Department of Radiology, Changhai Hospital, The Navy Military Medical University, Changhai road 168, Shanghai, 200434, China.
Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Eur Radiol. 2020 Nov;30(11):6139-6150. doi: 10.1007/s00330-020-06973-9. Epub 2020 May 30.
To investigate whether meaningful subgroups sharing the CT features of patients with COVID-19 pneumonia could be identified using latent class analysis (LCA) and explore the relationship between the LCA-derived subgroups and clinical types.
This retrospective review included 499 patients with confirmed COVID-19 pneumonia between February 11 and March 8, 2020. Subgroups sharing the CT features were identified using LCA. Univariate and multivariate logistic regression models were utilized to analyze the association between clinical types and the LCA-derived subgroups.
Two radiological subgroups were identified using LCA. There were 228 subjects (45.69%) in class 1 and 271 subjects (54.31%) in class 2. The CT findings of class 1 were smaller pulmonary infection volume, more peripheral distribution, more GGO, more maximum lesion range ≤ 5 cm, a smaller number of lesions, less involvement of lobes, less air bronchogram, less dilatation of vessels, less hilar and mediastinal lymph node enlargement, and less pleural effusion than the CT findings of class 2. Univariate analysis demonstrated that older age, therapy, presence of fever, presence of hypertension, decreased lymphocyte count, and increased CRP levels were significant parameters associated with an increased risk for class 2. Multivariate analyses revealed that the patients with clinically severe type disease had a 1.97-fold risk of class 2 than the patients with clinically moderate-type disease.
The demographic and clinical differences between the two radiological subgroups based on the LCA were significantly different. Two radiological subgroups were significantly associated with clinical moderate and severe types.
• Two radiological subgroups were identified using LCA. • Older age, therapy, presence of fever, presence of hypertension, decreased lymphocyte count, and increased CRP levels were significant parameters with an increased risk for class 2 defined by LCA. • Patients with clinically severe type had a 1.97-fold higher risk of class 2 defined by LCA in comparison with patients showing clinically moderate-type disease.
通过潜在类别分析（LCA）确定具有 COVID-19 肺炎患者 CT 特征的有意义亚组，并探讨 LCA 衍生的亚组与临床类型之间的关系。
本回顾性研究纳入了 2020 年 2 月 11 日至 3 月 8 日期间确诊为 COVID-19 肺炎的 499 例患者。使用 LCA 确定具有 CT 特征的亚组。采用单变量和多变量逻辑回归模型分析临床类型与 LCA 衍生亚组之间的关系。
使用 LCA 确定了两个放射学亚组。类 1 中包含 228 例（45.69%）患者，类 2 中包含 271 例（54.31%）患者。与类 2 相比，类 1 的 CT 表现为肺部感染体积较小、更外周分布、更多磨玻璃影、最大病变范围≤5cm 的更多、病变数量更少、累及肺叶更少、空气支气管征更少、血管扩张更少、肺门和纵隔淋巴结肿大更少、胸腔积液更少。单变量分析表明，年龄较大、治疗、发热、高血压、淋巴细胞计数减少和 CRP 水平升高是与类 2 风险增加相关的显著参数。多变量分析显示，临床重型疾病患者类 2 的风险是临床中型疾病患者的 1.97 倍。
基于 LCA 的两个放射学亚组在人口统计学和临床方面存在显著差异。两个放射学亚组与临床中、重型显著相关。
使用 LCA 确定了两个放射学亚组。
年龄较大、治疗、发热、高血压、淋巴细胞计数减少和 CRP 水平升高是与 LCA 定义的类 2 风险增加相关的显著参数。
与临床中型疾病患者相比，临床重型疾病患者 LCA 定义的类 2 风险高 1.97 倍。
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