Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/81814
Type: Conference paper
Title: Tiered prediction system for preeclampsia: an integrative application of multiple models
Author: Leemaqz, S.
Dekker, G.
Roberts, C.
Citation: MODSIM2013: 20th International Congress on Modelling and Simulation, December 2013 / J. Piantadosi, R.S. Anderssen, and J. Boland (eds.):pp. 2041-2046
Publisher: The Modelling and Simulation Society of Australia and New Zealand
Issue Date: 2013
ISBN: 9780987214331
Conference Name: International Congress on Modelling and Simulation (20th : 2013 : Adelaide, South Australia)
Statement of
Responsibility: 
S.Y. Leemaqz, G.A. Dekker and C.T. Roberts
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.
Keywords: Preeclampsia; prediction; Bayes' theorem
Rights: Copyright status unknown
RMID: 0030000211
Description (link): http://www.mssanz.org.au/modsim2013/index.html
Published version: http://www.mssanz.org.au/modsim2013/I5/leemaqz.pdf
Appears in Collections:Obstetrics and Gynaecology publications

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