Erratum to risk factors and predictors associated with the severity of COVID-19 in China: a systematic review, meta-analysis, and meta-regression

Zhang, Tao; Huang, Weisen; Guan, Weijun; Hong, Ziying; Gao, Jiabo; Gao, Guoying; Wu, Guofeng; Qin, Yinyin

doi:10.21037/jtd-2021-06

J Thorac Dis

2021

DOI: 10.21037/jtd-2021-06

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Erratum to risk factors and predictors associated with the severity of COVID-19 in China: a systematic review, meta-analysis, and meta-regression

Tao Zhang¹,

Weisen Huang²,

Weijun Guan³

et al.

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Cited by 17 publications

References 32 publications

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Eosinophils and COVID-19: diagnosis, prognosis, and vaccination strategies

Rosenberg

Foster

2021

Semin Immunopathol

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The unprecedented impact of the coronavirus disease 2019 (COVID-19) pandemic has resulted in global challenges to our health-care systems and our economic security. As such, there has been significant research into all aspects of the disease, including diagnostic biomarkers, associated risk factors, and strategies that might be used for its treatment and prevention. Toward this end, eosinopenia has been identified as one of many factors that might facilitate the diagnosis and prognosis of severe COVID-19. However, this finding is neither definitive nor pathognomonic for COVID-19. While eosinophil-associated conditions have been misdiagnosed as COVID-19 and others are among its reported complications, patients with pre-existing eosinophil-associated disorders (e.g., asthma, eosinophilic gastrointestinal disorders) do not appear to be at increased risk for severe disease; interestingly, several recent studies suggest that a diagnosis of asthma may be associated with some degree of protection. Finally, although vaccine-associated aberrant inflammatory responses, including eosinophil accumulation in the respiratory tract, were observed in preclinical immunization studies targeting the related SARS-CoV and MERS-CoV pathogens, no similar complications have been reported clinically in response to the widespread dissemination of either of the two encapsulated mRNA-based vaccines for COVID-19.

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Eosinophils and COVID-19: diagnosis, prognosis, and vaccination strategies

Rosenberg

Foster

2021

Semin Immunopathol

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Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results

et al. 2021

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Purpose To evaluate whether early chest computed tomography (CT) lesions quantified by an artificial intelligence (AI)based commercial software and blood test values at the initial presentation can differentiate the severity of COVID-19 pneumonia.Materials and methods This retrospective study included 100 SARS-CoV-2-positive patients with mild (n = 23), moderate (n = 37) or severe (n = 40) pneumonia classified according to the Japanese guidelines. Univariate Kruskal-Wallis and multivariate ordinal logistic analyses were used to examine whether CT parameters (opacity score, volume of opacity, % opacity, volume of high opacity, % high opacity and mean HU total on CT) as well as blood test parameters [procalcitonin, estimated glomerular filtration rate (eGFR), C-reactive protein, % lymphocyte, ferritin, aspartate aminotransferase, lactate dehydrogenase, alanine aminotransferase, creatine kinase, hemoglobin A1c, prothrombin time, activated partial prothrombin time (APTT), white blood cell count and creatinine] differed by disease severity. Results All CT parameters and all blood test parameters except procalcitonin and APPT were significantly different among mild, moderate and severe groups. By multivariate analysis, mean HU total and eGFR were two independent factors associated with severity (p < 0.0001). Cutoff values for mean HU total and eGFR were, respectively, − 801 HU and 77 ml/min/1.73 m 2 between mild and moderate pneumonia and − 704 HU and 53 ml/min/1.73 m 2 between moderate and severe pneumonia. ConclusionThe mean HU total of the whole lung, determined by the AI algorithm, and eGFR reflect the severity of COVID-19 pneumonia.

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COVID-19 and liver dysfunction: Epidemiology, association and potential mechanisms

Song

Liu

et al. 2022

Clinics and Research in Hepatology and Gastroenterology

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Erratum to risk factors and predictors associated with the severity of COVID-19 in China: a systematic review, meta-analysis, and meta-regression

Cited by 17 publications

References 32 publications

Eosinophils and COVID-19: diagnosis, prognosis, and vaccination strategies

Eosinophils and COVID-19: diagnosis, prognosis, and vaccination strategies

Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results

COVID-19 and liver dysfunction: Epidemiology, association and potential mechanisms

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