Department of Surgery, University Medical Center Utrecht, PO Box 85500, 3508 GA, Utrecht, The Netherlands.
Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands.
Eur J Trauma Emerg Surg. 2023 Aug;49(4):1947-1958. doi: 10.1007/s00068-023-02252-6. Epub 2023 Mar 2.
The present study aims to assess whether CT-derived muscle mass, muscle density, and visceral fat mass are associated with in-hospital complications and clinical outcome in level-1 trauma patients.
A retrospective cohort study was conducted on adult patients admitted to the University Medical Center Utrecht following a trauma between January 1 and December 31, 2017. Trauma patients aged 16 years or older without severe neurological injuries, who underwent a CT that included the abdomen within 7 days of admission, were included. An artificial intelligence (AI) algorithm was used to retrieve muscle areas to calculate the psoas muscle index and to retrieve psoas muscle radiation attenuation and visceral fat (VF) area from axial CT images. Multivariable logistic and linear regression analyses were performed to assess associations between body composition parameters and outcomes.
A total of 404 patients were included for analysis. The median age was 49 years (interquartile range [IQR] 30-64), and 66.6% were male. Severe comorbidities (ASA 3-4) were seen in 10.9%, and the median ISS was 9 (IQR 5-14). Psoas muscle index was not independently associated with complications, but it was associated with ICU admission (odds ratio [OR] 0.79, 95% confidence interval [CI] 0.65-0.95), and an unfavorable Glasgow Outcome Scale (GOS) score at discharge (OR 0.62, 95% CI 0.45-0.85). Psoas muscle radiation attenuation was independently associated with the development of any complication (OR 0.60, 95% CI 0.42-0.85), pneumonia (OR 0.63, 95% CI 0.41-0.96), and delirium (OR 0.49, 95% CI 0.28-0.87). VF was associated with developing a delirium (OR 1.95, 95% CI 1.12-3.41).
In level-1 trauma patients without severe neurological injuries, automatically derived body composition parameters are able to independently predict an increased risk of specific complications and other poor outcomes.
本研究旨在评估 CT 衍生的肌肉量、肌肉密度和内脏脂肪量是否与 1 级创伤患者的住院并发症和临床结局相关。
这是一项回顾性队列研究，纳入了 2017 年 1 月 1 日至 12 月 31 日期间入住乌得勒支大学医学中心的成年创伤患者。纳入标准为年龄≥16 岁、无严重神经损伤、入院后 7 天内行腹部 CT 检查的患者。使用人工智能（AI）算法检索肌肉区域以计算腰大肌指数，并从轴位 CT 图像中检索腰大肌辐射衰减和内脏脂肪（VF）面积。进行多变量逻辑回归和线性回归分析，以评估身体成分参数与结局之间的关系。
共纳入 404 例患者进行分析。患者的中位年龄为 49 岁（四分位距 30-64 岁），66.6%为男性。严重合并症（ASA 3-4 级）占 10.9%，中位 ISS 为 9 分（四分位距 5-14 分）。腰大肌指数与并发症无独立相关性，但与 ICU 入院（优势比 [OR] 0.79，95%置信区间 [CI] 0.65-0.95）和出院时不良格拉斯哥结局量表（GOS）评分（OR 0.62，95%CI 0.45-0.85）相关。腰大肌辐射衰减与任何并发症（OR 0.60，95%CI 0.42-0.85）、肺炎（OR 0.63，95%CI 0.41-0.96）和谵妄（OR 0.49，95%CI 0.28-0.87）的发生独立相关。VF 与谵妄的发生相关（OR 1.95，95%CI 1.12-3.41）。
在无严重神经损伤的 1 级创伤患者中，自动生成的身体成分参数能够独立预测特定并发症和其他不良结局的风险增加。
