From the Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada.
Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal, QC, Canada.
Epidemiology. 2021 Jul 1;32(4):560-568. doi: 10.1097/EDE.0000000000001349.
In perinatal epidemiology, the development of risk prediction models is complicated by parity; how repeat pregnancies influence the predictive accuracy of models that include obstetrical history is unclear.
To assess the influence of repeat pregnancies on the association between predictors and the outcomes, as well as the influence of ignoring the nonindependence between pregnancies, we created four analytical cohorts using the Clinical Practice Research Datalink. The cohorts included (1) first deliveries, (2) a random sample of one delivery per woman, (3) all eligible deliveries per woman, and (4) all eligible deliveries and censoring of follow-up at subsequent pregnancies. Using Plasmode simulations, we varied the predictor-outcome association across cohorts.
We found minimal differences in the relative contribution of predictors to the overall predictions and the discriminative accuracy of models in the cohort of randomly sampled deliveries versus the all deliveries cohort (C-statistic: 0.62 vs. 0.63; Nagelkerke's R2: 0.03 for both). Accounting for clustering and censoring upon subsequent pregnancies also had negligible influence on model performance. We found important differences in model performance between the models developed in the cohort of first deliveries and the random sample of deliveries.
In our study, a model including first deliveries had the best predictive accuracy but was not generalizable to women of varying parities. Moreover, including repeat pregnancies did not improve the predictive accuracy of the models. Multiple models may be needed to improve the transportability and accuracy of prediction models when the outcome of interest is influenced by parity.
在围产期流行病学中，由于生育次数的影响，风险预测模型的发展变得复杂；包含产科史的模型中，多次妊娠如何影响预测准确性尚不清楚。
为了评估多次妊娠对预测因子与结局之间的关联的影响，以及忽略妊娠之间非独立性的影响，我们使用临床实践研究数据链接创建了四个分析队列。队列包括（1）首次分娩，（2）每位女性随机抽取一次分娩，（3）每位女性所有符合条件的分娩，以及（4）每位女性所有符合条件的分娩，并在后续妊娠中进行随访。我们使用 Plasmode 模拟，在各队列中改变了预测因子-结局的关联。
我们发现，在随机抽取的分娩队列与所有分娩队列中，预测因子对总体预测的相对贡献以及模型的判别准确性差异极小（C 统计量：0.62 与 0.63；Nagelkerke 的 R2：两者均为 0.03）。考虑到随后妊娠的聚类和删失，对模型性能的影响也可以忽略不计。我们发现，在首次分娩队列和随机抽取的分娩队列中开发的模型之间，模型性能存在重要差异。
在我们的研究中，包含首次分娩的模型具有最佳的预测准确性，但不能推广到不同生育次数的女性。此外，包含重复妊娠并没有提高模型的预测准确性。当感兴趣的结局受生育次数影响时，可能需要多个模型来提高预测模型的可转移性和准确性。
