Lu Shan, Xing Zhiheng, Zhao Shiyu, Meng Xianglu, Yang Juhong, Ding Wenlong, Wang Jigang, Huang Chencui, Xu Jingxu, Chang Baocheng, Shen Jun
NHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, Tianjin, China.
Haihe Hospital, Tianjin University, Tianjin Institute of Respiratory Diseases, Tianjin, China.
Int J Endocrinol. 2021 Mar 11;2021:6616069. doi: 10.1155/2021/6616069. eCollection 2021.
COVID-19 is a kind of pneumonia with new coronavirus infection, and the risk of death in COVID-19 patients with diabetes is four times higher than that in healthy people. It is unclear whether there is a difference in chest CT images between type 2 diabetes mellitus (T2DM) and non-diabetes mellitus (NDM) COVID-19 patients. The aim of this study was to investigate the differences in chest CT images between T2DM and NDM patients with COVID-19 based on a quantitative method of artificial intelligence. A total of 62 patients with COVID-19 pneumonia were retrospectively enrolled and divided into group A (T2DM COVID-19 pneumonia group, = 15) and group B (NDM COVID-19 pneumonia group, = 47). The clinical and laboratory examination information of the two groups was collected. Quantitative features (volume of consolidation shadows and ground glass shadows, proportion of consolidation shadow (or ground glass shadow) to lobe volume, total volume, total proportion, and number) of chest spiral CT images were extracted using Dr. Wise @Pneumonia software. The results showed that among the 26 CT image features, the total volume and proportion of bilateral pulmonary consolidation shadow in group A were larger than those in group B (=0.031 and 0.019, respectively); there was no significant difference in the total volume and proportion of bilateral pulmonary ground glass density shadow between the two groups ( > 0.05). In group A, the blood glucose level was correlated with the volume of consolidation shadow and the proportion of consolidation shadow to right middle lobe volume, and higher than those patients in group B. In conclusion, the inflammatory exudation in the lung of COVID-19 patients with diabetes is more serious than that of patients without diabetes based on the quantitative method of artificial intelligence. Moreover, the blood glucose level is positively correlated with pulmonary inflammatory exudation in COVID-19 patients.
新型冠状病毒肺炎（COVID-19）是一种由新型冠状病毒感染引起的肺炎，糖尿病患者感染COVID-19后的死亡风险是健康人的四倍。2型糖尿病（T2DM）合并COVID-19患者与非糖尿病（NDM）合并COVID-19患者的胸部CT图像是否存在差异尚不清楚。本研究旨在基于人工智能定量方法，探讨T2DM合并COVID-19患者与NDM合并COVID-19患者胸部CT图像的差异。本研究回顾性纳入62例COVID-19肺炎患者，分为A组（T2DM合并COVID-19肺炎组，n = 15）和B组（NDM合并COVID-19肺炎组，n = 47）。收集两组患者的临床和实验室检查信息。使用Wise博士@肺炎软件提取胸部螺旋CT图像的定量特征（实变影和磨玻璃影体积、实变影（或磨玻璃影）占肺叶体积的比例、总体积、总比例和数量）。结果显示，在26项CT图像特征中，A组双侧肺实变影的总体积和比例均大于B组（分别为P = 0.031和0.019）；两组双侧肺磨玻璃密度影的总体积和比例差异无统计学意义（P > 0.05）。A组患者血糖水平与实变影体积及实变影占右中叶体积的比例相关，且高于B组患者。综上所述，基于人工智能定量方法，糖尿病合并COVID-19患者肺部炎症渗出比非糖尿病患者更严重。此外，COVID-19患者血糖水平与肺部炎症渗出呈正相关。
