Initial CT features of COVID-19 predicting clinical category.
Initial CT features of COVID-19 predicting clinical category.
复制标题
COVID-19 的初始 CT 特征预测临床类别
DOI：
10.1007/s42058-021-00056-4
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Liu S
中科院分区：
文献类型：
--
作者：
Fan L;Le W;Zou Q;Zhou X;Wang Y;Tang H;Han J;Liu S
分析不同临床类型新冠肺炎的初始CT表现。分析86例新冠肺炎患者的临床、实验室及影像特点。分析病变数量、部位、密度、肺结节、晕征、分布规律、内部结构及邻近结构改变等影像特征。计数资料采用卡方检验、Fisher‘s精确检验或Mann-Whitney U检验。用二元Logistic回归分析得出回归方程，估计重症和危重类别的可能性。变量选择采用正向条件法。年龄(p = 0.001)、性别(p = 0.028)在轻度与中度、重度与危重之间差异有统计学意义。两组在临床症状和白细胞计数方面均无明显差异。大多数病例(91.8%)表现为多灶性病变。重度和危重组GGO发生率高于轻、中度组。(57.8%vs.31.7%，p = 0.015)。淋巴细胞计数是判断重症危重病的重要指标。不同临床类型的初始CT表现重叠。结合实验室检查，尤其是淋巴细胞计数，有助于预测新冠肺炎的严重程度。网上版载有补充材料，可在10.1007/s42058-021-00056-4查阅。
To analyze the initial CT features of different clinical categories of COVID-19. A total of 86 patients with COVID-19 were analyzed, including the clinical, laboratory and imaging features. The following imaging features were analyzed, the lesion amount, location, density, lung nodule, halo sign, reversed-halo sign, distribution pattern, inner structures and changes of adjacent structures. Chi-square test, Fisher’s exact test, or Mann–Whitney U test was used for the enumeration data. Binary logistic regression analysis was performed to draw a regression equation to estimate the likelihood of severe and critical category. The forward conditional method was employed for variable selection. Significant statistical differences were found in age (p = 0.001) and sex (p = 0.028) between mild and moderate and severe and critical category. No significant difference was found in clinical symptoms and WBC count between the two groups. The majority of cases (91.8%) showed multifocal lesions. The presence of GGO was higher in severe and critical category than in the mild and moderate category. (57.8% vs.31.7%, p = 0.015). Lymphocyte count was important indicator for the severe and critical category. The initial CT features of the different clinical category overlapped. Combining with laboratory test, especially the lymphocyte count, could help to predict the severity of COVID-19. The online version contains supplementary material available at 10.1007/s42058-021-00056-4.
登录
查看更多内容
影响因子：
5.9
作者：
Francone, Marco;Iafrate, Franco;Catalano, Carlo
通讯作者：
Catalano, Carlo
DOI：
10.1148/ryct.2020200028
发表时间：
2020-02-01
期刊：
RADIOLOGY-CARDIOTHORACIC IMAGING
影响因子：
--
作者：
Kong, Weifang;Agarwal, Prachi P.
通讯作者：
Agarwal, Prachi P.
中国武汉市 138 例 2019 新型冠状病毒感染肺炎住院患者的临床特征。
影响因子：
120.7
作者：
Wang, Dawei;Hu, Bo;Peng, Zhiyong
通讯作者：
Peng, Zhiyong
影响因子：
19.7
作者：
Fang, Yicheng;Zhang, Huangqi;Ji, Wenbin
通讯作者：
Ji, Wenbin
影响因子：
19.7
作者：
Song, Fengxiang;Shi, Nannan;Shi, Yuxin
通讯作者：
Shi, Yuxin