Gioan Marion, Fenollar Florence, Loundou Anderson, Menard Jean-Pierre, Blanc Julie, D'Ercole Claude, Bretelle Florence
CHG Sainte-Musse, 54, rue Henri-Sainte-Claire-Deville, 83100 Toulon, France.
Unité de recherche sur les maladies infectieuses tropicales et emergentes, UM63, CNRS 7278, IRD 198, INSERM 1095, 13000 Marseille, France.
J Gynecol Obstet Hum Reprod. 2018 Dec;47(10):545-548. doi: 10.1016/j.jogoh.2018.08.014. Epub 2018 Aug 24.
This study aimed to develop a new tool for personalised preterm birth risk evaluation in high-risk population.
813 high-risk asymptomatic pregnant women included in a French multicentric prospective study were analysed. Clinical and paraclinical variables, including screening for bacterial vaginosis with molecular biology, cervical length, have been used to create the nomogram, based on the logistic regression model. The validity was checked by bootstrap. A downloadable calculator was build.
Nine risk factors were included in this model: history of late miscarriage and/or preterm delivery, active smoking, ultrasound cervical length, term of pregnancy at screening, bacterial vaginosis, premature rupture of membranes, daily travel more than 30min. Discrimination and calibration of the nomogram revealed good predictive abilities. The area under the receiver operating characteristic curve was 0.77 (95% CI; 0.72-0.81). The mean absolute error was 0.018, which showed proper calibration. The optimal risk threshold was 23.2% with a sensitivity of 74%, a specificity of 72.7% and a predictive negative value of 90.6%.
The nomogram can help to better define individual preterm birth risk in high-risk pregnancies.
本研究旨在开发一种用于高危人群个性化早产风险评估的新工具。
对纳入法国一项多中心前瞻性研究的813名高危无症状孕妇进行分析。基于逻辑回归模型,利用临床和辅助临床变量(包括通过分子生物学筛查细菌性阴道病、宫颈长度)创建列线图。通过自抽样法检验其有效性。构建了一个可下载的计算器。
该模型纳入了九个风险因素:晚期流产和/或早产史、主动吸烟、超声测量的宫颈长度、筛查时的孕周、细菌性阴道病、胎膜早破、每日出行超过30分钟。列线图的辨别力和校准显示出良好的预测能力。受试者操作特征曲线下面积为0.77(95%可信区间:0.72 - 0.81)。平均绝对误差为0.018,表明校准良好。最佳风险阈值为23.2%,灵敏度为74%,特异度为72.7%,预测阴性值为90.6%。
列线图有助于更好地界定高危妊娠中个体的早产风险。