METHODS: This was a multicenter, prospective study that commenced in 2013 and enrolled participants from various clinical centers across China. We constructed GLIM criteria based on various muscle measurements, including fat-free mass index (FFMI), skeletal muscle index (SMI), calf circumference (CC), midarm circumference (MAC), midarm muscle circumference (MAMC), and midarm muscle area (MAMA). Survival was estimated using the Kaplan-Meier method and survival curves were compared using the log-rank test. Cox proportional hazards regression was used to assess the independent association between the GLIM criteria and OS. The discriminatory performance of different muscle-measurement-based GLIM criteria for mortality was evaluated using Harrell\'s concordance index (C-index). Logistic regression was used to evaluate the association of the GLIM criteria with short-term outcomes and healthcare burden.
RESULTS: A total of 4769 patients were included in the analysis, of whom 1659 (34.8%) died during the study period. The Kaplan-Meier curves demonstrated that all muscle-measurement-based GLIM criteria significantly predicted survival in patients with cancer (all p < 0.001). The survival rate of malnourished patients was approximately 10% lower than that of non-malnourished patients. Cox proportional hazards regression showed that all the muscle-measurement-based GLIM could independently predict the OS of patients (all p < 0.001). The prognostic discrimination was as follows: MAMC (Chi-square: 79.61) > MAMA (Chi-square: 79.10) > MAC (Chi-square: 64.09) > FFMI (Chi-square: 62.33) > CC (Chi-square: 58.62) > ASMI (Chi-square: 57.29). In comparison to the FFMI-based GLIM criteria, the ASMI-based criteria (-0.002, 95% CI: -0.006 to 0.002, p = 0.334) and CC-based criteria (-0.003, 95% CI: -0.007 to 0.002, p = 0.227) did not exhibit a significant advantage. However, the MAC-based criteria (0.001, 95% CI: -0.003 to 0.004, p = 0.776), MAMA-based criteria (0.004, 95% CI: 0.000-0.007, p = 0.035), and MAMC-based criteria (0.005, 95% CI: 0.000-0.007, p = 0.030) outperformed the FFMI-based GLIM criteria. Logistic regression showed that muscle measurement-based GLIM criteria predicted short-term outcomes and length of hospital stay in patients with cancer.
CONCLUSIONS: All muscle measurement-based GLIM criteria can effectively predict OS, short-term outcomes, and healthcare burden in patients with cancer. Anthropometric measurement-based GLIM criteria have potential for clinical application as an alternative to BIA-based measurement.
方法:这是一个多中心,前瞻性研究于2013年开始,招募了来自中国各个临床中心的参与者。我们根据各种肌肉测量结果构建了GLIM标准,包括无脂肪质量指数(FFMI),骨骼肌指数(SMI),小腿周长(CC),中臂周长(MAC),中臂肌围(MAMC),和中臂肌肉面积(MAMA)。使用Kaplan-Meier方法估计存活率,并使用对数秩检验比较存活曲线。Cox比例风险回归用于评估GLIM标准与OS之间的独立关联。使用Harrell一致性指数(C指数)评估基于不同肌肉测量的GLIM死亡率标准的辨别性能。使用Logistic回归评估GLIM标准与短期结果和医疗负担的相关性。
结果:总共4769名患者被纳入分析,其中1659人(34.8%)在研究期间死亡。Kaplan-Meier曲线表明,所有基于肌肉测量的GLIM标准均显着预测癌症患者的生存(所有p<0.001)。营养不良患者的生存率比非营养不良患者低约10%。Cox比例风险回归分析显示,所有基于肌肉测量的GLIM均可独立预测患者的OS(均p<0.001)。预后判断为:MAMC(卡方:79.61)>MAMA(卡方:79.10)>MAC(卡方:64.09)>FFMI(卡方:62.33)>CC(卡方:58.62)>ASMI(卡方:57.29)。与基于FFMI的GLIM标准相比,基于ASMI的标准(-0.002,95%CI:-0.006~0.002,p=0.334)和基于CC的标准(-0.003,95%CI:-0.007~0.002,p=0.227)没有显著优势.然而,基于MAC的标准(0.001,95%CI:-0.003至0.004,p=0.776),基于MAMA的标准(0.004,95%CI:0.000-0.007,p=0.035),基于MAMC的标准(0.005,95%CI:0.000-0.007,p=0.030)优于基于FFMI的GLIM标准。Logistic回归显示,基于肌肉测量的GLIM标准可预测癌症患者的短期预后和住院时间。
结论:所有基于肌肉测量的GLIM标准都可以有效地预测OS,短期结果,以及癌症患者的医疗负担。基于人体测量的GLIM标准具有临床应用潜力,可替代基于BIA的测量。