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      Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis

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          Abstract

          Purpose

          Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis.

          Methods

          Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes.

          Results

          This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157–1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307–9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg’s funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg’s test P = 0.945 and 0.356, respectively).

          Conclusions

          The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.

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          Clinical Characteristics of Coronavirus Disease 2019 in China

          Wei-jie Guan, Zheng-yi Ni, Yu Hu … (2020)
          Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
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            Re-epithelialization and immune cell behaviour in an ex vivo human skin model

            Ana Rakita, Nenad Nikolić, Michael Mildner … (2020)
            A large body of literature is available on wound healing in humans. Nonetheless, a standardized ex vivo wound model without disruption of the dermal compartment has not been put forward with compelling justification. Here, we present a novel wound model based on application of negative pressure and its effects for epidermal regeneration and immune cell behaviour. Importantly, the basement membrane remained intact after blister roof removal and keratinocytes were absent in the wounded area. Upon six days of culture, the wound was covered with one to three-cell thick K14+Ki67+ keratinocyte layers, indicating that proliferation and migration were involved in wound closure. After eight to twelve days, a multi-layered epidermis was formed expressing epidermal differentiation markers (K10, filaggrin, DSG-1, CDSN). Investigations about immune cell-specific manners revealed more T cells in the blister roof epidermis compared to normal epidermis. We identified several cell populations in blister roof epidermis and suction blister fluid that are absent in normal epidermis which correlated with their decrease in the dermis, indicating a dermal efflux upon negative pressure. Together, our model recapitulates the main features of epithelial wound regeneration, and can be applied for testing wound healing therapies and investigating underlying mechanisms.
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              Fleischner Society: glossary of terms for thoracic imaging.

              David Hansell, Alexander Bankier, Heber MacMahon … (2008)
              Members of the Fleischner Society compiled a glossary of terms for thoracic imaging that replaces previous glossaries published in 1984 and 1996 for thoracic radiography and computed tomography (CT), respectively. The need to update the previous versions came from the recognition that new words have emerged, others have become obsolete, and the meaning of some terms has changed. Brief descriptions of some diseases are included, and pictorial examples (chest radiographs and CT scans) are provided for the majority of terms. (c) RSNA, 2008.
              Article 0 comments Cited 890 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Seyed Salman Zakariaee:
                ORCID: http://orcid.org/0000-0003-2537-3375
                salman_zakariaee@yahoo.com
                Hadi Kazemi-Arpanahi: hadi.kazemi67@gmail.com
                Journal
                Journal ID (nlm-ta): Clin Transl Imaging
                Journal ID (iso-abbrev): Clin Transl Imaging
                Title: Clinical and Translational Imaging
                Publisher: Springer International Publishing (Cham )
                ISSN (Print): 2281-5872
                ISSN (Electronic): 2281-7565
                Publication date (Electronic): 21 July 2022
                Pages: 1-14
                Affiliations
                [1 ]GRID grid.449129.3, ISNI 0000 0004 0611 9408, Department of Medical Physics, Faculty of Paramedical Sciences, , Ilam University of Medical Sciences, ; Ilam, Iran
                [2 ]GRID grid.449129.3, ISNI 0000 0004 0611 9408, Department of Radiology, Faculty of Medicine, , Ilam University of Medical Sciences, ; Ilam, Iran
                [3 ]GRID grid.449129.3, ISNI 0000 0004 0611 9408, Department of Midwifery, Faculty of Nursing and Midwifery, , Ilam University of Medical Sciences, ; Ilam, Iran
                [4 ]Department of Health Information Technology, School of Management and Medical Information Sciences, Abadan University of Medical Sciences, Abadan, Iran
                [5 ]GRID grid.449129.3, ISNI 0000 0004 0611 9408, Department of Health Information Technology, School of Paramedical Sciences, , Ilam University of Medical Sciences, ; Ilam, Iran
                Author information
                http://orcid.org/0000-0003-2537-3375
                Article
                Publisher ID: 512
                DOI: 10.1007/s40336-022-00512-w
                PMC ID: 9302953
                PubMed ID: 35892066
                SO-VID: 537100da-38a1-49a7-9a6f-c4588be0301c
                Copyright © © The Author(s), under exclusive licence to Italian Association of Nuclear Medicine and Molecular Imaging 2022
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 19 April 2022
                Date accepted : 5 July 2022
                Funding
                Funded by: Abadan University of Medical Sciences
                Award ID: 1304
                Award ID: 10 Nov 2021
                Award Recipient :
                Categories
                Subject: Meta-Analysis

                Keywords: covid-19,computed tomography,mortality,ct,ct severity score,pneumonia
                Data availability:
                Keywords: covid-19, computed tomography, mortality, ct, ct severity score, pneumonia

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