Accounting for Repeat Pregnancies in Risk Prediction Models. Issue 4 (24th March 2021) - Grandi, Sonia M.; Filion, Kristian B.; Hutcheon, Jennifer A.; Rosella, Laura C.; Platt, Robert W.
Abstract : Supplemental Digital Content is available in the text. Abstract : Background: 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. Methods: 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. Results: 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 R 2 : 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. Conclusions: 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.