Quality of first trimester risk prediction models for pre-eclampsia: a systematic review.

BACKGROUND: There is an increasing interest in first trimester risk prediction models for pre-eclampsia.
OBJECTIVES: To systematically review and critically assess the building and reporting of methods used to develop first trimester risk prediction models for pre-eclampsia. SEARCH STRATEGY: Search of PubMed and EMBASE databases from inception to July 2013. SELECTION CRITERIA: Logistic regression model for predicting the risk of pre-eclampsia in the first trimester, including uterine artery Doppler among independent variables. DATA COLLECTION AND ANALYSIS: We extracted information on study design, outcome definition, participant recruitment, sample size and number of events, risk predictors and their selection and treatment, model-building strategies, missing data, overfitting and validation. MAIN
RESULTS: The initial search identified 80 articles. A total of 24 studies were eligible for review, from which 38 predictive models were identified. The median number of study participants was 697 [interquartile range (IQR) 377- 5126]. The median number of cases of pre-eclampsia per model was 37 (IQR 19-97). The median number of risk predictors was 5 (IQR 3.75-7). In 22% of the models, the number of events per variable was fewer than the commonly recommended value of 10 events per predictor; this proportion increased to 94% in models for early pre-eclampsia. Treatment and handling of missing data were not reported in 37 models. Only three models reported model validation.
CONCLUSIONS: We found frequent methodological deficiencies in studies reporting risk prediction models for pre-eclampsia. This may limit their reliability and validity.