Tiered prediction system for preeclampsia: An integrative application of multiple models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For years, it has been a challenge to identify women at risk of Preeclampsia (PE), one of the leading causes of maternal and perinatal morbidity and mortality. This would be especially useful in early pregnancy when modifiable factors can be addressed to reduce the risk or severity of outcome. Despite an increasing number of clinical and statistical prediction models being developed, which have been shown to outperform traditional maternal history or Doppler ultrasound approaches, it is still difficult to make accurate predictions based on a single model at a single time-point. Hence, here we investigate the use of multiple models integrated by Bayes' theorem. Methods: Prediction models based on three stages of pregnancy, pre-pregnancy, 15 weeks and 20 weeks of gestation, were developed with varying levels of sensitivity and specificity specific to each stage. Post-test probabilities at each stage are then calculated based on the Likelihood of each test using Bayes' theorem. The accuracy measures and predictive values are evaluated for both pre-test and post-test probabilities. Results: The overall proportion of truly identified cases have improved in the integrated model, with 81% correctly identified at 20 weeks of gestation, compared to 75% by the individual model. A relatively balanced accuracy can be achieved even when individual tests have been specified for higher sensitivity or specificity. Conclusion: Through an integrated prediction system, the accuracy of prediction is further enhanced and tailored for individual women, as the risk is assessed and updated throughout pregnancy based on predictors at different stages, the likelihood of PE from prediction at earlier stages, and clinicians' knowledge or hypotheses.

Original languageEnglish
Title of host publicationODSIM2013, 20th International Congress on Modelling and Simulation
EditorsJulia Piantadosi, Robert Anderssen, John Boland
PublisherModelling and Simulation Society of Australia and New Zealand
Pages2041-2046
Number of pages6
ISBN (Electronic)9780987214331
DOIs
Publication statusPublished - Dec 2013
Externally publishedYes
Event20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 - Held jointly with the 22nd National Conference of the Australian Society for Operations Research, ASOR 2013 and the DSTO led Defence Operations Research Symposium, DORS 2013 - Adelaide, Australia
Duration: 1 Dec 20136 Dec 2013

Publication series

NameProceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013

Conference

Conference20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 - Held jointly with the 22nd National Conference of the Australian Society for Operations Research, ASOR 2013 and the DSTO led Defence Operations Research Symposium, DORS 2013
CountryAustralia
CityAdelaide
Period1/12/136/12/13

Keywords

  • Bayes' theorem
  • Prediction
  • Preeclampsia

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  • Cite this

    Leemaqz, S. Y., Dekker, G. A., & Roberts, C. T. (2013). Tiered prediction system for preeclampsia: An integrative application of multiple models. In J. Piantadosi, R. Anderssen, & J. Boland (Eds.), ODSIM2013, 20th International Congress on Modelling and Simulation (pp. 2041-2046). (Proceedings - 20th International Congress on Modelling and Simulation, MODSIM 2013). Modelling and Simulation Society of Australia and New Zealand. https://doi.org/10.36334/modsim.2013.I5.leemaqz