Department of Gastroenterology, Surgical Division, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
Department of Gastroenterology, Surgical Division, Faculty of Medicine, University of São Paulo, São Paulo, Brazil.
Nutrition. 2021 Apr;84:111083. doi: 10.1016/j.nut.2020.111083. Epub 2020 Nov 21.
The use of easily accessible methods to estimate skeletal muscle mass (SMM) in patients with cirrhosis is often limited by the presence of edema and ascites, precluding a reliable diagnosis of sarcopenia. The aim of this study was to design predictive models using variables derived from anthropometric and/or biochemical measures to estimate SMM; and to validate their applicability in diagnosing sarcopenia in patients with cirrhosis.
Anthropometric and biochemical data were obtained from 124 male patients (18-76 y of age) with cirrhosis who also underwent dual-energy x-ray absorptiometry (DXA) and handgrip strength (HGS) assessments to identify low SMM and diagnose sarcopenia using reference cutoff values. Univariate analyses for variable selection were applied to generate predictive decision tree models for low SMM. Model accuracy for the prediction of low SMM and sarcopenia (when associated with HGS) was tested by comparison with reference cutoff values (appendicular SMM index, obtained by DXA) and clinical sarcopenia diagnoses. The prognostic value of the models for the prediction of sarcopenia and mortality at 104 wk of follow up was further tested using Kaplan-Meier graphics and Cox models.
The models with anthropometric variables, alone and combined with biochemical variables, showed good accuracy (0.89 [0.83; 0.94] and 0.90 [0.84; 0.95], respectively) and sensitivity (0.72 [0.56; 0.85] and 0.74 [0.59; 0.86], respectively) and excellent specificity (0.96 [0.90; 0.99] and 0.97 [0.92; 0.99], respectively) in predicting SMM. Both models showed excellent accuracy (0.94 [0.89; 0.98], good sensitivity (0.68 [0.45; 0.86]), and excellent specificity (1.00 [0.96; 1.00]) in predicting sarcopenia. The models predicted mortality in patients with sarcopenia, with the likelihood of death sixfold greater relative to patients not predicted to have sarcopenia.
Our simple and inexpensive models provided a practical and safe approach to diagnosing sarcopenia patients with cirrhosis along with an estimate of their mortality risk when other reference methods are unavailable.
在肝硬化患者中，使用易于获取的方法来评估骨骼肌量（SMM）常常受到水肿和腹水的限制，从而无法可靠地诊断肌少症。本研究旨在设计使用来自人体测量学和/或生化指标的变量来预测 SMM 的预测模型，并验证其在诊断肝硬化患者肌少症中的适用性。
对 124 名男性肝硬化患者（年龄 18-76 岁）进行人体测量学和生化数据采集，这些患者还接受了双能 X 线吸收法（DXA）和握力（HGS）评估，以使用参考截断值确定低 SMM 并诊断肌少症。应用单变量分析进行变量选择，以生成用于预测低 SMM 的预测决策树模型。通过与参考截断值（通过 DXA 获得的四肢 SMM 指数）和临床肌少症诊断比较，测试模型预测低 SMM 和肌少症（当与 HGS 相关联时）的准确性。进一步使用 Kaplan-Meier 图和 Cox 模型测试模型预测肌少症和 104 周随访时死亡率的预后价值。
单独使用人体测量学变量和联合使用生化变量的模型显示出良好的准确性（0.89 [0.83；0.94]和 0.90 [0.84；0.95]）和敏感性（0.72 [0.56；0.85]和 0.74 [0.59；0.86]）以及出色的特异性（0.96 [0.90；0.99]和 0.97 [0.92；0.99]），可预测 SMM。两种模型在预测肌少症方面均具有出色的准确性（0.94 [0.89；0.98]，良好的敏感性（0.68 [0.45；0.86]）和出色的特异性（1.00 [0.96；1.00]）。模型预测了肌少症患者的死亡率，与未预测为肌少症的患者相比，死亡的可能性增加了六倍。
我们的简单且经济实惠的模型为诊断肝硬化肌少症患者提供了一种实用且安全的方法，并估计了无法使用其他参考方法时的死亡率风险。
