Brown Mary M, Kuhle Stefan, Smith Bruce, Allen Victoria M, Payne Jennifer, Woolcott Christy G
School of Integrated Health, University of New Brunswick, Saint John, NB, Canada.
Perinatal Epidemiology Research Unit, Depts of Obstetrics & Gynaecology and Pediatrics, Dalhousie University, Halifax, NS, Canada.
Sci Rep. 2025 Sep 26;15(1):33212. doi: 10.1038/s41598-025-18466-0.
Prediction of small (SGA) and large for gestational age (LGA) using routinely collected antenatal data remains suboptimal, particularly among nulliparous women. In this study, models for SGA (< 10 percentile) and LGA (> 90 percentile) were developed by combining grandmaternal pregnancy-related information and maternal birth characteristics ("G0 predictors") with maternal clinical factors available at 26 weeks' gestation ("G1 predictors"). The study used a cohort of first-born, singleton births to nulliparous women in Nova Scotia, Canada (1981-2011), and their mothers, from the Nova Scotia Atlee Perinatal Database. Models using G0 predictors, G1 predictors, and their combination were developed with Super Learner, an ensemble machine learning algorithm, and internally validated using nested cross-validation. Discrimination was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve (AUC-PR); calibration was also evaluated. Among 9,097 grandmother-mother-infant triads, 902 (9.9%) infants were SGA and 891 (9.8%) were LGA. Including G0 predictors improved discrimination compared to G1-only models (AUC-ROC 0.69 vs. 0.66 for SGA and 0.71 vs. 0.66 for LGA; AUC-PR: 0.21 vs. 0.18 for SGA and 0.22 vs. 0.18 for LGA). Models fitted using both sets of predictors were well calibrated. While incorporating intergenerational information modestly improved prediction, overall predictive performance remains poor.
利用常规收集的产前数据预测小于胎龄儿（SGA）和大于胎龄儿（LGA）的情况仍不尽人意，尤其是在初产妇中。在本研究中，通过将祖母辈与妊娠相关的信息和母亲的出生特征（“G0预测因子”）与妊娠26周时可获得的母亲临床因素（“G1预测因子”）相结合，建立了SGA（<第10百分位数）和LGA（>第90百分位数）的模型。该研究使用了加拿大新斯科舍省（1981 - 2011年）初产妇的头胎单胎分娩队列及其母亲的数据，数据来自新斯科舍省阿特利围产期数据库。使用集成机器学习算法超级学习器建立了使用G0预测因子、G1预测因子及其组合的模型，并使用嵌套交叉验证进行内部验证。通过受试者操作特征曲线下面积（AUC - ROC）和精确召回率曲线（AUC - PR）评估区分度；同时也评估了校准情况。在9097个祖母 - 母亲 - 婴儿三元组中，902例（9.9%）婴儿为SGA，891例（9.8%）为LGA。与仅使用G1模型相比，纳入G0预测因子可提高区分度（SGA的AUC - ROC为0.69对0.66，LGA为0.71对0.66；SGA的AUC - PR为0.21对0.18，LGA为0.22对0.18）。使用两组预测因子拟合的模型校准良好。虽然纳入代际信息适度改善了预测，但总体预测性能仍然较差。
