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External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis
Kym I E Snell1, John Allotey2,3, Melanie Smuk3
1Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK. k.snell@keele.ac.uk.
BMC Medicine
|November 2, 2020
Summary
This study found that most pre-eclampsia prediction models have modest accuracy and poor calibration, limiting their clinical use. Further validation is needed before implementing these models in UK healthcare settings.
Area of Science:
- Obstetrics and Gynecology
- Clinical Prediction Modeling
- Maternal Health
Background:
- Pre-eclampsia is a major cause of maternal and infant mortality.
- Early risk identification is crucial for effective pregnancy management.
- Many pre-eclampsia prediction models exist, but external validation is limited.
Purpose of the Study:
- To externally validate published pre-eclampsia prediction models using UK individual participant data.
- To assess the accuracy and clinical utility of these models within the UK healthcare setting.
Main Methods:
- Individual participant data from 11 UK cohort studies (217,415 women) were used.
- 24 models were validated based on systematic review and data availability.
- Model performance was evaluated using discrimination (C-statistic) and calibration metrics.
Main Results:
- Most validated models demonstrated modest discrimination (C-statistics 0.6-0.7).
- Calibration was generally poor, with predictions often being too extreme.
- Significant between-study heterogeneity was observed in model calibration.
Conclusions:
- Existing pre-eclampsia prediction models show limited clinical utility due to modest performance and poor calibration.
- Further validation across diverse UK settings is recommended before clinical adoption.
- The evidence supporting the use of these models in clinical decision-making is currently insufficient.
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