Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis
Authors: Allotey John , Laivuori Hannele , Snell Kym IE , Smuk Melanie , Hooper Richard , Chan Claire L , Ahmed Asif , Chappell Lucy C , von Dadelszen Peter , Dodds Julie , Green Marcus , Kenny Louise , Khalil Asma , Khan Khalid S , Mol Ben W , Myers Jenny , Poston Lucilla , Thilaganathan Basky , Staff Anne C , Smith Gordon CS , Ganzevoort Wessel , Odibo Anthony O , Ramírez Javier A , Kingdom John , Daskalakis George , Farrar Diane , Baschat Ahmet A , Seed Paul T , Prefumo Federico , da Silva Costa Fabricio , Groen Henk , Audibert Francois , Masse Jacques , Skråstad Ragnhild B , Salvesen Kjell Å , Haavaldsen Camilla , Nagata Chie , Rumbold Alice R , Heinonen Seppo , Askie Lisa M , Smits Luc JM , Vinter Christina A , Magnus Per M , Eero Kajantie , Villa Pia M , Jenum Anne K , Andersen Louise B , Norman Jane E , Ohkuchi Akihide , Eskild Anne , Bhattacharya Sohinee , McAuliffe Fionnuala M , Galindo Alberto , Herraiz Ignacio , Carbillon Lionel , Klipstein-Grobusch Kerstin , Yeo SeonAe , Teede Helena J , Browne Joyce L , Moons Karel GM , Riley Richard D , Thangaratinam Shakila 
Published: December 2020

Background

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 I
2 and τ2. 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.

Future work

Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate.

Study registration

This study is registered as PROSPERO CRD42015029349.

Funding

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.