Xiaoping Yi1, Haipeng Liu1, Liping Zhu1, Dongcui Wang1, Fangfang Xie1, Linbo Shi2, Ji Mei3, Xiaolong Jiang4, Qiuhua Zeng5, Pingfeng Hu6, Yihui Li7, Peipei Pang8, Jie Liu4, Wanxiang Peng7, Harrison X Bai9, Weihua Liao10, Bihong T Chen11. 1. Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, PR China. 2. Department of Radiology, Yongzhou Central Hospital, Yongzhou, Hunan, 425006, PR China. 3. Department of Radiology, Changde Second People's Hospital, Changde, Hunan, 415001, PR China. 4. Department of Radiology, Affiliated Nan Hua Hospital, University of South China, Hengyang, Hunan, 421002, PR China. 5. Department of Radiology, Loudi Central Hospital, Loudi, Hunan, 417000, PR China. 6. Department of Radiology, Chenzhou Second People's Hospital, Chenzhou, Hunan, 423000, PR China. 7. Department of Radiology, Zhuzhou Central Hospital, Zhuzhou, Hunan, 412002, PR China. 8. GE Healthcare, Hangzhou, 310000, PR China. 9. Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA. 10. Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, PR China; Molecular Imaging Research Center of Central South University, Changsha, 410008, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, 410008, PR China. Electronic address: Owenliao@csu.edu.cn. 11. Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, USA.
Abstract
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 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. CONCLUSION: 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.
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-19infection. METHODS:Patients with laboratory-confirmed COVID-19infection 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 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. CONCLUSION: 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-19infection.
Authors: Daniela M H Padilha; Maria C S Mendes; Fabiana Lascala; Marina N Silveira; Lara Pozzuto; Larissa A O Santos; Lívia D Guerra; Rafaella C L Moreira; Sandra R Branbilla; Ademar D C Junior; Mateus B O Duarte; Maria L Moretti; José B C Carvalheira Journal: Sci Rep Date: 2022-09-20 Impact factor: 4.996