Department of Radiology, The 3rd Peoples' Hospital of Kunming, China.
Medical Imaging Department, First Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China.
Ann Palliat Med. 2021 Feb;10(2):2048-2061. doi: 10.21037/apm-20-2603.
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.
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.
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.
The ROC curve regression model based on chest CT signs displayed a high diagnostic value for COVID-19.
COVID-19 的爆发对全球公共卫生构成了重大而紧迫的威胁。本文旨在回顾性分析云南省首次计算机断层扫描（CT）后冠状病毒病 2019（COVID-19）患者的异常肺部变化，并评估基于胸部 CT 的模型对 COVID-19 的诊断价值。
回顾性分析 109 例经新型冠状病毒核酸抗体阳性确诊的 COVID-19 肺炎患者，观察首次 CT 异常的病变数量、分布、形态和密度变化，并对其流行病学、临床和 CT 影像学表现（+/-）进行相关性分析。对有意义的因素进行单因素和多因素逻辑回归分析，绘制受试者工作特征（ROC）曲线，建立模型评估 CT 对 COVID-19 的诊断能力。
我们的结果表明，COVID-19 患者在流行病学史（第一代、第二代和第三代）、临床类型（中度、重度和危重症）和异常 CT 影像学特征（+/-）方面存在显著差异（P<0.05）。此外，第一代和其他代之间异常 CT 影像学特征（包括部位、范围和焦点）存在显著差异（P<0.05）。对于 COVID-19 的诊断，逻辑回归模型 1、2 和 3 的 ROC 曲线下面积分别为 0.8016（95% CI：0.6759-0.9274）、0.9132（95% CI：0.8571-0.9693）和 0.9758（95% CI：0.9466-1）。
基于胸部 CT 征象的 ROC 曲线回归模型对 COVID-19 具有较高的诊断价值。
