Literature DB >> 32475764

An updated meta-analysis of AST and ALT levels and the mortality of COVID-19 patients.

Ying Wang1, Li Shi1, Yadong Wang2, Haiyan Yang3.   

Abstract

Entities:  

Keywords:  Alanine aminotransferase; Aspartate aminotransferase; Coronavirus disease 2019; Mortality

Mesh:

Substances:

Year:  2020        PMID: 32475764      PMCID: PMC7251355          DOI: 10.1016/j.ajem.2020.05.063

Source DB:  PubMed          Journal:  Am J Emerg Med        ISSN: 0735-6757            Impact factor:   2.469


× No keyword cloud information.
To the Editor, With great interest, we read the recent paper titled “Factors associated with mortality in patients with COVID-19. A quantitative evidence synthesis of clinical and laboratory data” by Martins-Filho et al. published in the European Journal of Internal Medicine [1]. This study is extremely interesting. The authors observed that several biomedical markers such as albumin, blood urea nitrogen, creatinine, creatinine kinase, hypersensitive cardiac troponin I (hs-cTnI) and lactate dehydrogenase (LDH) were positively associated with the risk of mortality in coronavirus disease 2019 (COVID-19) patients based on four published studies. But the levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were not observed to be associated with the risk of mortality in COVID-19 patients. Recently, some emerging papers are reporting the association of AST and ALT with the risk of mortality in COVID-19 patients, so an updated meta-analysis was performed on the basis of the last data. We hope that our results will provide comprehensive evidence for the association between AST and ALT levels and the risk of mortality in COVID-19 patients. An electronic search was conducted in PubMed, Web of Science and China National Knowledge Infrastructure (CNKI) until April 30, 2020, using the keywords: ((“coronavirus” or “COVID-19” or “SARS-CoV-2” or “2019-nCoV”) and (“laboratory” or “clinical”) and (“mortality” or “outcome”)). Articles reporting AST and ALT levels for both non-survival and survival COVID-19 patients were included. Articles with potential overlapping reports were excluded (by checking the hospital where the patients came from, the author's organization and other information). The mean and standard deviation were estimated using the sample size, median and interquartile range (IQR) [2]. The pooled effects were presented as standardized mean difference (SMD) and 95% confidence interval (CI). Heterogeneity was checked using the I statistic. We used Stata 11.2 (StataCorp, College Station, TX) to perform the analysis, and P < 0.05 was considered significant. We found a total of 966 records, and 741 remained after the removal of duplicates. 42 remained after reading the title and abstract. After reading the full texts, we excluded 35 studies that did not report AST or ALT levels for both non-survival and survival COVID-19 patients, or reported patients may overlap with other articles. Finally, seven eligible studies were enrolled in this meta-analysis [[3], [4], [5], [6], [7], [8], [9]]. Data were collected from patients admitted to Jinyintan Hospital, Hankou and Caidian branch of Tongji Hospital, Hankou branch of Central Hospital of Wuhan, Tongji Hospital, Renmin Hospital, Zhongnan Hospital, and Xishui Peoples Hospital. The basic characteristics of the included studies are shown in Table 1 .
Table 1

Characteristics of the included studies.

AuthorLocationCasesNon-survival patients
Survival patients
nAge(IQR)MaleALT(U/L)AST(U/L)nAge(IQR)MaleALT(U/L)AST(U/L)
Zhou Fei et al.(PMID: 32171076)China1915469(63–76)38(70%)40(24–51)NR13752(45–58)81(59%)27(15–40)NR
Ruan Qiurong et al.(PMID: 32253449)China1506867(15–81)49(72%)170.8 ± 991.6288.9 ± 1875.58250(44–81)53(65%)48.68 ± 83.140.7 ± 57.8
Deng Yan et al.(PMID: 32209890)China22510969(62–74)73(67%)22(15–34)34(27–47)11640(33–57)51(44%)18.7(13–30.38)22(17.65–31.75)
Wu Chaomin et al.(PMID: 32167524)China844468.5(59.3–75)29(66%)39(20.5–52.5)37(30–52)4050(40.3–56.8)31(78%)35(23.25–55.25)38.5(32.25–57.25)
Chen Tao et al.(PMID: 32217556)China27411368(62–77)83(73%)28(18–47)45(31–67)16151(37–66)88(55%)20(14.8–32)25(20−33)
Wang Lang et al.(PMID: 32240670)China3396576(70–83)39(60%)24(19–49)43(30–68)27468(64–74)127(46%)28(17–43)29(22–43)
Wang Dawei et al.(PMID: 32354360)China1071973(64–81)16(84%)47(22–66)67(38–90)8844.5(35–58.8)41(47%)22(15–34)29(23–41)

All values are n (%), median (IQR), or mean ± SD; ALT, alanine transaminase; AST, aspartate transaminase; NR, not reported.

Characteristics of the included studies. All values are n (%), median (IQR), or mean ± SD; ALT, alanine transaminase; AST, aspartate transaminase; NR, not reported. All the studies we included were from China, with a total of 1370 COVID-19 patients. We observed there was a significant association between the elevated AST levels and an increased risk of mortality in COVID-19 patients (SMD = 0.75, 95% CI: 0.33–1.17, P < 0.001; I  = 89.9%, P = 0.000) using a random-effects model (Fig. 1A). The ALT values showed similar result (SMD = 0.35, 95% CI: 0.13–0.57, P = 0.002; I  = 70.4%, P = 0.002) (Fig. 1B). The results of the leave-one-out sensitivity analysis indicated any single study had no obvious effects on the combined SMD, suggesting our results were robust (Fig. 1C and D). Begg's test (All P > 0.05) and Egger's test (All P > 0.05) suggest no significant publication bias.
Fig. 1

Standardized mean difference and 95% confidence interval (CI) of aspartate aminotransferase (AST) (A) and alanine aminotransferase (ALT) (B), and sensitivity analysis for AST (C) and ALT (D) between non-survival and survival coronavirus disease 2019 patients by random-effects model.

Standardized mean difference and 95% confidence interval (CI) of aspartate aminotransferase (AST) (A) and alanine aminotransferase (ALT) (B), and sensitivity analysis for AST (C) and ALT (D) between non-survival and survival coronavirus disease 2019 patients by random-effects model. In summary, AST and ALT should be considered as predictors of clinical outcomes such as mortality in COVID-19 patients based on the last data. To reach a definitive conclusion, more studies with large sample size are needed to validate the association between the levels of AST and ALT and the risk of mortality in COVID-19 patients in the future.

Funding

This work was supported by a grant from the (grant number 81973105). The funder has no role in the preparation of manuscript and decision to submission.

Declaration of competing interest

None.
  9 in total

1.  Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China.

Authors:  Chaomin Wu; Xiaoyan Chen; Yanping Cai; Jia'an Xia; Xing Zhou; Sha Xu; Hanping Huang; Li Zhang; Xia Zhou; Chunling Du; Yuye Zhang; Juan Song; Sijiao Wang; Yencheng Chao; Zeyong Yang; Jie Xu; Xin Zhou; Dechang Chen; Weining Xiong; Lei Xu; Feng Zhou; Jinjun Jiang; Chunxue Bai; Junhua Zheng; Yuanlin Song
Journal:  JAMA Intern Med       Date:  2020-07-01       Impact factor: 21.873

2.  Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range.

Authors:  Xiang Wan; Wenqian Wang; Jiming Liu; Tiejun Tong
Journal:  BMC Med Res Methodol       Date:  2014-12-19       Impact factor: 4.615

3.  Clinical course and outcome of 107 patients infected with the novel coronavirus, SARS-CoV-2, discharged from two hospitals in Wuhan, China.

Authors:  Dawei Wang; Yimei Yin; Chang Hu; Xing Liu; Xingguo Zhang; Shuliang Zhou; Mingzhi Jian; Haibo Xu; John Prowle; Bo Hu; Yirong Li; Zhiyong Peng
Journal:  Crit Care       Date:  2020-04-30       Impact factor: 9.097

4.  Factors associated with mortality in patients with COVID-19. A quantitative evidence synthesis of clinical and laboratory data.

Authors:  Paulo Ricardo Martins-Filho; Carolina Santos Souza Tavares; Victor Santana Santos
Journal:  Eur J Intern Med       Date:  2020-04-23       Impact factor: 4.487

5.  Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China.

Authors:  Qiurong Ruan; Kun Yang; Wenxia Wang; Lingyu Jiang; Jianxin Song
Journal:  Intensive Care Med       Date:  2020-03-03       Impact factor: 17.440

6.  Clinical characteristics of fatal and recovered cases of coronavirus disease 2019 in Wuhan, China: a retrospective study.

Authors:  Yan Deng; Wei Liu; Kui Liu; Yuan-Yuan Fang; Jin Shang; Ling Zhou; Ke Wang; Fan Leng; Shuang Wei; Lei Chen; Hui-Guo Liu
Journal:  Chin Med J (Engl)       Date:  2020-06-05       Impact factor: 2.628

7.  Coronavirus disease 2019 in elderly patients: Characteristics and prognostic factors based on 4-week follow-up.

Authors:  Lang Wang; Wenbo He; Xiaomei Yu; Dalong Hu; Mingwei Bao; Huafen Liu; Jiali Zhou; Hong Jiang
Journal:  J Infect       Date:  2020-03-30       Impact factor: 6.072

8.  Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study.

Authors:  Tao Chen; Di Wu; Huilong Chen; Weiming Yan; Danlei Yang; Guang Chen; Ke Ma; Dong Xu; Haijing Yu; Hongwu Wang; Tao Wang; Wei Guo; Jia Chen; Chen Ding; Xiaoping Zhang; Jiaquan Huang; Meifang Han; Shusheng Li; Xiaoping Luo; Jianping Zhao; Qin Ning
Journal:  BMJ       Date:  2020-03-26

9.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.

Authors:  Fei Zhou; Ting Yu; Ronghui Du; Guohui Fan; Ying Liu; Zhibo Liu; Jie Xiang; Yeming Wang; Bin Song; Xiaoying Gu; Lulu Guan; Yuan Wei; Hui Li; Xudong Wu; Jiuyang Xu; Shengjin Tu; Yi Zhang; Hua Chen; Bin Cao
Journal:  Lancet       Date:  2020-03-11       Impact factor: 79.321

  9 in total
  5 in total

1.  Predictive Model of Severity in SARS CoV-2 Patients at Hospital Admission Using Blood-Related Parameters.

Authors:  Laura Criado Gómez; Santiago Villanueva Curto; Maria Belén Pérez Sebastian; Begoña Fernández Jiménez; Melisa Duque Duniol
Journal:  EJIFCC       Date:  2021-06-29

2.  A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19.

Authors:  Vasileios C Pezoulas; Konstantina D Kourou; Costas Papaloukas; Vassiliki Triantafyllia; Vicky Lampropoulou; Eleni Siouti; Maria Papadaki; Maria Salagianni; Evangelia Koukaki; Nikoletta Rovina; Antonia Koutsoukou; Evangelos Andreakos; Dimitrios I Fotiadis
Journal:  Diagnostics (Basel)       Date:  2021-12-28

Review 3.  What GI Physicians Need to Know During COVID-19 Pandemic.

Authors:  Paul J Thuluvath; Joseph J Alukal; Nishal Ravindran; Sanjaya K Satapathy
Journal:  Dig Dis Sci       Date:  2020-10-05       Impact factor: 3.199

Review 4.  Liver Fibrosis Scores and Hospitalization, Mechanical Ventilation, Severity, and Death in Patients with COVID-19: A Systematic Review and Dose-Response Meta-Analysis.

Authors:  Menglu Liu; Kaibo Mei; Ziqi Tan; Shan Huang; Fuwei Liu; Chao Deng; Jianyong Ma; Peng Yu; Xiao Liu
Journal:  Can J Gastroenterol Hepatol       Date:  2022-03-29

5.  Liver Function Tests in COVID-19: Assessment of the Actual Prognostic Value.

Authors:  Urszula Tokarczyk; Krzysztof Kaliszewski; Anna Kopszak; Łukasz Nowak; Karolina Sutkowska-Stępień; Maciej Sroczyński; Monika Sępek; Agata Dudek; Dorota Diakowska; Małgorzata Trocha; Damian Gajecki; Jakub Gawryś; Tomasz Matys; Justyna Maciejiczek; Valeriia Kozub; Roman Szalast; Marcin Madziarski; Anna Zubkiewicz-Zarębska; Krzysztof Letachowicz; Katarzyna Kiliś-Pstrusińska; Agnieszka Matera-Witkiewicz; Michał Pomorski; Marcin Protasiewicz; Janusz Sokołowski; Barbara Adamik; Krzysztof Kujawa; Adrian Doroszko; Katarzyna Madziarska; Ewa Anita Jankowska
Journal:  J Clin Med       Date:  2022-08-01       Impact factor: 4.964

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.