Department of Kinesiology, University of Waterloo, Waterloo, Ontario, Canada.
Department of Surgery, University of Maryland, Baltimore, Maryland, USA.
JPEN J Parenter Enteral Nutr. 2018 Jul;42(5):885-891. doi: 10.1002/jpen.1019. Epub 2017 Dec 6.
Computed tomography (CT) scans performed during routine hospital care offer the opportunity to quantify skeletal muscle and predict mortality and morbidity in intensive care unit (ICU) patients. Existing methods of muscle cross-sectional area (CSA) quantification require specialized software, training, and time commitment that may not be feasible in a clinical setting. In this article, we explore a new screening method to identify patients with low muscle mass.
We analyzed 145 scans of elderly ICU patients (≥65 years old) using a combination of measures obtained with a digital ruler, commonly found on hospital radiological software. The psoas and paraspinal muscle groups at the level of the third lumbar vertebra (L3) were evaluated by using 2 linear measures each and compared with an established method of CT image analysis of total muscle CSA in the L3 region.
There was a strong association between linear measures of psoas and paraspinal muscle groups and total L3 muscle CSA (R = 0.745, P < 0.001). Linear measures, age, and sex were included as covariates in a multiple logistic regression to predict those with low muscle mass; receiver operating characteristic (ROC) area under the curve (AUC) of the combined psoas and paraspinal linear index model was 0.920. Intraclass correlation coefficients (ICCs) were used to evaluate intrarater and interrater reliability, resulting in scores of 0.979 (95% CI: 0.940-0.992) and 0.937 (95% CI: 0.828-0.978), respectively.
A digital ruler can reliably predict L3 muscle CSA, and these linear measures may be used to identify critically ill patients with low muscularity who are at risk for worse clinical outcomes.
在常规医院护理中进行的计算机断层扫描 (CT) 检查提供了量化骨骼肌的机会，并可预测重症监护病房 (ICU) 患者的死亡率和发病率。现有的肌肉横截面积 (CSA) 量化方法需要专门的软件、培训和时间投入，这在临床环境中可能不可行。在本文中，我们探索了一种新的筛选方法，以识别肌肉量低的患者。
我们使用数字标尺组合的措施对 145 例老年 ICU 患者（≥65 岁）进行分析，这些措施通常在医院放射学软件中找到。使用 2 个线性测量值评估第三腰椎 (L3) 水平的腰大肌和竖脊肌肌群，并与 L3 区域 CT 图像分析总肌肉 CSA 的既定方法进行比较。
腰大肌和竖脊肌肌群的线性测量值与总 L3 肌肉 CSA 之间存在很强的关联（R = 0.745，P <0.001）。线性测量值、年龄和性别被纳入多变量逻辑回归模型，以预测肌肉量低的患者；联合腰大肌和竖脊肌线性指数模型的受试者工作特征 (ROC) 曲线下面积 (AUC) 为 0.920。使用组内相关系数 (ICC) 评估了测量者内和测量者间的可靠性，结果分别为 0.979（95% CI：0.940-0.992）和 0.937（95% CI：0.828-0.978）。
数字标尺可以可靠地预测 L3 肌肉 CSA，这些线性测量值可用于识别肌肉量低的危重症患者，这些患者的临床结局可能更差。
