Palliative Care Unit, National Cancer Institute José Alencar Gomes da Silva (INCA), Rio de Janeiro, RJ, Brazil.
Clin Nutr. 2020 May;39(5):1587-1592. doi: 10.1016/j.clnu.2019.07.002. Epub 2019 Jul 20.
BACKGROUND & AIMS: It is a challenge in clinical practice to identify and classify cancer cachexia. Currently, it has been extensively discussed if the presence of alterations in inflammatory biomarkers implies the presence of cachexia. This study aimed to evaluate the clinical relevance of cachexia classification through modified Glasgow Prognostic Score (mGPS) in advanced cancer patients in palliative care.
Observational prospective cohort study conducted at a Palliative Care Unit in Brazil. Cachexia classification was performed according to mGPS (based on albumin and C-reactive protein) in four different stages: no cachexia (NCa), undernourished (Un), pre cachexia (PCa), and refractory cachexia (RCa). Logistic regression models were used to test the association between cachexia stages and clinical, nutritional and functional domains. Kaplan-Meier curve and Cox multivariate model were used to analyze overall survival (OS).
A total of 1166 patients were included in the study. According to the cachexia framework 37.5% were NCa, 32.3% Un, 3.9% PCa and 26.4% RCa. Significant differences were observed among cachexia stages for most of the outcome measures. This classification was able to predict mortality in 90 days [Un (HR, 1.55; 95% CI, 1.25; 1.93); PCa (HR, 2.00; 95% CI, 1.34; 2.98); RCa (HR, 2.45; 95% CI, 1.34; 2.98)].
Cachexia stages were associated with significant differences in poor clinical outcomes and were also capable of predicting OS. This framework based on simple and objective criteria can be used as part of the routine to characterize the presence and stages of cachexia in advanced cancer patients.
在临床实践中，识别和分类癌症恶病质是一项挑战。目前，人们广泛讨论了炎症生物标志物的改变是否意味着恶病质的存在。本研究旨在评估改良格拉斯哥预后评分（mGPS）在姑息治疗中晚期癌症患者中对恶病质分类的临床相关性。
这是一项在巴西姑息治疗病房进行的观察性前瞻性队列研究。根据 mGPS（基于白蛋白和 C 反应蛋白）将恶病质分类为四个不同阶段：无恶病质（NCa）、营养不良（Un）、前恶病质（PCa）和难治性恶病质（RCa）。使用逻辑回归模型检验恶病质分期与临床、营养和功能领域之间的关联。使用 Kaplan-Meier 曲线和 Cox 多变量模型分析总生存期（OS）。
本研究共纳入 1166 例患者。根据恶病质框架，37.5%为 NCa，32.3%为 Un，3.9%为 PCa，26.4%为 RCa。在大多数结局指标中，各恶病质分期之间存在显著差异。这种分类能够预测 90 天内的死亡率[Un（HR，1.55；95%CI，1.25；1.93）；PCa（HR，2.00；95%CI，1.34；2.98）；RCa（HR，2.45；95%CI，1.34；2.98）]。
恶病质分期与不良临床结局有显著差异，且能够预测 OS。这种基于简单客观标准的框架可用于常规工作中，以确定晚期癌症患者恶病质的存在和分期。
