OBJECTIVE Serum cystatin-c is a protein that is filtered freely through the glomerulus and reabsorbed and degraded by proximal tubular cells and can be used as a biomarker of renal function. Its levels rise during the third trimester and decrease in the postpartum period. The purpose of the present meta-analysis is to assess the performance of serum cystatin-c for the prediction of preeclampsia. DESIGN AND METHODS We used the Medline, Scopus, Clinicaltrials.gov, EMBASE, Cochrane Central Register of Controlled Trials CENTRAL and Google Scholar. We selected all observational studies (both prospective and retrospective) that investigated the accuracy of cystatin-c in predicting preeclampsia. Statistical meta-analysis was performed with the RevMan 5.3 and Stata/IC 13.0 software, using hierarchical models to develop the SROC curve. RESULTS The quantitative synthesis was based in 27 studies with a total number of 2,320 women. Serum cystatin-c levels were higher in preeclamptic women compared to healthy pregnant controls (MD: 0.40 mg/l, 95% CI [0.33, 0.46]). The pooled sensitivity of serum cystatin-c for the prediction of preeclampsia was 0.85 (95% CI [0.79-0.89]) and the pooled specificity 0.84 (95% CI [0.77-0.90]. Fagan's nomogram indicated that the post-test probability increased to 14% (positive test) and decreased to 1% (negative test), when the pre-test probability was set at 3%. CONCLUSIONS According to the findings of our study serum cystatin-c seems to be a promising biomarker for the detection of preeclampsia during the third trimester of pregnancy. Therefore, its implementation in future predictive models in the field is recommended.
Serum cystatin-c as predictive factor of preeclampsia: A meta-analysis of 27 observational studies.
I. Bellos,Georgia Fitrou,G. Daskalakis,N. Papantoniou,V. Pergialiotis
Published 2019 in Pregnancy Hypertension
ABSTRACT
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- Publication year
2019
- Venue
Pregnancy Hypertension
- Publication date
2019-04-01
- Fields of study
Medicine
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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