Suppr超能文献

入院时COVID-19肺炎的CT定量可预测向危重症的进展:一项回顾性多中心队列研究

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study.

作者信息

Pang Baoguo, Li Haijun, Liu Qin, Wu Penghui, Xia Tingting, Zhang Xiaoxian, Le Wenjun, Li Jianyu, Lai Lihua, Ou Changxing, Ma Jianjuan, Liu Shuai, Zhou Fuling, Wang Xinlu, Xie Jiaxing, Zhang Qingling, Jiang Min, Liu Yumei, Zeng Qingsi

机构信息

Department of Radiology, Huangpi District Hospital of Traditional Chinese Medicine, Wuhan, China.

Department of Radiology, Han Kou Hospital of Wuhan, Wuhan, China.

出版信息

Front Med (Lausanne). 2021 Jun 17;8:689568. doi: 10.3389/fmed.2021.689568. eCollection 2021.

Abstract

Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients. This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis. A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) ( = 0.535, < 0.001), erythrocyte sedimentation rate ( = 0.567, < 0.001), d-Dimer ( = 0.444, < 0.001), high-sensitivity C-reactive protein ( = 0.495, < 0.001), aspartate aminotransferase ( = 0.410, < 0.001), lactate dehydrogenase ( = 0.644, < 0.001), and urea nitrogen ( = 0.439, < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) ( = -0.535, < 0.001). Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

摘要

早期识别预后较差的2019冠状病毒病(COVID-19)患者可能有助于患者的临床管理。我们旨在量化COVID-19患者入院时CT上的肺炎表现,以预测其进展为危重症的情况。这项回顾性研究纳入了实验室确诊的成年COVID-19患者。所有患者均接受了胸部薄层计算机断层扫描(CT),显示有肺炎证据。分析中排除了有严重运动伪影的CT图像。从病历中收集患者的临床和实验室数据。使用care.ai智能多学科影像诊断平台的COVID-19胸部CT智能评估系统自动计算肺炎病灶的三个定量CT特征,即肺炎体积百分比(PPV)、磨玻璃影体积(PGV)和实变体积(PCV)。根据中国COVID-19诊疗指南(试行第七版),将患者分为非危重症组和危重症组。危重症定义为入住重症监护病房、需要机械通气的呼吸衰竭、休克或死亡的综合情况。评估了PPV、PGV和PCV在区分危重症方面的性能。通过Pearson相关分析评估PPV与实验室变量之间的相关性。共纳入140例患者,平均年龄58.6岁,男性85例(60.7%)。32例(22.9%)患者为危重症。以22.6%为临界值时,PPV在预测危重症方面表现最佳,曲线下面积为0.868,敏感性为81.3%,特异性为80.6%。PPV与中性粒细胞百分比(r = 0.535,P < 0.001)、红细胞沉降率(r = 0.567,P < 0.001)、D-二聚体(r = 0.444,P < 0.001)、高敏C反应蛋白(r = 0.495,P < 0.001)、天门冬氨酸氨基转移酶(r = 0.410,P < 0.001)、乳酸脱氢酶(r = 0.644,P < 0.001)和尿素氮(r = 0.439,P < 0.001)呈中度正相关,而PPV与淋巴细胞百分比(r = -0.535,P < 0.001)呈中度负相关。初始CT上量化的肺炎体积可提前无创预测进展为危重症的情况,可作为COVID-19的预后标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66b4/8245676/24d417e7a602/fmed-08-689568-g0002.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验