Association between skeletal muscle mass and the prognosis of patients undergoing percutaneous coronary intervention: a retrospective study.

backgroundThe predicted skeletal muscle mass index (pSMI) is a proven and reliable index that reflects muscle mass; however, its ability to predict major adverse cardiovascular events (MACES) in patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI) remains uncertain.

methodsA total of 1340 enrolled patients were ultimately included in the study and stratified according to the pSMI tertiles. The primary endpoint was a complex set of MACEs, including all-cause mortality, nonfatal myocardial infarction, and unplanned revascularization. The Kaplan‒Meier method was used to generate a cumulative incidence curve of MACEs and secondary endpoint events of all-cause mortality. Due to the competing risk relationship between all-cause mortality and cardiovascular mortality, non-fatal myocardial infarction, and unplanned revascularization events, a competing risk model was employed to analyze the cumulative event incidence curves of competing risk events.The restricted cubic spline analysis was conducted to examine the linear association between pSMI and the incidence of MACE. A univariate and multivariate Cox regression model was utilized to identify predictors of MACEs. The predictive value of the pSMI was evaluated by determining the area under the ROC curve.

resultsDuring a median follow-up of 31.38 months, 217 patients developed MACEs. The Kaplan-Meier survival curve showed the lowest risk of MACEs and all-cause mortality in the high pSMI group(log-rank test, P < 0.05). After adjusting for competing risk factors for all-cause death, the cumulative events of cardiac death in the T3 group were lower than other two groups (Gray's test, P < 0.001), with no significant difference in the cumulative incidence of non-fatal myocardial infarction and unplanned revascularization between the pSMI groups (Gray's test, P > 0.05). The adjusted hazard ratio (HR) for the incidence of MACEs in the highest pSMI tertile was 0.335(95% CI 0.182-0.615; P < 0.001), as shown by multivariable Cox regression analysis. Subgroup analysis revealed that the pSMI was negatively correlated with the incidence of MACEs in a population of nonelderly individuals, and those without heart failure (all P < 0.05). Both the univariate and fully adjusted restriction cubic spline (RCS) curves showed a linear relationship between the pSMI and MACEs. In addition, the inclusion of the pSMI in the basic risk prediction model improved prognostic prediction (the area under the ROC curve increased from 0.647 to 0.682, P = 0.033).

conclusionIn patients with CAD undergoing PCI, the pSMI is a protective factor and potentially simple method for assessing the risk of MACEs independently. CLINICAL TRIAL NUMBER: Not applicable.