Prediction of pre‐eclampsia: review of reviews

Abstract

Objective Primary studies and systematic reviews provide estimates of varying accuracy for different factors in the prediction of pre‐eclampsia. The aim of this study was to review published systematic reviews to collate evidence on the ability of available tests to predict pre‐eclampsia, to identify high‐value avenues for future research and to minimize future research waste in this field. Methods MEDLINE, EMBASE and The Cochrane Library including DARE (Database of Abstracts of Reviews of Effects) databases, from database inception to March 2017, and bibliographies of relevant articles were searched, without language restrictions, for systematic reviews and meta‐analyses on the prediction of pre‐eclampsia. The quality of the included reviews was assessed using the AMSTAR tool and a modified version of the QUIPS tool. We evaluated the comprehensiveness of search, sample size, tests and outcomes evaluated, data synthesis methods, predictive ability estimates, risk of bias related to the population studied, measurement of predictors and outcomes, study attrition and adjustment for confounding. Results From 2444 citations identified, 126 reviews were included, reporting on over 90 predictors and 52 prediction models for pre‐eclampsia. Around a third (n = 37 (29.4%)) of all reviews investigated solely biochemical markers for predicting pre‐eclampsia, 31 (24.6%) investigated genetic associations with pre‐eclampsia, 46 (36.5%) reported on clinical characteristics, four (3.2%) evaluated only ultrasound markers and six (4.8%) studied a combination of tests; two (1.6%) additional reviews evaluated primary studies investigating any screening test for pre‐eclampsia. Reviews included between two and 265 primary studies, including up to 25 356 688 women in the largest review. Only approximately half (n = 67 (53.2%)) of the reviews assessed the quality of the included studies. There was a high risk of bias in many of the included reviews, particularly in relation to population representativeness and study attrition. Over 80% (n = 106 (84.1%)) summarized the findings using meta‐analysis. Thirty‐two (25.4%) studies lacked a formal statement on funding. The predictors with the best test performance were body mass index (BMI) > 35 kg/m2, with a specificity of 92% (95% CI, 89–95%) and a sensitivity of 21% (95% CI, 12–31%); BMI > 25 kg/m2, with a specificity of 73% (95% CI, 64–83%) and a sensitivity of 47% (95% CI, 33–61%); first‐trimester uterine artery pulsatility index or resistance index > 90th centile (specificity 93% (95% CI, 90–96%) and sensitivity 26% (95% CI, 23–31%)); placental growth factor (specificity 89% (95% CI, 89–89%) and sensitivity 65% (95% CI, 63–67%)); and placental protein 13 (specificity 88% (95% CI, 87–89%) and sensitivity 37% (95% CI, 33–41%)). No single marker had a test performance suitable for routine clinical use. Models combining markers showed promise, but none had undergone external validation. Conclusions This review of reviews calls into question the need for further aggregate meta‐analy...

Re: Prediction of pre‐eclampsia: review of reviews

“…Whilst we agree with the conclusion of Townsend et al . that well‐designed prospective studies of predictive markers and randomized intervention studies are necessary to develop and validate new prediction models before their introduction into clinical practice, we strongly disagree that these have yet to be performed.…”

“…The meta‐analysis of previous meta‐analyses by Townsend et al . does not benefit but rather hinders research and clinical implementation of strategies for the prediction and prevention of pre‐eclampsia (PE).…”

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The meta-analysis of previous meta-analyses by Townsend et al 1 does not benefit but rather hinders research and clinical implementation of strategies for the prediction and prevention of pre-eclampsia (PE). It is inevitable that there is limited value in performing such umbrella meta-analyses and that misleading conclusions can be drawn when combining data from a series of heterogeneous studies in terms of size and type of populations examined, methodology used, timing of biomarker measurement, adjustment or not for confounders, use of different models combining risk factors from maternal demographic characteristics and medical history with biomarkers, as well as use of a plethora of PE definitions resulting in delivery at different gestational ages.Whilst we agree with the conclusion of Townsend et al 1 that well-designed prospective studies of predictive markers and randomized intervention studies are necessary to develop and validate new prediction models before their introduction into clinical practice, we strongly disagree that these have yet to be performed.

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Renal Parenchymal Disease Mimicking Preeclampsia

“…12 Townsend et al in their reviews suggested that prospective, well-designed studies of predictive markers, preferably in randomized intervention studies and combined through IPD (individual patient data) meta-analyses, are required to develop and validate new prediction models to facilitate the prediction of preeclampsia and minimize further research waste in this field. 13…”

Increasing value and reducing research waste in obstetrics: towards woman‐centered research

“…An integral part of the IPPIC initiative has been the publication of a comprehensive review of all the published systematic reviews on screening for pre‐eclampsia, which highlighted the ongoing unnecessary repetition of small studies with heterogeneous case definitions and outcomes, hindering further development in this field. This form of ‘umbrella review’ provides a broad overview of the evidence and, if performed rigorously, should incorporate an assessment of the degree of confidence in findings of individual reviews that can usefully inform the decision‐making of healthcare providers and their patients.…”