机构：[1]Department of Radiology, The 3rd Peoples’ Hospital of Kunming, China[2]Medical Imaging Department, First Affiliated Hospital of KunmingMedical University, Kunming Medical University, Kunming, China医技科室医学影像中心昆明医科大学附属第一医院[3]Department of Radiology, Dali Bai Autonomous Prefecture People’s Hospital,Dali, China[4]Department of Radiology, CT Room, People’s Hospital of Yuxi City, Yuxi, China[5]Medical Imaging Department, Yunnan ProvincialInfectious Disease Hospital (Yunnan AIDS Care Center), Kunming, China[6]Department of Radiology, the First People’s Hospital of Zhaotong,Zhaotong, China[7]NHC Key Laboratory of Drug Addiction Medicine, First Affiliated Hospital of Kunming Medical University, Kunming MedicalUniversity, Kunming, China昆明医科大学附属第一医院国家卫生健康委毒品依赖和戒治重点实验室国家级重点实验室
Background: The outbreak of COVID-19 poses a major and urgent threat to global public health. CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. This study aimed to retrospectively analyze abnormal lung changes following initial computed tomography (CT) among patients with coronavirus disease 2019 (COVID-19) in Yunnan, and to evaluate the effectiveness of a chest CT-based model for the diagnosis of COVID-19. Methods: One hundred and nine patients with COVID-19 pneumonia confirmed with the positive new coronavirus nucleic acid antibody who exhibited abnormal findings on initial CT were retrospectively analyzed. Thereafter, changes in the number, distribution, shape, and density of the lesions were observed. Further, the epidemiological, clinical, and CT imaging findings (+/-) were correlated. Following univariate and multivariate logistic regression analysis, receiver operating characteristic (ROC) curves were generated for significant factors, and models were established to evaluate the diagnostic ability of CT for COVID-19. Results: Our results showed significant differences between patients with COVID-19 in epidemiological history (first, second, and third generation), clinical type (moderate, severe, and critical), and abnormal CT imaging characteristics (+/-) (P<0.05). Moreover, significant differences in abnormal CT imaging characteristics, including region, extent, and focus, were observed between the first generation and the other generations (P<0.05). For the diagnosis of COVID-19, the areas under the ROC curves for logistic regression models 1, 2, and 3 were 0.8016 (95% CI: 0.6759-0.9274), 0.9132 (95% CI: 0.8571-0.9693), and 0.9758 (95% CI: 0.9466-1), respectively. Conclusions: The ROC curve regression model based on chest CT signs displayed a high diagnostic value for COVID-19.
第一作者机构：[1]Department of Radiology, The 3rd Peoples’ Hospital of Kunming, China
共同第一作者：
通讯作者：
通讯机构：[*1]NHC Key Laboratory of Drug Addiction Medicine, Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming 650031, China.[*2]Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming 650031, China
推荐引用方式(GB/T 7714)：
Li Xiang,Yuan Feng,Zhang Zhenguang,et al.Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19[J].ANNALS OF PALLIATIVE MEDICINE.2021,10(2):2048-+.doi:10.21037/apm-20-2603.
APA：
Li, Xiang,Yuan, Feng,Zhang, Zhenguang,Yang, Juntao,Zhang, Jing...&Wang, Kunhua.(2021).Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19.ANNALS OF PALLIATIVE MEDICINE,10,(2)
MLA：
Li, Xiang,et al."Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19".ANNALS OF PALLIATIVE MEDICINE 10..2(2021):2048-+
