Yland Jennifer J, Zad Zahra, Wang Tanran R, Wesselink Amelia K, Jiang Tammy, Hatch Elizabeth E, Paschalidis Ioannis Ch, Wise Lauren A
Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.
Hariri Institute for Computing and Computational Science & Engineering, Boston University, Boston, Massachusetts; Division of Systems Engineering, Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts.
Fertil Steril. 2024 Jul;122(1):140-149. doi: 10.1016/j.fertnstert.2024.04.007. Epub 2024 Apr 10.
To use self-reported preconception data to derive models that predict the risk of miscarriage.
Prospective preconception cohort study.
Not applicable.
Study participants were female, aged 21-45 years, residents of the United States or Canada, and attempting spontaneous pregnancy at enrollment during 2013-2022. Participants were followed for up to 12 months of pregnancy attempts; those who conceived were followed through pregnancy and postpartum. We restricted analyses to participants who conceived during the study period.
On baseline and follow-up questionnaires completed every 8 weeks until pregnancy, we collected self-reported data on sociodemographic factors, reproductive history, lifestyle, anthropometrics, diet, medical history, and male partner characteristics. We included 160 potential predictor variables in our models.
The primary outcome was a miscarriage, defined as pregnancy loss before 20 weeks of gestation. We followed participants from their first positive pregnancy test until miscarriage or a censoring event (induced abortion, ectopic pregnancy, loss of follow-up, or 20 weeks of gestation), whichever occurred first. We fit both survival and static models using Cox proportional hazards models, logistic regression, support vector machines, gradient-boosted trees, and random forest algorithms. We evaluated model performance using the concordance index (survival models) and the weighted F1 score (static models).
Among the 8,720 participants who conceived, 20.4% reported miscarriage. In multivariable models, the strongest predictors of miscarriage were female age, history of miscarriage, and male partner age. The weighted F1 score ranged from 73%-89% for static models and the concordance index ranged from 53%-56% for survival models, indicating better discrimination for the static models compared with the survival models (i.e., the ability of the model to discriminate between individuals with and without miscarriage). No appreciable differences were observed across strata of miscarriage history or among models restricted to ≥8 weeks of gestation.
Our findings suggest that miscarriage is not easily predicted on the basis of preconception lifestyle characteristics and that advancing age and a history of miscarriage are the most important predictors of incident miscarriage.
利用自我报告的孕前数据推导预测流产风险的模型。
前瞻性孕前队列研究。
不适用。
研究参与者为年龄在21 - 45岁之间的女性，居住在美国或加拿大，于2013年至2022年登记时尝试自然受孕。对参与者进行长达12个月的受孕尝试随访；受孕者在孕期及产后接受随访。我们将分析限制在研究期间受孕的参与者。
在基线及直至怀孕前每8周完成的随访问卷中，我们收集了关于社会人口学因素、生殖史、生活方式、人体测量学、饮食、病史及男性伴侣特征的自我报告数据。我们在模型中纳入了160个潜在预测变量。
主要结局为流产，定义为妊娠20周前的妊娠丢失。我们从参与者首次妊娠试验阳性开始随访，直至流产或发生审查事件（人工流产、异位妊娠、失访或妊娠20周），以先发生者为准。我们使用Cox比例风险模型、逻辑回归、支持向量机、梯度提升树和随机森林算法拟合生存模型和静态模型。我们使用一致性指数（生存模型）和加权F1分数（静态模型）评估模型性能。
在8720名受孕的参与者中，20.4%报告有流产。在多变量模型中，流产的最强预测因素是女性年龄、流产史和男性伴侣年龄。静态模型的加权F1分数在73% - 89%之间，生存模型的一致性指数在53% - 56%之间，表明与生存模型相比，静态模型的辨别能力更好（即模型区分有流产和无流产个体的能力）。在流产史各分层或限制在妊娠≥8周的模型之间未观察到明显差异。
我们的研究结果表明，基于孕前生活方式特征不易预测流产，年龄增长和流产史是偶发性流产的最重要预测因素。
