfatal myocardial infarction, nonfatal stroke, heart failure hospitalization), the secondary endpoint was all-cause mortality. We used Cox proportional hazards regression analysis to evaluate the association between body composition and clinical outcomes after TAVR.
Results:
A total of 371 patients (198 men and 173 women) were analyzed, 71 patients (19.1%) experienced MACE and 46 patients (12.4%) died. Myosteatosis was associated with higher risks of both MACE (HR: 2.39, 95% CI: 1.13-5.05, P = 0.023) and all-cause mortality (HR: 3.83, 95% CI: 1.54-9.53, P = 0.004). Patients with concomitant myosteatosis and low muscle mass had the worst outcomes, including MACE (HR: 5.06, 95% CI: 2.26-11.32, P < 0.001) and all-cause mortality (HR: 4.70, 95% CI: 1.30-16.95, P = 0.018). Adding myosteatosis to the clinical and low muscle mass model improved prognostic accuracy for both MACE (P = 0.005) and all-cause mortality (P = 0.018).
Conclusion:
CT-derived myosteatosis was associated with adverse outcomes after TAVR and showed modest additional prognostic information beyond low muscle mass and clinical variables. These findings should be externally validated before clinical implementation.
