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Accounting for Repeat Pregnancies in Risk Prediction Models.

Sonia M Grandi1,2, Kristian B Filion1,2,3, Jennifer A Hutcheon4

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Repeat pregnancies do not improve prediction models in perinatal epidemiology. Models focusing only on first deliveries offer the best accuracy but lack generalizability. Multiple parity-specific models may be necessary.

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Area of Science:

  • Perinatal epidemiology
  • Reproductive health research
  • Clinical prediction modeling

Background:

  • Parity complicates risk prediction model development in perinatal epidemiology.
  • The impact of repeat pregnancies on model accuracy, including obstetrical history, remains unclear.

Purpose of the Study:

  • To evaluate how repeat pregnancies affect the association between predictors and outcomes.
  • To assess the impact of ignoring non-independence between pregnancies in analytical models.

Main Methods:

  • Four analytical cohorts were created from the Clinical Practice Research Datalink.
  • Cohorts included first deliveries, random samples, all deliveries, and censored follow-up deliveries.
  • Plasmode simulations were used to vary predictor-outcome associations across cohorts.

Main Results:

  • Minimal differences in predictive contribution and accuracy were observed between random sample and all deliveries cohorts.
  • Accounting for pregnancy clustering and censoring had negligible effects on model performance.
  • Significant performance differences emerged between models developed from first deliveries versus random samples.

Conclusions:

  • Models based on first deliveries showed highest predictive accuracy but limited generalizability across parities.
  • Including repeat pregnancies did not enhance model predictive accuracy.
  • Multiple parity-specific models may be required for improved transportability and accuracy in perinatal prediction.