Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, US.
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, US.
BMC Med Res Methodol. 2022 May 30;22(1):156. doi: 10.1186/s12874-022-01589-7.
Preconception pregnancy risk profiles-characterizing the likelihood that a pregnancy attempt results in a full-term birth, preterm birth, clinical pregnancy loss, or failure to conceive-can provide critical information during the early stages of a pregnancy attempt, when obstetricians are best positioned to intervene to improve the chances of successful conception and full-term live birth. Yet the task of constructing and validating risk assessment tools for this earlier intervention window is complicated by several statistical features: the final outcome of the pregnancy attempt is multinomial in nature, and it summarizes the results of two intermediate stages, conception and gestation, whose outcomes are subject to competing risks, measured on different time scales, and governed by different biological processes. In light of this complexity, existing pregnancy risk assessment tools largely focus on predicting a single adverse pregnancy outcome, and make these predictions at some later, post-conception time point.
We reframe the individual pregnancy attempt as a multistate model comprised of two nested multinomial prediction tasks: one corresponding to conception and the other to the subsequent outcome of that pregnancy. We discuss the estimation of this model in the presence of multiple stages of outcome missingness and then introduce an inverse-probability-weighted Hypervolume Under the Manifold statistic to validate the resulting multivariate risk scores. Finally, we use data from the Effects of Aspirin in Gestation and Reproduction (EAGeR) trial to illustrate how this multistate competing risks framework might be utilized in practice to construct and validate a preconception pregnancy risk assessment tool.
In the EAGeR study population, the resulting risk profiles are able to meaningfully discriminate between the four pregnancy attempt outcomes of interest and represent a significant improvement over classification by random chance.
As illustrated in our analysis of the EAGeR data, our proposed prediction framework expands the pregnancy risk assessment task in two key ways-by considering a broader array of pregnancy outcomes and by providing the predictions at an earlier, preconception intervention window-providing obstetricians and their patients with more information and opportunities to successfully guide pregnancy attempts.
受孕前妊娠风险预测模型——通过评估妊娠尝试成功足月分娩、早产、临床妊娠丢失或未受孕的可能性，为妊娠尝试早期提供重要信息，此时妇产科医生最有机会进行干预，以提高成功受孕和足月活产的机会。然而，构建和验证该早期干预窗口风险评估工具的任务因以下几个统计特征而变得复杂：妊娠尝试的最终结局本质上是多项的，它总结了受孕和妊娠两个中间阶段的结果，而这些结果受到竞争风险的影响，在不同的时间尺度上进行测量，并受不同的生物学过程的影响。鉴于这种复杂性，现有的妊娠风险评估工具主要侧重于预测单一不良妊娠结局，并在受孕后某个时间点进行这些预测。
我们将个体妊娠尝试重新定义为一个多状态模型，该模型由两个嵌套的多项预测任务组成：一个对应于受孕，另一个对应于该妊娠的后续结果。我们讨论了在存在多个阶段结局缺失的情况下对该模型的估计，然后引入逆概率加权超体积下流形统计量来验证由此产生的多变量风险评分。最后，我们使用 Effects of Aspirin in Gestation and Reproduction (EAGeR) 试验的数据来说明如何在实践中利用这种多状态竞争风险框架来构建和验证受孕前妊娠风险评估工具。
在 EAGeR 研究人群中，由此产生的风险预测模型能够有意义地区分四个感兴趣的妊娠尝试结局，并且比随机分类有显著的改善。
正如我们对 EAGeR 数据的分析所示，我们提出的预测框架通过考虑更广泛的妊娠结局和更早的受孕前干预窗口提供预测，在两个关键方面扩展了妊娠风险评估任务，为妇产科医生及其患者提供了更多的信息和机会，以成功指导妊娠尝试。
