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Meta-Analysis
. 2021 Mar 12;100(10):e24999.
doi: 10.1097/MD.0000000000024999.

Meta-analysis of procalcitonin as a predictor for acute kidney injury

Yunxia Feng  1 , Haiyan He  2 , Chao Jia  2 , Zhihua Xu  2 , Yuan Li  3 , Dan Liao  1
Affiliations
Meta-Analysis

Meta-analysis of procalcitonin as a predictor for acute kidney injury

Yunxia Feng et al. Medicine (Baltimore). .

Abstract

Background: Procalcitonin (PCT) was used for predicting the development of acute kidney injury (AKI) in several studies recently. We aimed to investigate the accuracy of PCT for predicting AKI in this study.

Methods: Studies that assessed the predictive performance of PCT for the development of AKI in adult patients were searched from Medline, Embase, and the Cochrane Library from inception to June 2020. We calculated the pooled sensitivities and specificities and the area under the summary receiver-operating characteristic (SROC) curves. I2 was used to test the heterogeneity and the potential heterogeneity was investigated by meta-regression.

Results: In total, 9 of 119 studies with 4852 patients were included, 1272 were diagnosed with AKI. In the overall analysis, the area under the SROC curve was 0.82 (95% CI, 0.79-0.85) and the pooled sensitivity and specificity were 0.76 (95% confidence interval [CI], 0.64-0.85) and 0.75 (95% CI, 0.61-0.86), respectively. In the subgroup analysis among septic patients, the pooled sensitivity and specificity were 0.59 (95% CI, 0.29-0.84) and 0.53 (95% CI, 0.31-0.74), and the area under the SROC was 0.57 (95% CI, 0.53-0.62).

Conclusion: PCT may be a potential predictor for the development of AKI.

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Conflict of interest statement

The authors have no conflicts of interests to disclose.

Figures

Figure 1
Figure 1
Flowchart of study selection.
Figure 2
Figure 2
Quality Assessment of Diagnostic Accuracy Studies criteria for the included studies.
Figure 3
Figure 3
Deeks funnel plot of publication bias.
Figure 4
Figure 4
Forest plot of the sensitivity and specificity for studies using procalcitonin to predict AKI.
Figure 5
Figure 5
Summary receiver operating characteristic curves (receiver operating characteristic curve is a two-dimensional indicator including information of sensitivity and specificity, larger of area under receiver operating characteristic curve means better diagnostic performance) and the corresponding 95% confidence contours and 95% prediction contours (A plot for all included studies, B plot for studies of septic patients).
Figure 6
Figure 6
Forest plot of the diagnostic odds ratio (diagnostic odds ratio if the ratio of the odds of positivity in disease relative to the odds of positivity in the non-diseased, it can be calculated as following: (TP/FN)/(FP/TN)) for the use of PCT in predicting AKI (A plot for all included studies, B plot for studies of septic patients).
Figure 7
Figure 7
Forest plot of meta regression (meta regression was performed to investigate the sources of heterogeneity).
Figure 8
Figure 8
Forest plot of the sensitivity and specificity for studies using procalcitonin to predict AKI among patients with sepsis.
See this image and copyright information in PMC

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