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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Predicting stillbirth in a low resource setting.

Gbenga A Kayode1, Diederick E Grobbee2,3, Mary Amoakoh-Coleman2

  • 1Julius Global Health, Julius Center for Health Sciences and Primary Care|University Medical Centre Utrecht, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands. g.a.kayode@umcutrecht.nl.

BMC Pregnancy and Childbirth
|September 22, 2016
PubMed
Summary
This summary is machine-generated.

A new prediction model can identify pregnancies at high risk of stillbirth, aiding early intervention in low-resource settings. This model, developed in Nigeria, shows excellent performance for stillbirth prediction.

Keywords:
Low-resource settingPredictingStillbirth

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Area of Science:

  • Obstetrics and Gynecology
  • Perinatal Health
  • Public Health

Background:

  • Stillbirth is a significant cause of perinatal mortality, particularly in low- and middle-income countries.
  • Approximately three million third-trimester stillbirths occur annually in these regions.
  • Early detection of high-risk pregnancies is crucial for intervention.

Purpose of the Study:

  • To develop a prediction model for early detection of pregnancies at high risk of stillbirth.
  • To create a tool for early intervention in low-resource settings.
  • To assess the predictive performance of the developed model.

Main Methods:

  • Retrospective cohort study of 6,573 pregnant women in Nigeria (2010-2013).
  • Multivariable logistic regression analysis to identify stillbirth predictors.
  • Internal validation and performance assessment (discrimination and calibration) of the prediction model.

Main Results:

  • A prediction model was developed including maternal comorbidity, residence, occupation, parity, bleeding in pregnancy, and fetal presentation.
  • An extended model incorporating fetal growth rate showed improved predictive performance.
  • Both models demonstrated excellent calibration and discrimination (C-statistics of 0.80 and 0.82).

Conclusions:

  • A simple, informative prediction model for high-risk stillbirth pregnancies was developed for low-resource settings.
  • The model facilitates early detection and intervention to reduce stillbirth rates.
  • External validation of this promising model is recommended for future research.