基于胸部CT的实验室验证COVID-19预后预测：38,051例患者的回顾性分析

封面

如何引用文章

全文:

详细

论证：在目前的流行病学情况下，胸部器官CT（胸部器官的计算机断层扫描）在该病的诊断中起着重要的作用。临床和CT数据使医生能够快速判断COVID-19患者的存在概率和预后。

目的：预测实验室证实的COVID-19患者的结果，基于胸部器官CT，使用肺实质损伤程度半定量视觉量表（CT0—CT4量表）。

材料与方法。对2020年3月1日至2020年7月30日期间从统一医疗信息和分析服务处（UMIAS）和从统一放射信息服务处（ERIS）卸载的医疗记录和协议进行了回顾性分析。本研究纳入了根据ICD-10诊断为U07.1患者的病历（实验室确诊新型冠状病毒感染病例）。从2020年3月1日至7月30日，这些患者在疑似COVID-19引起的社区获得性肺炎的内科医生的指导下接受胸部器官CT检查；实验室检查和胸部器官计算机断层扫描之间最长允许的时间不超过5天。每位病人的随访期由CT日期起计最少为30天。这项研究是在向莫斯科成年人口提供初级医疗保健的48个医疗机构中进行的。本研究不包括截至2020年7月30日COVID-19聚合酶链反应试验结果为阴性的患者。CT0-CT4量表推荐在俄罗斯联邦用于评估疑似COVID-19病例肺实质损害的程度。

结果。样本量为38,051例。根据研究结果，CT-4类患者的死亡风险比CT-0类患者高3倍。Kaplan-Meyer 生存曲线显示，CT-3类患者的存活比例比CT0-CT2类患者低3倍（HR = 2.94）。此外，发现了CT的初始类别越高，恶化的风险越低。根据胸部器官CT显示，住院时间随类别的增加而减少。

结果。CT0-CT4的视觉尺度可用于预测疑似COVID-19患者的预后（住院和死亡），如果患者在初级卫生保健的基础上接受了胸部器官CT检查。

作者简介

Sergey Morozov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: morozov@npcmr.ru
ORCID iD: 0000-0001-6545-6170
SPIN 代码: 8542-1720

MD, PhD

俄罗斯联邦, Moscow

Valeria Chernina

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: v.chernina@npcmr.ru
ORCID iD: 0000-0002-0302-293X
SPIN 代码: 8896-8051

MD

俄罗斯联邦, Moscow

Andreevich Blokhin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

Email: i.blokhin@npcmr.ru
ORCID iD: 0000-0002-2681-9378
SPIN 代码: 3306-1387

MD

俄罗斯联邦, Moscow

Victor Gombolevskiy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department

编辑信件的主要联系方式.
Email: g_victor@mail.ru
ORCID iD: 0000-0003-1816-1315
SPIN 代码: 6810-3279

MD, PhD, MPH

俄罗斯联邦, Moscow

参考

  1. World Health Organization. Timeline of WHO’s response to COVID-19 [Internet]. WHO; 2020 [cited 2020 Sept 9]. Available from: https://www.who.int/news-room/detail/29-06-2020-covidtimeline
  2. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533–534. doi: 10.1016/S1473-3099(20)30120-1
  3. Zhang R, Ouyang H, Fu L, et al. CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city. Eur Radiol. 2020;30(8):4417–4426. doi: 10.1007/s00330-020-06854-1
  4. Silverstein WK, Stroud L, Cleghorn GE, Leis JA. First imported case of 2019 novel coronavirus in Canada, presenting as mild pneumonia. Lancet. 2020;395(10225):734. doi: 10.1016/S0140-6736(20)30370-6
  5. Yoon SH, Lee KH, Kim JY, et al. Chest radiographic and CT findings of the 2019 Novel Coronavirus Disease (COVID-19): analysis of nine patients treated in Korea. Korean J Radiol. 2020;21(4):494-500. doi: 10.3348/kjr.2020.0132
  6. Sverzellati N, Milanese G, Milone F, et al. Integrated radiologic algorithm for COVID-19 pandemic. J Thorac Imaging. 2020;35(4):228–233. doi: 10.1097/RTI.0000000000000516
  7. Colombi D, Bodini FC, Petrini M, et al. Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology. 2020;296(2):E86–E96. doi: 10.1148/radiol.2020201433
  8. Li K, Fang Y, Li W, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol. 2020;30(8):4407–4416. doi: 10.1007/s00330-020-06817-6
  9. Wynants L, van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369:M1328. doi: 10.1136/bmj.m1328
  10. Morozov SP, Protsenko DN, Smetanina SV, et al. Radiation diagnostics of coronavirus disease (COVID-19): organization, methodology, interpretation of results: Preprint No. CDT – 2020 – II. Version 2 from 17.04.2020. Series «Best practices of radiation and instrumental diagnostics». Issue 65. Moscow: GBUZ «NPKTS DIT DZM»; 2020. 78 р. (In Russ).
  11. Sinitsyn VE, Tyurin IE, Mitkov VV. Consensus Guidelines of Russian Society of Radiology (RSR) and Russian Association of Specialists in Ultrasound Diagnostics in Medicine (RASUDM) «Role of Imaging (X-ray, CT and US) in Diagnosis of COVID-19 Pneumonia» (version 2). Journal of radiology and nuclear medicine. 2020;101(2):72–89. (In Russ). doi: 10.20862/0042-4676-2020-101-2-72-89
  12. Morozov SP, Gombolevskiy VA, Cherninа VY, et al. Prediction of lethal outcomes in COVID-19 cases based on the results chest computed tomography. Tuberculosis and Lung Diseases. 2020;98(6):7–14. (In Russ). doi: 10.21292/2075-1230-2020-98-6-7-14
  13. Khristenko E, von Stackelberg O, Kauczor HU, et al. Ctpatterns in COVID-19 associated pneumonia – unification of radiological reports based on glossary of Fleischner society. REJR. 2020;10(1):16–26. (In Russ). doi: 10.21569/2222-7415-2020-10-1-16-26
  14. Raptis CA, Hammer MM, Short RG, et al. Chest CT and coronavirus disease (COVID-19): a critical review of the literature to date. AJR Am J Roentgenol. 2020;215(4):839–842. doi: 10.2214/AJR.20.23202
  15. Yuan M, Yin W, Tao Z, et al. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS One. 2020;15(3):E0230548. doi: 10.1371/journal.pone.0230548
  16. Petrikov SS, Popugaev KА, Barmina TG, et al. Comparison of clinical data and computed tomography semiotics of the lungs in COVID-19. Tuberculosis and Lung Diseases. 2020;98(7):14–25. (In Russ). doi: 10.21292/2075-1230-2020-98-7-14-25
  17. Xu PP, Tian RH, Luo S, et al. Risk factors for adverse clinical outcomes with COVID-19 in China: a multicenter, retrospective, observational study. Theranostics. 2020;10(14):6372–6383. doi: 10.7150/thno.46833
  18. Xiong Y, Sun D, Liu Y, et al. Clinical and High-Resolution CT Features of the COVID-19 Infection: Comparison of the Initial and Follow-up Changes. Invest Radiol. 2020;55(6):332–339. doi: 10.1097/RLI.0000000000000674

补充文件

附件文件
动作
1. JATS XML
下载
2. 图 1抽样生成的框图。

下载 (289KB)
索引源数据
3. 图 2CT0-CT4分级Kaplan-Meier的总体生存曲线（p<0.0001）。

下载 (120KB)
索引源数据
4. 图 3与基线水平相比，胸部计算机断层扫描显示的恶化时间的Kaplan-Meyer曲线（p<0.0001）。

下载 (129KB)
索引源数据
5. 图 4从胸部器官首次计算机断层扫描到住院的时间数据的Kaplan-Meyer曲线（p<0.0001）。

下载 (139KB)
索引源数据
6. Video-presentation
预览 
索引源数据

版权所有 © Morozov S., Chernina V., Blokhin A., Gombolevskiy V., 2020

Creative Commons License
此作品已接受知识共享署名-非商业性使用-禁止演绎 4.0国际许可协议的许可。

##common.cookie##