The association between skeletal muscle mass and all-cause mortality in acute exacerbation of chronic obstructive pulmonary disease

Background Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) significantly impacts patient quality of life and prognosis. Skeletal muscle mass loss, a systemic manifestation of COPD, has garnered increasing attention, but its association with all-cause mortality in AECOPD remains unclear. This study aimed to quantitatively assess skeletal muscle mass in AECOPD patients using computed tomography and explore the association between muscle mass-related indices and all-cause mortality risk. Methods A total of 319 patients were enrolled in this single-center retrospective cohort study. Muscle mass-related indices, including skeletal muscle area, mean muscle density, intermuscular fat density, intermuscular fat area, and skeletal muscle index (SMI), were considered as independent variables. All-cause mortality was considered as the dependent variable. Univariate and multivariate Cox regression, subgroup and sensitivity analyses, receiver operating characteristic curve (ROC), restricted cubic spline plot (RCS), and Kaplan–Meier survival curves were used to examine the association between these indices and all-cause mortality in AECOPD patients. Results During a median follow-up of 14.63 (6.33, 21.13) months, the all-cause mortality was 113 (35.4%). Multivariate Cox regression revealed that, regardless of whether SMI was grouped based on the median of 26.08 or the cut-off point of 24.01, the low SMI group had a higher risk of all-cause mortality (HR: 0.495, 95% CI: 0.330–0.743, p = 0.001; HR: 0.400, 95% CI: 0.270–0.592, p < 0.001). Moreover, as a continuous variable, lower levels of SMI were independently associated with a higher risk of all-cause mortality (SMI, HR = 0.964, 95% CI: 0.934–0.996, p = 0.027; Standardized SMI, HR = 0.748, 95% CI: 0.578–0.967, p = 0.027). Subgroup and sensitivity analyses confirmed the significant association between SMI and all-cause mortality ( p < 0.05). ROC analysis showed good predictive value for SMI (area under the curve = 0.663, 95% CI: 0.559–0.728, p < 0.001), and RCS analysis revealed a non-linear relationship between SMI and mortality ( p nonlinear = 0.019). The Kaplan–Meier survival curves analysis indicated that regardless of whether SMI was grouped by median or by cut-off point, there were significant differences in the survival probability of all-cause mortality among different SMI groups, with the low SMI group having a poorer prognosis ( p < 0.001). Conclusion Among patients with AECOPD, higher levels of SMI are significantly associated with a lower risk of all-cause mortality, suggesting that SMI may have important prognostic value in the assessment of mortality risk in AECOPD patients.