Literature DB >> 33254281

Is creatinine an independent risk factor for predicting adverse outcomes in COVID-19 patients?

Jian Wu1, Li Shi1, Peihua Zhang1, Yadong Wang2, Haiyan Yang1.   

Abstract

Entities:  

Keywords:  COVID-19; adjusted effect estimates; creatinine; meta-analysis

Mesh:

Substances:

Year:  2020        PMID: 33254281      PMCID: PMC7744867          DOI: 10.1111/tid.13539

Source DB:  PubMed          Journal:  Transpl Infect Dis        ISSN: 1398-2273


× No keyword cloud information.
To the Editor, Recently, a meta‐analysis by Zheng et al reported that increased creatinine (Cr) was significantly associated with critical or mortal outcomes in coronavirus disease 2019 (COVID‐19) patients based on unadjusted effect estimates (odds ratio (OR): 5.19, 95% confidence interval (CI): 2.19‐12.83). To our knowledge, in a univariate model, the adverse outcomes of COVID‐19 patients are significantly associated with Cr, , , , while the significant association disappeared in a multivariate model. Several confounders (age, gender and co‐existing comorbidities) might affect the association of creatinine with adverse outcomes in COVID‐19 patients. Therefore, a quantitative meta‐analysis based on adjusted effect estimates was performed to clarify the association of Cr value with adverse outcomes in COVID‐19 patients. All the related articles were searched from PubMed, Web of Science and EMBASE on 23 July 2020 by using the following key‐words: “creatinine or laboratory”, “2019‐nCoV or SARS‐CoV‐2 or novel coronavirus 2019 or COVID‐19 or coronavirus disease 2019” and “adverse outcome or severe or critical or severity or mortality or fatality or death”. The meta‐analysis was processed by using Stata14.2. I2 statistics was used to assess heterogeneity. The value of I2 > 50% indicated evident heterogeneity across articles and a random‐effects model was selected; otherwise a fixed‐effects model was used. Meta‐regression and sensitivity analysis were conducted to assess the source of heterogeneity and evaluate the robustness of our findings respectively. Begg's test and Egger's test were used in the assessment of publication bias. Fifteen articles with 7,420 patients were included in this meta‐analysis. The main characteristics are shown in Table S1. Our results showed that increased Cr values were significantly associated with adverse COVID‐19 patients (pooled effect: 1.10, 95% CI: 1.04‐1.16, P < .001, random‐effects model) (Figure 1A). Meta‐regression demonstrated that hypertension might be a source of heterogeneity (P = .037). Heterogeneity among studies adjusted for hypertension was significantly lower (I2 = 11.3%, P = .343, pooled effect = 1.03, 95% CI = 1.01‐1.05) (Figure 1B). The results of sensitivity analysis demonstrated the pooled effect was robust by excluding each study successively (Figure 1C). Publication bias was not presented in Begg's test (P = .381), but presented in the Egger's test (P = .002).
Figure 1

Forest plot of the pooled effect (A) and Subgroup analysis based on whether hypertension was included as an adjustment factor (1, yes; 0, no) of the enrolled studies (B); Sensitivity analysis (C)

Forest plot of the pooled effect (A) and Subgroup analysis based on whether hypertension was included as an adjustment factor (1, yes; 0, no) of the enrolled studies (B); Sensitivity analysis (C) This study has some limitations. First, although this meta‐analysis was based on adjusted effect estimates, the adjusted factors were different among studies. Second, we tried to screen eligible studies through three databases but the publication bias still existed in Egger's test. Third, most of the included articles are retrospective, and only two are prospective, so we did not perform subgroup analysis by study design. In conclusion, increased Cr value was an independent risk factor for predicting adverse outcomes in COVID‐19 patients. Further study based on prospective data with larger sample size is required to confirm the conclusion of our meta‐analysis.

CONFLICTS OF INTEREST

All authors report that they have no potential conflicts of interest.

AUTHORS CONTRIBUTION

W. Y. D. and Y. H. Y. designed the analysis; W. J. and Z. P. H. extracted the data; W. J. performed the analysis; W. J. and S. L. contributed to the statistical analyses and interpretation; W. J. drafted the manuscript, which was modified by W. Y. D. and Y. H. Y. All authors read and approved the final manuscript.

Funding information

This study was supported by a grant from the National Natural Science Foundation of China (No. 81973105). Table S1 Click here for additional data file.
  6 in total

1.  Prognostic significance of cardiac injury in COVID-19 patients with and without coronary artery disease.

Authors:  Hasan Ali Barman; Adem Atici; Irfan Sahin; Gokhan Alici; Esra Aktas Tekin; Ömer Faruk Baycan; Fatih Ozturk; Ersan Oflar; Sevil Tugrul; Mustafa Baran Yavuz; Fatma Betul Celik; Aysu Oktay; Haluk Vahaboglu; Mine Adas; Namigar Turgut; Ertugrul Okuyan; Mustafa Taner Yildirmak; Baris Gungor
Journal:  Coron Artery Dis       Date:  2021-08-01       Impact factor: 1.439

2.  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

3.  Fasting blood glucose predicts the occurrence of critical illness in COVID-19 patients: A multicenter retrospective cohort study.

Authors:  Qin Liu; Huai Chen; Jianyu Li; Xiaoyan Huang; Lihua Lai; Shenghao Li; Qingsi Zeng
Journal:  J Infect       Date:  2020-07-08       Impact factor: 6.072

4.  Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease.

Authors:  Wei Liu; Zhao-Wu Tao; Lei Wang; Ming-Li Yuan; Kui Liu; Ling Zhou; Shuang Wei; Yan Deng; Jing Liu; Hui-Guo Liu; Ming Yang; Yi Hu
Journal:  Chin Med J (Engl)       Date:  2020-05-05       Impact factor: 2.628

5.  Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis.

Authors:  Zhaohai Zheng; Fang Peng; Buyun Xu; Jingjing Zhao; Huahua Liu; Jiahao Peng; Qingsong Li; Chongfu Jiang; Yan Zhou; Shuqing Liu; Chunji Ye; Peng Zhang; Yangbo Xing; Hangyuan Guo; Weiliang Tang
Journal:  J Infect       Date:  2020-04-23       Impact factor: 6.072

6.  Is creatinine an independent risk factor for predicting adverse outcomes in COVID-19 patients?

Authors:  Jian Wu; Li Shi; Peihua Zhang; Yadong Wang; Haiyan Yang
Journal:  Transpl Infect Dis       Date:  2020-12-12
  6 in total
  3 in total

1.  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

2.  Limited value of neutrophil-to-lymphocyte ratio and serum creatinine as point-of-care biomarkers of disease severity and infection mortality in patients hospitalized with COVID-19.

Authors:  Abdisa Tufa; Tewodros Haile Gebremariam; Tsegahun Manyazewal; Yidnekachew Asrat; Tewodros Getinet; Tsegaye Gebreyes Hundie; Dominic-Luc Webb; Per M Hellström; Solomon Genet
Journal:  PLoS One       Date:  2022-10-06       Impact factor: 3.752

3.  Is creatinine an independent risk factor for predicting adverse outcomes in COVID-19 patients?

Authors:  Jian Wu; Li Shi; Peihua Zhang; Yadong Wang; Haiyan Yang
Journal:  Transpl Infect Dis       Date:  2020-12-12
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.