Tiruneh Sofonyas Abebaw, Rolnik Daniel Lorber, Selvaratnam Roshan, da Silva Costa Fabricio, McLennan Andrew, Hyett Jon, Teede Helena, Enticott Joanne
Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria, Australia.
Acta Obstet Gynecol Scand. 2025 Sep;104(9):1774-1782. doi: 10.1111/aogs.70002. Epub 2025 Jul 2.
Pre-eclampsia causes adverse maternal and perinatal complications and is preventable through early screening and aspirin treatment. This study evaluates the predictive performance of the Fetal Medicine Foundation first-trimester preterm pre-eclampsia competing risks model in an Australian population.
This was a retrospective cohort study of prospectively collected multisite screening data and pregnancy outcomes between 2014 and 2017 in Australia. Individualized risk for preterm pre-eclampsia was calculated using the Fetal Medicine Foundation model at 11-14 weeks by using maternal factors, biophysical biomarkers (mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI)), and serum biochemical biomarkers (placental growth factor (PlGF) and/or pregnancy-associated plasma protein A (PAPP-A)). The predictive performance was evaluated using the area under the receiver-operating characteristic curve (AUC) and calibration. The detection rates for delivery with preterm pre-eclampsia were calculated at a 10% fixed false-positive rate. Decision curve analysis of the model was evaluated.
Of 29 609 women screened, 132 (0.45%) experienced preterm pre-eclampsia. The median age (interquartile range) was 34 (30-38) years. Women with pre-eclampsia had higher multiple of the median values of MAP and UtA-PI and lower values of PIGF and PAPP-A compared to those without pre-eclampsia. Combined screening by maternal factors, biophysical, and biochemical biomarkers yielded an AUC of 0.87 (95% CI 0.79-0.92), detecting 71% of preterm pre-eclampsia cases at 10% fixed false-positive rate, with the addition of PlGF improving the detection rate by 31% over sole PAPP-A use. Preterm pre-eclampsia screening using maternal factors with all biomarkers showed better clinical net benefit at preference thresholds between 1% and 12% compared to default strategies.
The Fetal Medicine Foundation model, combining maternal factors with biophysical and biochemical biomarkers, demonstrated similar predictive performance in the Australian population compared to previous validation studies in other settings, detecting 71% of preterm pre-eclampsia cases at 10% fixed false-positive rate. The clinical utility analysis showed that early screening and intervention strategies based on a risk-based screening approach is more beneficial than universal or no intervention strategies.
子痫前期会导致不良的母婴并发症，可通过早期筛查和阿司匹林治疗来预防。本研究评估了胎儿医学基金会孕早期早产子痫前期竞争风险模型在澳大利亚人群中的预测性能。
这是一项回顾性队列研究，对2014年至2017年澳大利亚前瞻性收集的多站点筛查数据和妊娠结局进行分析。在孕11 - 14周时，使用胎儿医学基金会模型，通过产妇因素、生物物理生物标志物（平均动脉压（MAP）、子宫动脉搏动指数（UtA - PI））和血清生化生物标志物（胎盘生长因子（PlGF）和/或妊娠相关血浆蛋白A（PAPP - A））计算早产子痫前期的个体风险。使用受试者操作特征曲线下面积（AUC）和校准来评估预测性能。在固定假阳性率为10%的情况下计算早产子痫前期分娩的检出率。对该模型进行决策曲线分析。
在29609名接受筛查的女性中，132名（0.45%）发生了早产子痫前期。中位年龄（四分位间距）为34（30 - 38）岁。与未患子痫前期的女性相比，患子痫前期的女性MAP和UtA - PI的中位数倍数更高，而PIGF和PAPP - A的值更低。通过产妇因素、生物物理和生化生物标志物进行联合筛查，AUC为0.87（95%可信区间0.79 - 0.92），在固定假阳性率为10%时可检测出71%的早产子痫前期病例，与仅使用PAPP - A相比，添加PlGF可使检出率提高31%。与默认策略相比，使用产妇因素和所有生物标志物进行早产子痫前期筛查在1%至12%的偏好阈值下显示出更好的临床净效益。
胎儿医学基金会模型将产妇因素与生物物理和生化生物标志物相结合，与之前在其他环境中的验证研究相比，在澳大利亚人群中表现出相似的预测性能，在固定假阳性率为10%时可检测出71%的早产子痫前期病例。临床效用分析表明，基于风险筛查方法的早期筛查和干预策略比普遍干预或不干预策略更有益。
