• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

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

危重症 COVID-19 患者的临床特征和胸部 CT 影像学特征。

Clinical characteristics and chest CT imaging features of critically ill COVID-19 patients.

机构信息

Department of Radiology, Capital Medical University, Beijing Anzhen Hospital, 2nd Anzhen Road, Chaoyang District, Beijing, China.

Department of Radiology, China Resources & WISCO General Hospital, Wuhan, Hubei Province, China.

出版信息

Eur Radiol. 2020 Nov;30(11):6151-6160. doi: 10.1007/s00330-020-06955-x. Epub 2020 May 30.

DOI:10.1007/s00330-020-06955-x
PMID:32474629
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7260469/
Abstract

OBJECTIVES

To compare clinical, laboratory, and chest computed tomography (CT) findings in critically ill patients diagnosed with coronavirus disease 2019 (COVID-19) who survived and who died.

METHODS

This retrospective study reviewed 60 critically ill patients (43 males and 17 females, mean age 64.4 ± 11.0 years) with COVID-19 pneumonia who were admitted to two different clinical centers. Their clinical and medical records were analyzed, and the chest CT images were assessed to determine the involvement of lobes and the distribution of lesions in the lungs between the patients who recovered from the illness and those who died.

RESULTS

Compared with recovered patients (50/60, 83%), deceased patients (10/60, 17%) were older (mean age, 70.6 vs. 62.6 years, p = 0.044). C-reactive protein (CRP) (110.8 ± 26.3 mg/L vs 63.0 ± 50.4 mg/L, p < 0.001) and neutrophil-to-lymphocyte ratio (NLR) (18.7 ± 16.6 vs 8.4 ± 7.5, p = 0.030) were significantly elevated in the deceased as opposed to the recovered. Medial or parahilar area involvement was observed in all the deceased patients (10/10, 100%), when compared to only 54% (27/50) in the recovered. Ground-glass opacities (97%), crazy-paving pattern (92%), and air bronchogram (93%) were the most common radiological findings. There was significant difference in diabetes (p = 0.025) and emphysema (p = 0.013), and the odds ratio on a deceased patient having diabetes and emphysema was 6 times and 21 times the odds ratio on a recovered patient having diabetes and emphysema, respectively.

CONCLUSIONS

Older patients with comorbidities such as diabetes and emphysema, and higher CRP and NLRs with diffuse lung involvement were more likely to die of COVID-19.

KEY POINTS

• Almost all patients critically ill with COVID-19 pneumonia had five lung lobes involved. • Medial or parahilar area involvement and degree of lung involvement were more serious in the deceased patients when compared with those who recovered from treatment. • Chronic lung disease, e.g., emphysema, diabetes, and higher serum CRP and NLR characterized patients who died of COVID-19.

摘要

目的

比较临床、实验室和胸部计算机断层扫描(CT)在新冠肺炎(COVID-19)危重症患者存活和死亡患者中的发现。

方法

本回顾性研究纳入了在两个不同临床中心住院的 60 例 COVID-19 肺炎危重症患者(43 名男性和 17 名女性,平均年龄 64.4±11.0 岁)。分析了他们的临床和病历记录,并评估了胸部 CT 图像,以确定从疾病中康复的患者和死亡患者之间肺叶受累和病变分布的情况。

结果

与康复患者(50/60,83%)相比,死亡患者(10/60,17%)年龄更大(平均年龄 70.6 岁 vs. 62.6 岁,p=0.044)。死亡患者的 C 反应蛋白(CRP)(110.8±26.3 mg/L 比 63.0±50.4 mg/L,p<0.001)和中性粒细胞与淋巴细胞比值(NLR)(18.7±16.6 比 8.4±7.5,p=0.030)显著升高。所有死亡患者(10/10,100%)均有中隔或旁区受累,而康复患者仅有 54%(27/50)有中隔或旁区受累。磨玻璃影(97%)、铺路石征(92%)和空气支气管征(93%)是最常见的影像学表现。糖尿病(p=0.025)和肺气肿(p=0.013)存在显著差异,死亡患者合并糖尿病和肺气肿的比值比为康复患者合并糖尿病和肺气肿的比值比的 6 倍和 21 倍。

结论

患有糖尿病和肺气肿等合并症的老年患者,以及 CRP 和 NLR 较高且肺部弥漫性受累的患者,更有可能死于 COVID-19。

关键要点

  • 几乎所有患有 COVID-19 肺炎的危重症患者都有五个肺叶受累。

  • 与康复患者相比,死亡患者的中隔或旁区受累和肺部受累程度更严重。

  • 慢性肺部疾病,如肺气肿、糖尿病以及更高的血清 CRP 和 NLR 特征,是 COVID-19 死亡患者的特点。

相似文献

1
Clinical characteristics and chest CT imaging features of critically ill COVID-19 patients.危重症 COVID-19 患者的临床特征和胸部 CT 影像学特征。
Eur Radiol. 2020 Nov;30(11):6151-6160. doi: 10.1007/s00330-020-06955-x. Epub 2020 May 30.
2
Analysis of clinical features and imaging signs of COVID-19 with the assistance of artificial intelligence.人工智能辅助分析 COVID-19 的临床特征和影像征象。
Eur Rev Med Pharmacol Sci. 2020 Aug;24(15):8210-8218. doi: 10.26355/eurrev_202008_22510.
3
Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19).新冠肺炎(COVID-19)康复过程中胸部 CT 肺部变化的时间进程。
Radiology. 2020 Jun;295(3):715-721. doi: 10.1148/radiol.2020200370. Epub 2020 Feb 13.
4
The Performance of Chest CT in Evaluating the Clinical Severity of COVID-19 Pneumonia: Identifying Critical Cases Based on CT Characteristics.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。
Invest Radiol. 2020 Jul;55(7):412-421. doi: 10.1097/RLI.0000000000000689.
5
CT in coronavirus disease 2019 (COVID-19): a systematic review of chest CT findings in 4410 adult patients.CT 在 2019 年冠状病毒病(COVID-19)中的应用:对 4410 例成年患者胸部 CT 表现的系统综述。
Eur Radiol. 2020 Nov;30(11):6129-6138. doi: 10.1007/s00330-020-06975-7. Epub 2020 May 30.
6
Coronavirus Disease 2019 (COVID-19) CT Findings: A Systematic Review and Meta-analysis.新型冠状病毒病 2019(COVID-19)的 CT 表现:系统评价和荟萃分析。
J Am Coll Radiol. 2020 Jun;17(6):701-709. doi: 10.1016/j.jacr.2020.03.006. Epub 2020 Mar 25.
7
CT Manifestations and Clinical Characteristics of 1115 Patients with Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-analysis.新冠肺炎患者 1115 例的 CT 表现及临床特征:系统评价和荟萃分析。
Acad Radiol. 2020 Jul;27(7):910-921. doi: 10.1016/j.acra.2020.04.033. Epub 2020 May 5.
8
Relationship between clinical types and radiological subgroups defined by latent class analysis in 2019 novel coronavirus pneumonia caused by SARS-CoV-2.2019 年新型冠状病毒肺炎(SARS-CoV-2)临床类型与潜伏类别分析定义的放射学亚组之间的关系。
Eur Radiol. 2020 Nov;30(11):6139-6150. doi: 10.1007/s00330-020-06973-9. Epub 2020 May 30.
9
Role of computed tomography in predicting critical disease in patients with covid-19 pneumonia: A retrospective study using a semiautomatic quantitative method.CT 对预测新冠肺炎患者危重症的作用:一项使用半自动定量方法的回顾性研究。
Eur J Radiol. 2020 Sep;130:109202. doi: 10.1016/j.ejrad.2020.109202. Epub 2020 Jul 29.
10
Mobile chest X-ray manifestations of 54 deceased patients with coronavirus disease 2019: Retrospective study.54例新型冠状病毒肺炎死亡患者的移动胸部X线表现:回顾性研究
Medicine (Baltimore). 2020 Nov 13;99(46):e23167. doi: 10.1097/MD.0000000000023167.

引用本文的文献

1
Lung field-based severity score (LFSS): a feasible tool to identify COVID-19 patients at high risk of progressing to critical disease.基于肺野的严重程度评分(LFSS):一种识别有进展为危重症疾病高风险的COVID-19患者的可行工具。
J Thorac Dis. 2024 Sep 30;16(9):5591-5603. doi: 10.21037/jtd-24-544. Epub 2024 Sep 6.
2
The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model.使用融合深度学习模型对合并 COPD 的 COVID-19 患者进行严重程度评估和核酸转阴时间预测。
BMC Pulm Med. 2024 Oct 14;24(1):515. doi: 10.1186/s12890-024-03333-x.
3
Early predictors of intensive care unit admission among COVID-19 patients in Qatar.

本文引用的文献

1
Correction to: Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China.对《基于对来自中国武汉的150例患者数据的分析的2019冠状病毒病死亡的临床预测因素》的更正
Intensive Care Med. 2020 Jun;46(6):1294-1297. doi: 10.1007/s00134-020-06028-z.
2
Contentious issues and evolving concepts in the clinical presentation and management of patients with COVID-19 infectionwith reference to use of therapeutic and other drugs used in Co-morbid diseases (Hypertension, diabetes etc).新型冠状病毒肺炎(COVID-19)感染患者临床表现及管理中的争议问题与不断演变的概念,涉及合并疾病(高血压、糖尿病等)治疗中使用的治疗性药物及其他药物的应用
Diabetes Metab Syndr. 2020 May-Jun;14(3):251-254. doi: 10.1016/j.dsx.2020.03.012. Epub 2020 Mar 25.
3
卡塔尔 COVID-19 患者入住重症监护病房的早期预测指标。
Front Public Health. 2024 Mar 20;12:1278046. doi: 10.3389/fpubh.2024.1278046. eCollection 2024.
4
Artificial intelligence-based analysis of the spatial distribution of abnormal computed tomography patterns in SARS-CoV-2 pneumonia: association with disease severity.基于人工智能的 SARS-CoV-2 肺炎异常计算机断层扫描模式的空间分布分析:与疾病严重程度的关系。
Respir Res. 2024 Jan 10;25(1):24. doi: 10.1186/s12931-024-02673-w.
5
Tocilizumab in severe COVID-19 - A randomized, double-blind, placebo-controlled trial.托珠单抗治疗重症新型冠状病毒肺炎——一项随机、双盲、安慰剂对照试验
Infect Med (Beijing). 2022 Jun;1(2):88-94. doi: 10.1016/j.imj.2022.05.001. Epub 2022 Jun 2.
6
Inflammation associated with lung function abnormalities in COVID-19 survivors.新冠肺炎幸存者肺部功能异常与炎症相关。
BMC Pulm Med. 2023 Jul 1;23(1):235. doi: 10.1186/s12890-023-02521-5.
7
CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict.用于新型冠状病毒肺炎的CT检查:对方案、辐射剂量以及诊断和预测所需数量的系统评价
Taehan Yongsang Uihakhoe Chi. 2021 Nov;82(6):1505-1523. doi: 10.3348/jksr.2021.0096. Epub 2021 Nov 4.
8
The Role of Neutrophil-to-Lymphocyte Ratio in Risk Stratification and Prognostication of COVID-19: A Systematic Review and Meta-Analysis.中性粒细胞与淋巴细胞比值在COVID-19风险分层和预后评估中的作用:一项系统评价和荟萃分析
Vaccines (Basel). 2022 Aug 1;10(8):1233. doi: 10.3390/vaccines10081233.
9
An Entropy-Based Measure of Complexity: An Application in Lung-Damage.一种基于熵的复杂性度量:在肺损伤中的应用
Entropy (Basel). 2022 Aug 14;24(8):1119. doi: 10.3390/e24081119.
10
Predictive Value of Systemic Immune-Inflammation index and Neutrophil-to-Lymphocyte Ratio in Patients with Severe COVID-19.全身免疫炎症指数和中性粒细胞与淋巴细胞比值对重症 COVID-19 患者的预测价值。
Clin Appl Thromb Hemost. 2022 Jan-Dec;28:10760296221111391. doi: 10.1177/10760296221111391.
Clinical Features and Short-term Outcomes of 102 Patients with Coronavirus Disease 2019 in Wuhan, China.中国武汉 102 例 2019 年冠状病毒病患者的临床特征和短期预后。
Clin Infect Dis. 2020 Jul 28;71(15):748-755. doi: 10.1093/cid/ciaa243.
4
The epidemiology, diagnosis and treatment of COVID-19.新型冠状病毒肺炎的流行病学、诊断与治疗。
Int J Antimicrob Agents. 2020 May;55(5):105955. doi: 10.1016/j.ijantimicag.2020.105955. Epub 2020 Mar 28.
5
Clinical considerations for patients with diabetes in times of COVID-19 epidemic.2019冠状病毒病疫情期间糖尿病患者的临床考量
Diabetes Metab Syndr. 2020 May-Jun;14(3):211-212. doi: 10.1016/j.dsx.2020.03.002. Epub 2020 Mar 10.
6
Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease.分析与 2019 年新型冠状病毒病住院患者疾病结局相关的因素。
Chin Med J (Engl). 2020 May 5;133(9):1032-1038. doi: 10.1097/CM9.0000000000000775.
7
The Clinical and Chest CT Features Associated With Severe and Critical COVID-19 Pneumonia.与严重和危重新冠肺炎相关的临床和胸部 CT 特征。
Invest Radiol. 2020 Jun;55(6):327-331. doi: 10.1097/RLI.0000000000000672.
8
Clinical and computed tomographic imaging features of novel coronavirus pneumonia caused by SARS-CoV-2.SARS-CoV-2 引起的新型冠状病毒肺炎的临床和计算机断层扫描影像学特征。
J Infect. 2020 Apr;80(4):394-400. doi: 10.1016/j.jinf.2020.02.017. Epub 2020 Feb 25.
9
Clinical Characteristics of Imported Cases of Coronavirus Disease 2019 (COVID-19) in Jiangsu Province: A Multicenter Descriptive Study.江苏省输入性新型冠状病毒肺炎(COVID-19)病例的临床特征:一项多中心描述性研究。
Clin Infect Dis. 2020 Jul 28;71(15):706-712. doi: 10.1093/cid/ciaa199.
10
Clinical Characteristics of Coronavirus Disease 2019 in China.《中国 2019 年冠状病毒病临床特征》
N Engl J Med. 2020 Apr 30;382(18):1708-1720. doi: 10.1056/NEJMoa2002032. Epub 2020 Feb 28.