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Keywords:

  • Biomarkers;
  • early pregnancy;
  • model;
  • pre-eclampsia

Objective

To investigate the performance of a multivariable model combining a priori clinical characteristics and biomarkers to detect, early in pregnancy, women at higher risk of developing pre-eclampsia (PE).

Design

Nested case–control study.

Setting

University medical centre, Quebec, Canada (CHU de Québec).

Population

A total of 7929 pregnant women recruited between 10 and 18 weeks of gestation. In all, 350 developed hypertensive disorders of pregnancy (HDP)—of which 139 had PE, comprising 68 with severe PE and 47 with preterm PE—and were matched with two women with a normal pregnancy.

Methods

We selected a priori clinical characteristics and promising markers to create multivariable logistic regression models: body mass index (BMI), mean arterial pressure (MAP), placental growth factor, soluble Fms-like tyrosine kinase-1, pregnancy-associated plasma protein A and inhibin A.

Main outcome measures

PE, severe PE, preterm PE, HDP.

Results

At false-positive rates of 5 and 10%, the estimated detection rates were between 15% (5–29%) and 32% (25–39%), and between 39% (19–59%) and 50% (34–66%), respectively. Considering the low prevalence of PE in this population, the positive predictive values were 7% (5–9%) to 10% (7–13%) for PE and 2% (1–4%) to 4% (3–6%) in the preterm and severe PE subgroups. The multivariable model yielded areas under the receiver operating characteristics curves (AUC) between 0.72 (0.61–0.81) and 0.78 (0.68–0.88). When only BMI and MAP were included in the model, the AUC were similar to those of the a priori model.

Conclusions

In a population with a low prevalence of preterm PE, a multivariable risk algorithm using an a priori combination of clinical characteristics and biochemical markers did not reach a performance justifying clinical implementation as screening test early in pregnancy.