Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, PR China.
Department of Radiology, Yongzhou Central Hospital, Yongzhou, Hunan, 425006, PR China.
Clin Nutr. 2022 Dec;41(12):3007-3015. doi: 10.1016/j.clnu.2021.05.031. Epub 2021 Jun 7.
About 10-20% of patients with Coronavirus disease 2019 (COVID-19) infection progressed to severe illness within a week or so after initially diagnosed as mild infection. Identification of this subgroup of patients was crucial for early aggressive intervention to improve survival. The purpose of this study was to evaluate whether computer tomography (CT) - derived measurements of body composition such as myosteatosis indicating fat deposition inside the muscles could be used to predict the risk of transition to severe illness in patients with initial diagnosis of mild COVID-19 infection.
Patients with laboratory-confirmed COVID-19 infection presenting initially as having the mild common-subtype illness were retrospectively recruited between January 21, 2020 and February 19, 2020. CT-derived body composition measurements were obtained from the initial chest CT images at the level of the twelfth thoracic vertebra (T12) and were used to build models to predict the risk of transition. A myosteatosis nomogram was constructed using multivariate logistic regression incorporating both clinical variables and myosteatosis measurements. The performance of the prediction models was assessed by receiver operating characteristic (ROC) curve including the area under the curve (AUC). The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve.
A total of 234 patients were included in this study. Thirty-one of the enrolled patients transitioned to severe illness. Myosteatosis measurements including SM-RA (skeletal muscle radiation attenuation) and SMFI (skeletal muscle fat index) score fitted with SMFI, age and gender, were significantly associated with risk of transition for both the training and validation cohorts (P < 0.01). The nomogram combining the SM-RA, SMFI score and clinical model improved prediction for the transition risk with an AUC of 0.85 [95% CI, 0.75 to 0.95] for the training cohort and 0.84 [95% CI, 0.71 to 0.97] for the validation cohort, as compared to the nomogram of the clinical model with AUC of 0.75 and 0.74 for the training and validation cohorts respectively. Favorable clinical utility was observed using decision curve analysis.
We found CT-derived measurements of thoracic myosteatosis to be associated with higher risk of transition to severe illness in patients affected by COVID-19 who presented initially as having the mild common-subtype infection. Our study showed the relevance of skeletal muscle examination in the overall assessment of disease progression and prognosis of patients with COVID-19 infection.
约 10-20% 的 2019 冠状病毒病（COVID-19）感染者在最初被诊断为轻症感染后一周左右发展为重症。识别这类亚组患者对于早期积极干预以提高生存率至关重要。本研究旨在评估计算机断层扫描（CT）衍生的身体成分测量值（如肌肉内脂肪沉积的肌内脂肪增多症）是否可用于预测初始诊断为轻度 COVID-19 感染患者向重症转变的风险。
本回顾性研究于 2020 年 1 月 21 日至 2 月 19 日期间招募了实验室确诊的 COVID-19 感染患者，这些患者最初表现为轻症普通型疾病。在第 12 胸椎（T12）水平的初始胸部 CT 图像上获得 CT 衍生的身体成分测量值，并使用这些测量值构建模型以预测向重症转变的风险。使用包含临床变量和肌内脂肪增多症测量值的多变量逻辑回归构建肌内脂肪增多症列线图。通过包括曲线下面积（AUC）在内的接受者操作特征（ROC）曲线评估预测模型的性能。通过区分度、校准曲线和决策曲线评估列线图的性能。
本研究共纳入 234 例患者。纳入的 31 例患者向重症疾病转变。肌内脂肪增多症测量值（包括骨骼肌衰减值和骨骼肌脂肪指数评分）与骨骼肌脂肪指数评分、年龄和性别拟合，与训练和验证队列的向重症转变风险显著相关（均 P<0.01）。将骨骼肌衰减值、骨骼肌脂肪指数评分和临床模型结合的列线图改善了向重症转变风险的预测，训练队列的 AUC 为 0.85[95%CI，0.75 至 0.95]，验证队列的 AUC 为 0.84[95%CI，0.71 至 0.97]，而临床模型的列线图的 AUC 分别为 0.75 和 0.74。决策曲线分析显示具有良好的临床实用性。
我们发现 CT 衍生的胸肌内脂肪增多症测量值与 COVID-19 感染患者最初表现为轻症普通型感染后向重症疾病转变的风险较高相关。我们的研究表明，骨骼肌检查在评估 COVID-19 感染患者疾病进展和预后方面具有重要意义。
