Myosteatosis predicting risk of transition to severe COVID-19 infection

作者：Xiaoping Yi, Haipeng Liu, Liping Zhu, Dongcui Wang, Fangfang Xie, Linbo Shi, Mei Ji, Xiaolong Jiang, Qiuhua Zeng, Ping-Feng Hu, Yihui Li, Peipei Pang, Jie Liu, Wanxiang Peng, Harrison X. Bai, Weihua Liao, Bihong T. Chen · 发表于：Clinical Nutrition · 年份：2021 · DOI：10.1016/j.clnu.2021.05.031 · 被引用次数：45 · 研究领域：Long-Term Effects of COVID-19、Body Composition Measurement Techniques、Nutrition and Health in Aging

BACKGROUND: 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. METHODS: 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. RESULTS: A total of 234 patients were included in this study. Thirty-one of the enrolle...