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Meta-Analysis
. 2019 Jun;98(23):e15973.
doi: 10.1097/MD.0000000000015973.

Base excess in predicting the prognosis of patients with paraquat poisoning: A meta-analysis

Feng Wei Zhang  1 , Jie Gao, Su Li Zhang, Cheng Pu Wu, Yong Li, Wen Jing Bai, Shun Yi Feng
Affiliations
Meta-Analysis

Base excess in predicting the prognosis of patients with paraquat poisoning: A meta-analysis

Feng Wei Zhang et al. Medicine (Baltimore). 2019 Jun.

Abstract

Background: Although the prognostic significance of base excess (BE) in patients with paraquat (PQ) poisoning has been investigated for several years, the results remain controversial. Thus, we performed for the first time a comprehensive meta-analysis to explore the value of BE in predicting the prognosis of patients with PQ poisoning.

Methods: We searched PubMed, EMBase, Web of Science, ScienceDirect, Cochrane Library, and the Chinese National Knowledge Infrastructure to identify all relevant papers that were published up to August 2018. The data were extracted for pooled analysis, heterogeneity testing, sensitivity analysis, publication bias analysis, and subgroup analysis.

Results: Pooled analysis revealed that a decreased BE is correlated with poor mortality (pooled OR = 21.358, 95% CI: 12.716-35.873, P < .001). Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 78% (95% CI: 0.66-0.86), 88% (95% CI: 0.66-0.97), 6.6 (95% CI: 2.2-19.9), 0.25 (95% CI: 0.18-0.36), and 26 (10-69), respectively. No publication bias was detected by Egger test (P = .263) and Begg test (P = .462). Sensitivity analyses indicated no important differences among the estimates of effects.

Conclusion: Our findings show that BE is useful for predicting the prognosis of PQ poisoning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the literature search and study selection for this meta-analysis.
Figure 2
Figure 2
Forest plot of the association between BE and mortality.
Figure 3
Figure 3
Summary ROC (SROC) curve for the 5 included studies. Numbers in brackets are 95% CIs. AUC = area under ROC curve, ROC = receiver operating characteristic, SENS = sensitivity, SPEC = specificity.
Figure 4
Figure 4
Funnel plot of the potential publication bias in the prognosis prediction value of BE for mortality. (A) Begg test; and (B) Egger test.
Figure 5
Figure 5
Sensitivity analysis of the correlation between BE and mortality.
See this image and copyright information in PMC

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