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dc.contributor.authorAllotey, J
dc.contributor.authorSnell, KIE
dc.contributor.authorSmuk, M
dc.contributor.authorHooper, R
dc.contributor.authorChan, CL
dc.contributor.authorAhmed, A
dc.contributor.authorChappell, LC
dc.contributor.authorvon Dadelszen, P
dc.contributor.authorDodds, J
dc.contributor.authorGreen, M
dc.contributor.authorKenny, L
dc.contributor.authorKhalil, A
dc.contributor.authorKhan, KS
dc.contributor.authorda Silva Costa, F
dc.contributor.authoret al.
dc.date.accessioned2021-10-22T04:37:11Z
dc.date.available2021-10-22T04:37:11Z
dc.date.issued2020
dc.identifier.issn1366-5278
dc.identifier.doi10.3310/HTA24720
dc.identifier.urihttp://hdl.handle.net/10072/409413
dc.description.abstractBackground: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. Objectives: To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. Design: This was an individual participant data meta-analysis of cohort studies. Setting: Source data from secondary and tertiary care. Predictors: We identified predictors from systematic reviews, and prioritised for importance in an international survey. Primary outcomes: Early-onset (delivery at < 34 weeks’ gestation), late-onset (delivery at ≥ 34 weeks’ gestation) and any-onset pre-eclampsia. Analysis: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration.We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I2 and 2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. Results: The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. Limitations: Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. Conclusion: For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherNational Institute for Health Research
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto290
dc.relation.ispartofissue72
dc.relation.ispartofjournalHealth Technology Assessment
dc.relation.ispartofvolume24
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchLibrary and information studies
dc.subject.fieldofresearchcode4609
dc.subject.fieldofresearchcode4610
dc.subject.keywordsINDIVIDUAL PARTICIPANT DATA
dc.subject.keywordsIPD
dc.subject.keywordsPRE-ECLAMPSIA
dc.subject.keywordsPREDICTION MODEL
dc.subject.keywordsPROGNOSTIC MODEL
dc.titleValidation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: An individual participant data meta-analysis
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationAllotey, J; Snell, KIE; Smuk, M; Hooper, R; Chan, CL; Ahmed, A; Chappell, LC; von Dadelszen, P; Dodds, J; Green, M; Kenny, L; Khalil, A; Khan, KS; da Silva Costa, F; et al.; Thangaratinam, S, Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: An individual participant data meta-analysis, Health Technology Assessment, 2020, 24 (72), pp. 1-290
dc.date.updated2021-10-22T03:26:29Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© Queen’s Printer and Controller of HMSO 2020. This work was produced by Allotey et al. under the terms of a commissioning contract issued by the Secretary of State for Health and Social Care. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.
gro.hasfulltextFull Text
gro.griffith.authorDa Silva Costa, Fabricio


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