胸部 CT 对评估 COVID-19 肺炎临床严重程度的性能：基于 CT 特征识别重症病例。
The Performance of Chest CT in Evaluating the Clinical Severity of COVID-19 Pneumonia: Identifying Critical Cases Based on CT Characteristics.
机构信息
From the The Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province.
CT Research Collaboration, Siemens Healthineers Ltd, Guangzhou, Guangdong Province, China.
出版信息
Invest Radiol. 2020 Jul;55(7):412-421. doi: 10.1097/RLI.0000000000000689.
OBJECTIVES
The aim of this study was to assess the clinical severity of COVID-19 pneumonia using qualitative and/or quantitative chest computed tomography (CT) indicators and identify the CT characteristics of critical cases.
MATERIALS AND METHODS
Fifty-one patients with COVID-19 pneumonia including ordinary cases (group A, n = 12), severe cases (group B, n = 15), and critical cases (group C, n = 24) were retrospectively enrolled. The qualitative and quantitative indicators from chest CT were recorded and compared using Fisher exact test, one-way analysis of variance, Kruskal-Wallis H test, and receiver operating characteristic analysis.
RESULTS
Depending on the severity of the disease, the number of involved lung segments and lobes, the frequencies of consolidation, crazy-paving pattern, and air bronchogram increased in more severe cases. Qualitative indicators including total severity score for the whole lung and total score for crazy-paving and consolidation could distinguish groups B and C from A (69% sensitivity, 83% specificity, and 73% accuracy) but were similar between group B and group C. Combined qualitative and quantitative indicators could distinguish these 3 groups with high sensitivity (B + C vs A, 90%; C vs B, 92%), specificity (100%, 87%), and accuracy (92%, 90%). Critical cases had higher total severity score (>10) and higher total score for crazy-paving and consolidation (>4) than ordinary cases, and had higher mean lung density (>-779 HU) and full width at half maximum (>128 HU) but lower relative volume of normal lung density (≦50%) than ordinary/severe cases. In our critical cases, 8 patients with relative volume of normal lung density smaller than 40% received mechanical ventilation for supportive treatment, and 2 of them had died.
CONCLUSIONS
A rapid, accurate severity assessment of COVID-19 pneumonia based on chest CT would be feasible and could provide help for making management decisions, especially for the critical cases.
目的
本研究旨在使用定性和/或定量胸部计算机断层扫描（CT）指标评估 COVID-19 肺炎的临床严重程度，并确定危重症病例的 CT 特征。
材料和方法
回顾性纳入 51 例 COVID-19 肺炎患者，包括普通病例（A 组，n=12）、重症病例（B 组，n=15）和危重症病例（C 组，n=24）。记录并比较胸部 CT 的定性和定量指标，采用 Fisher 确切检验、单因素方差分析、Kruskal-Wallis H 检验和受试者工作特征分析。
结果
根据疾病严重程度，受累肺段和肺叶数量、实变、铺路石征和空气支气管征的频率在病情较重的患者中增加。全肺总严重程度评分和铺路石征及实变总评分等定性指标可将 B 组和 C 组与 A 组区分开（敏感性 69%，特异性 83%，准确性 73%），但 B 组和 C 组之间无差异。定性和定量指标相结合可将这 3 组区分开来，具有较高的敏感性（B+C 与 A 相比，90%；C 与 B 相比，92%）、特异性（100%、87%）和准确性（92%、90%）。危重症患者的总严重程度评分（>10）和铺路石征及实变总评分（>4）高于普通病例，平均肺密度（>-779 HU）和全宽半最大值（>128 HU）高于普通/重症病例，而正常肺密度的相对体积（≦50%）低于普通/重症病例。在我们的危重症患者中，8 例相对正常肺密度体积小于 40%的患者接受机械通气支持治疗，其中 2 例死亡。
结论
基于胸部 CT 对 COVID-19 肺炎进行快速、准确的严重程度评估是可行的，可以为制定管理决策提供帮助，特别是对危重症病例。
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