Predictive models of pregnancy based on data from a preconception cohort study
Predictive models of pregnancy based on data from a preconception cohort study
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DOI：
10.1093/humrep/deab280
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发表时间：
2022-03-01
影响因子：
6.1
通讯作者：
Paschalidis, Ioannis Ch
中科院分区：
文献类型：
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作者：
Yland, Jennifer J.;Wang, Taiyao;Paschalidis, Ioannis Ch
研究问题我们能否推导出足够的模型来预测积极尝试怀孕的夫妇的受孕概率？利用从北美孕前队列研究的女性参与者中收集的数据，我们开发了预测妊娠的模型，其受试者工作特征曲线下面积（AUC）的性能接近70%。早期的工作主要集中在识别不孕症的个体风险因素上。在低生育力人群中已经开发了几种预测模型，区分度相对较低（AUC：59-64%）。研究设计、规模、持续时间研究参与者为女性，年龄21-45岁，美国或加拿大居民，未使用生育治疗，并在入组时积极尝试怀孕（2013-2019）。参与者在入组时完成了基线问卷调查，并每2个月完成一次随访问卷调查，持续12个月或直至受孕。我们使用了4133名参与者的数据，这些参与者在进入研究时没有超过一个月经周期的怀孕尝试。参与者/材料，设置，方法在基线调查表上，参与者报告了社会人口学因素，生活方式和行为因素，饮食质量，病史和选定的男性伴侣特征的数据。本研究共考虑了163个预测因子。我们实施了正则化逻辑回归、支持向量机、神经网络和梯度提升决策树来推导预测怀孕概率的模型：（i）在少于12个月经周期的怀孕尝试时间内（模型I），以及（ii）在6个月经周期的怀孕尝试时间内（模型II）。使用考克斯模型预测每个月经周期内的妊娠概率，随访时间长达12个周期（模型III）。我们使用模型I和II的AUC和加权F1评分以及模型III的一致性指数评估模型性能。在简约模型中，模型I和II的AUC分别为70%和66%，模型III的一致性指数为63%。在所有模型中，与妊娠呈正相关的预测因素是：以前母乳喂养过婴儿，并使用多种维生素或叶酸补充剂。在所有模型中，与妊娠呈负相关的预测因子为：女性年龄、女性BMI和不孕症史。在没有不孕症史的初产妇中，最重要的预测因素是：女性年龄，女性BMI，男性BMI，生育应用程序的使用，进入研究的尝试时间和感知压力。局限性，预防的原因依赖自我报告的预测数据可能会引入错误分类，考虑到前瞻性设计，这可能对妊娠结局无差异。此外，我们无法确定是否考虑了所有相关的预测变量。最后，尽管我们使用分裂样本复制技术验证了模型，但我们没有进行外部验证研究。鉴于广泛的预测数据，机器学习算法可以用来分析流行病学数据，并预测受孕概率，其识别能力超过早期工作。研究资金/竞争兴趣（S）该研究部分由美国国家科学基金会（赠款DMS-1664644，CNS-1645681和IIS-1914792）和美国国立卫生研究院（赠款R 01 GM 135930和UL 54 TR 004130）支持。在过去的3年里，L.A.W.收到了来自FertilityFriend.com、Kindara.com、Sandstone Diagnostics和Swiss Precision Diagnostics的实物捐赠，用于PRESTO的主要数据收集。洛杉矶机场还担任AbbVie，Inc.的子宫肌瘤顾问。其他作者声明没有竞争利益。
STUDY QUESTION Can we derive adequate models to predict the probability of conception among couples actively trying to conceive? SUMMARY ANSWER Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of similar to 70% in the area under the receiver operating characteristic curve (AUC). WHAT IS KNOWN ALREADY Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59-64%). STUDY DESIGN, SIZE, DURATION Study participants were female, aged 21-45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013-2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry. PARTICIPANTS/MATERIALS, SETTING, METHODS On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decision trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III. MAIN RESULTS AND THE ROLE OF CHANCE Model I and II AUCs were 70% and 66%, respectively, in parsimonious models, and the concordance index for Model III was 63%. The predictors that were positively associated with pregnancy in all models were: having previously breastfed an infant and using multivitamins or folic acid supplements. The predictors that were inversely associated with pregnancy in all models were: female age, female BMI and history of infertility. Among nulligravid women with no history of infertility, the most important predictors were: female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress. LIMITATIONS, REASONS FOR CAUTION Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study. WIDER IMPLICATIONS OF THE FINDINGS Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work. STUDY FUNDING/COMPETING INTEREST(S) The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W.has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests.