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Chest Computed Tomography for the Diagnosis of Patients with Coronavirus Disease 2019 (COVID-19): A Rapid Review and Meta-Analysis
Meng Lv; Mengshu Wang; Nan Yang; Xufei Luo; Wei Li; Xin Chen; Yunlan Liu; Mengjuan Ren; Xianzhuo Zhang; Ling Wang; Yanfang Ma; Junqiang Lei; Toshio Fukuoka; Hyeong Sik Ahn; Myeong Soo Lee; Zhengxiu Luo; Yaolong Chen; Enmei Liu; Jinhui Tian; Xiaohui Wang.
Afiliación
  • Meng Lv; Lanzhou University
  • Mengshu Wang; Department of Radiology, The First Affiliated Hospital, Lanzhou University
  • Nan Yang; Department of Respiratory Medicine; Children's Hospital of Chongqing Medical University
  • Xufei Luo; School of Public Health of Lanzhou University, Lanzhou
  • Wei Li; Department of Radiology; Children's Hospital of Chongqing Medical University
  • Xin Chen; Department of Radiology; Children's Hospital of Chongqing Medical University
  • Yunlan Liu; School of Public Health, Lanzhou University
  • Mengjuan Ren; School of Public Health, Lanzhou University
  • Xianzhuo Zhang; The First School of Clinical Medicine, Lanzhou University
  • Ling Wang; School of Public Health, Lanzhou University
  • Yanfang Ma; Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University
  • Junqiang Lei; Department of Radiology, The First Affiliated Hospital, Lanzhou University
  • Toshio Fukuoka; Emergency and Critical Care Center, the Department of General Medicine, Department of Research and Medical Education at Kurashiki Central Hospital
  • Hyeong Sik Ahn; Department of Preventive Medicine, Korea University College of Medicine
  • Myeong Soo Lee; Korea Institute of Oriental Medicine, Daejeon, Korea
  • Zhengxiu Luo; Department of Respiratory Medicine; Children's Hospital of Chongqing Medical University
  • Yaolong Chen; Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University
  • Enmei Liu; Department of Respiratory Medicine; Children's Hospital of Chongqing Medical University
  • Jinhui Tian; Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University
  • Xiaohui Wang; School of Public Health, Lanzhou University
Preprint en Inglés | medRxiv | ID: ppmedrxiv-20064733
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ABSTRACT
BackgroundThe outbreak of the coronavirus disease 2019 (COVID-19) has had a massive impact on the whole world. Computed tomography (CT) has been widely used in the diagnosis of this novel pneumonia. This study aims to understand the role of CT for the diagnosis and the main imaging manifestations of patients with COVID-19. MethodsWe conducted a rapid review and meta-analysis on studies about the use of chest CT for the diagnosis of COVID-19. We comprehensively searched databases and preprint servers on chest CT for patients with COVID-19 between 1 January 2020 and 31 March 2020. The primary outcome was the sensitivity of chest CT imaging. We also conducted subgroup analyses and evaluated the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. ResultsA total of 104 studies with 5694 patients were included. Using RT-PCR results as reference, a meta-analysis based on 64 studies estimated the sensitivity of chest CT imaging in COVID-19 was 99% (95% CI, 0.97-1.00). If case reports were excluded, the sensitivity in case series was 96% (95% CI, 0.93-0.99). The sensitivity of CT scan in confirmed patients under 18 years old was only 66% (95% CI, 0.11-1.00). The most common imaging manifestation was ground-glass opacities (GGO) which was found in 75% (95% CI, 0.68-0.82) of the patients. The pooled probability of bilateral involvement was 84% (95% CI, 0.81-0.88). The most commonly involved lobes were the right lower lobe (84%, 95% CI, 0.78-0.90) and left lower lobe (81%, 95% CI, 0.74-0.87). The quality of evidence was low across all outcomes. ConclusionsIn conclusion, this meta-analysis indicated that chest CT scan had a high sensitivity in diagnosis of patients with COVID-19. Therefore, CT can potentially be used to assist in the diagnosis of COVID-19.
Texto completo: Disponible Colección: Preprints Base de datos: medRxiv Tipo de estudio: Experimental_studies / Estudio pronóstico / Review Idioma: Inglés Año: 2020 Tipo del documento: Preprint
Texto completo: Disponible Colección: Preprints Base de datos: medRxiv Tipo de estudio: Experimental_studies / Estudio pronóstico / Review Idioma: Inglés Año: 2020 Tipo del documento: Preprint
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