背景：风险预测模型对于识别有先兆子痫风险的女性可能是有价值的，以指导早期妊娠阿司匹林的预防。
目的：评估使用常规收集的母体特征的“简单”先兆子痫风险模型的性能；与包括专门测试的“专门”模型进行比较；以及指南推荐的决策规则。
方法：MEDLINE，搜索Embase和PubMed至2014年6月。
方法：我们纳入了使用母体特征开发或验证先兆子痫风险模型的研究，有或没有专门测试，并报告了模型表现。
方法：我们提取了有关研究特征的数据；模型预测因子，验证和性能，包括曲线下面积(AUC)，敏感性和特异性。
结果：我们确定了29项研究，开发了70个模型，其中包括22个简单模型。研究包括151-9149名先兆子痫患病率为1.2-9.5%的妇女。所有模型中均未包含单个预测因子。四个简单的模型进行了外部验证，使用奇偶校验的模型，先兆子痫病史,种族,慢性高血压和概念方法来预测达到最高AUC的早发型先兆子痫（0.76，95％CI0.74-0.77）。九项研究比较了同一人群中的简单模型和专门模型，报告了AUC偏爱专门模型。一个简单的模型实现了比指南推荐的风险因素列表更少的误报，但未评估阿司匹林预防风险分类的敏感性.
结论：经过验证的简单先兆子痫风险模型显示出良好的风险区分度，可以通过专门的测试来改善。与决策规则相比，需要进一步研究以确定其指导阿司匹林预防的临床价值。
结论：使用母体因素的先兆子痫风险模型显示出良好的风险区分来指导阿司匹林的预防。
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.
METHODS: MEDLINE, Embase and PubMed were searched to June 2014.
METHODS: We included studies that developed or validated pre-eclampsia risk models using maternal characteristics with or without specialised tests and reported model performance.
METHODS: We extracted data on study characteristics; model predictors, validation and performance including area under the curve (AUC), sensitivity and specificity.
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.
CONCLUSIONS: 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.
CONCLUSIONS: Pre-eclampsia risk models using maternal factors show good risk discrimination to guide aspirin prophylaxis.
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