Prediction of pre-eclampsia: review of reviews

R. Townsend, A. Khalil, Y. Premakumar, J. Allotey, K. I.E. Snell, C. Chan, L. C. Chappell, R. Hooper, M. Green, B. W. Mol, B. Thilaganathan, S. Thangaratinam, on behalf of the IPPIC Network

Research output: Contribution to journalReview ArticleResearchpeer-review

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-analysis in this area given the large number of published reviews subject to the common limitations of primary predictive studies. Prospective, well-designed studies of predictive markers, preferably randomized intervention studies, and combined through individual-patient data meta-analysis are needed to develop and validate new prediction models to facilitate the prediction of pre-eclampsia and minimize further research waste in this field.

Original languageEnglish
Pages (from-to)16-27
Number of pages12
JournalUltrasound in Obstetrics and Gynecology
Volume54
Issue number1
DOIs
Publication statusPublished - 1 Jul 2019

Keywords

  • hypertension in pregnancy
  • pre-eclampsia
  • prediction
  • screening
  • systematic review

Cite this

Townsend, R., Khalil, A., Premakumar, Y., Allotey, J., Snell, K. I. E., Chan, C., ... on behalf of the IPPIC Network (2019). Prediction of pre-eclampsia: review of reviews. Ultrasound in Obstetrics and Gynecology, 54(1), 16-27. https://doi.org/10.1002/uog.20117
Townsend, R. ; Khalil, A. ; Premakumar, Y. ; Allotey, J. ; Snell, K. I.E. ; Chan, C. ; Chappell, L. C. ; Hooper, R. ; Green, M. ; Mol, B. W. ; Thilaganathan, B. ; Thangaratinam, S. ; on behalf of the IPPIC Network. / Prediction of pre-eclampsia : review of reviews. In: Ultrasound in Obstetrics and Gynecology. 2019 ; Vol. 54, No. 1. pp. 16-27.
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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-analysis in this area given the large number of published reviews subject to the common limitations of primary predictive studies. Prospective, well-designed studies of predictive markers, preferably randomized intervention studies, and combined through individual-patient data meta-analysis are needed to develop and validate new prediction models to facilitate the prediction of pre-eclampsia and minimize further research waste in this field.",
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author = "R. Townsend and A. Khalil and Y. Premakumar and J. Allotey and Snell, {K. I.E.} and C. Chan and Chappell, {L. C.} and R. Hooper and M. Green and Mol, {B. W.} and B. Thilaganathan and S. Thangaratinam and {on behalf of the IPPIC Network}",
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Townsend, R, Khalil, A, Premakumar, Y, Allotey, J, Snell, KIE, Chan, C, Chappell, LC, Hooper, R, Green, M, Mol, BW, Thilaganathan, B, Thangaratinam, S & on behalf of the IPPIC Network 2019, 'Prediction of pre-eclampsia: review of reviews' Ultrasound in Obstetrics and Gynecology, vol. 54, no. 1, pp. 16-27. https://doi.org/10.1002/uog.20117

Prediction of pre-eclampsia : review of reviews. / Townsend, R.; Khalil, A.; Premakumar, Y.; Allotey, J.; Snell, K. I.E.; Chan, C.; Chappell, L. C.; Hooper, R.; Green, M.; Mol, B. W.; Thilaganathan, B.; Thangaratinam, S.; on behalf of the IPPIC Network.

In: Ultrasound in Obstetrics and Gynecology, Vol. 54, No. 1, 01.07.2019, p. 16-27.

Research output: Contribution to journalReview ArticleResearchpeer-review

TY - JOUR

T1 - Prediction of pre-eclampsia

T2 - review of reviews

AU - Townsend, R.

AU - Khalil, A.

AU - Premakumar, Y.

AU - Allotey, J.

AU - Snell, K. I.E.

AU - Chan, C.

AU - Chappell, L. C.

AU - Hooper, R.

AU - Green, M.

AU - Mol, B. W.

AU - Thilaganathan, B.

AU - Thangaratinam, S.

AU - on behalf of the IPPIC Network

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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-analysis in this area given the large number of published reviews subject to the common limitations of primary predictive studies. Prospective, well-designed studies of predictive markers, preferably randomized intervention studies, and combined through individual-patient data meta-analysis are needed to develop and validate new prediction models to facilitate the prediction of pre-eclampsia and minimize further research waste in this field.

AB - 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-analysis in this area given the large number of published reviews subject to the common limitations of primary predictive studies. Prospective, well-designed studies of predictive markers, preferably randomized intervention studies, and combined through individual-patient data meta-analysis are needed to develop and validate new prediction models to facilitate the prediction of pre-eclampsia and minimize further research waste in this field.

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KW - pre-eclampsia

KW - prediction

KW - screening

KW - systematic review

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U2 - 10.1002/uog.20117

DO - 10.1002/uog.20117

M3 - Review Article

VL - 54

SP - 16

EP - 27

JO - Ultrasound in Obstetrics and Gynecology

JF - Ultrasound in Obstetrics and Gynecology

SN - 0960-7692

IS - 1

ER -

Townsend R, Khalil A, Premakumar Y, Allotey J, Snell KIE, Chan C et al. Prediction of pre-eclampsia: review of reviews. Ultrasound in Obstetrics and Gynecology. 2019 Jul 1;54(1):16-27. https://doi.org/10.1002/uog.20117