Working towards a risk prediction model for neural tube defects

Authors

  • A.J. Agopian,

    1. Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas
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  • Philip J. Lupo,

    1. Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas
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  • Sarah C. Tinker,

    1. National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia
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  • Mark A. Canfield,

    1. Birth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, Texas
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  • Laura E. Mitchell,

    Corresponding author
    1. Human Genetics Center, Division of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, Texas
    • University of Texas School of Public Health, 1200 Herman Pressler Dr., Houston, TX 77030
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  • and the National Birth Defects Prevention Study


  • The findings and conclusions in this report are those of the authors and do not necessarily reflect those of the Centers for Disease Control and Prevention.

Abstract

BACKGROUND Several risk factors have been consistently associated with neural tube defects (NTDs). However, the predictive ability of these risk factors in combination has not been evaluated. METHODS To assess the predictive ability of established risk factors for NTDs, we built predictive models using data from the National Birth Defects Prevention Study, which is a large, population-based study of nonsyndromic birth defects. Cases with spina bifida or anencephaly, or both (n = 1239), and controls (n = 8494) were randomly divided into separate training (75% of cases and controls) and validation (remaining 25%) samples. Multivariable logistic regression models were constructed with the training samples. The predictive ability of these models was evaluated in the validation samples by assessing the area under the receiver operator characteristic curves. An ordinal predictive risk index was also constructed and evaluated. In addition, the ability of classification and regression tree (CART) analysis to identify subgroups of women at increased risk for NTDs in offspring was evaluated. RESULTSThe predictive ability of the multivariable models was poor (area under the receiver operating curve: 0.55 for spina bifida only, 0.59 for anencephaly only, and 0.56 for anencephaly and spina bifida combined). The predictive abilities of the ordinal risk indexes and CART models were also low. CONCLUSION Current established risk factors for NTDs are insufficient for population-level prediction of a women's risk for having affected offspring. Identification of genetic risk factors and novel nongenetic risk factors will be critical to establishing models, with good predictive ability, for NTDs. Birth Defects Research (Part A) 2012. © 2012 Wiley Periodicals, Inc.

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