The performance of risk prediction models for pre-eclampsia using routinely collected maternal characteristics and comparison with models that include specialised tests and with clinical guideline decision rules: a systematic review
Al-Rubaie, Z., Askie, L., Ray, J., Hudson, H., & Lord, S. (2016). The performance of risk prediction models for pre-eclampsia using routinely collected maternal characteristics and comparison with models that include specialised tests and with clinical guideline decision rules: a systematic review. BJOG: an International Journal of Obstetrics and Gynaecology, Early View (Online First).
Background: Risk prediction models may be valuable to identify women at risk of pre-eclampsia to guide aspirin prophylaxis in early pregnancy.
Objective: To assess the performance of ‘simple’ risk models for pre-eclampsia that use routinely collected maternal characteristics; compare with ‘specialised’ models that include specialised tests; and to guideline recommended decision rules.
Search strategy: MEDLINE, Embase and PubMed were searched to June 2014.
Selection criteria: We included studies that developed or validated pre-eclampsia risk models using maternal characteristics with or without specialised tests and reported model performance.
Data collection and analysis: We extracted data on study characteristics; model predictors, validation and performance including area under the curve (AUC), sensitivity and specificity.
Main results: We identified 29 studies that developed 70 models including 22 simple models. Studies included 151–9149 women with a pre-eclampsia prevalence of 1.2–9.5%. No single predictor was included in all models. Four simple models were externally validated, with a model using parity, pre-eclampsia history, race, chronic hypertension and conception method to predict early-onset pre-eclampsia achieving the highest AUC (0.76, 95% CI 0.74–0.77). Nine studies comparing simple versus specialized models in the same population reported AUC favouring specialised models. A simple model achieved fewer false positives than a guideline recommended risk factor list, but sensitivity to classify risk for aspirin prophylaxis was not assessed.
Conclusion: Validated simple pre-eclampsia risk models demonstrate good risk discrimination that can be improved with specialised tests. Further research is needed to determine their clinical value to guide aspirin prophylaxis compared with decision rules.
Aspirin, pre-eclampsia, risk factors, risk prediction models, systematic review, validation
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