A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure - 30/07/16

Doi : 10.1016/j.jpeds.2016.06.047

Patrick K. Goh, BS ¹, Lauren R. Doyle, BS ¹, Leila Glass, MS ¹, Kenneth L. Jones, MD ², Edward P. Riley, PhD ¹, Claire D. Coles, PhD ^3,⁴, H. Eugene Hoyme, MD ⁵, Julie A. Kable, PhD ⁴, Philip A. May, PhD ^6,⁷, Wendy O. Kalberg, PhD ⁷, Elizabeth, R. Sowell, PhD ^8,⁹, Jeffrey R. Wozniak, PhD ¹⁰, Sarah N. Mattson, PhD ^1,^*
¹ Center for Behavioral Teratology, Department of Psychology, San Diego State University, San Diego, CA
² Department of Pediatrics, University of California, San Diego, School of Medicine, San Diego, CA
³ Department of Psychiatry and Behavior Sciences, Emory University School of Medicine, Atlanta, GA
⁴ Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
⁵ Sanford Research and Sanford School of Medicine of the University of South Dakota, Sioux Falls, SD
⁶ Department of Nutrition, Gillings School of Global Public Health, University of North Carolina Nutrition Research Institute, Kannapolis, NC
⁷ Center on Alcoholism, Substance Abuse, and Addictions, The University of New Mexico, Albuquerque, NM
⁸ Developmental Cognitive Neuroimaging Laboratory, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA
⁹ Division of Research on Children, Youth, and Families, Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA
¹⁰ Department of Psychiatry, University of Minnesota, Minneapolis, MN

^*Reprint requests: 6330 Alvarado Court, Suite 100, San Diego, CA 92120.6330 Alvarado CourtSuite 100San DiegoCA92120

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Abstract

Objective

To develop and validate a hierarchical decision tree model that combines neurobehavioral and physical measures to identify children affected by prenatal alcohol exposure even when facial dysmorphology is not present.

Study design

Data were collected as part of a multisite study across the US. The model was developed after we evaluated more than 1000 neurobehavioral and dysmorphology variables collected from 434 children (8-16 years of age) with prenatal alcohol exposure, with and without fetal alcohol syndrome, and nonexposed control subjects, with and without other clinically-relevant behavioral or cognitive concerns. The model subsequently was validated in an independent sample of 454 children in 2 age ranges (5-7 years or 10-16 years). In all analyses, the discriminatory ability of each model step was tested with logistic regression. Classification accuracies and positive and negative predictive values were calculated.

Results

The model consisted of variables from 4 measures (2 parent questionnaires, an IQ score, and a physical examination). Overall accuracy rates for both the development and validation samples met or exceeded our goal of 80% overall accuracy.

Conclusions

The decision tree model distinguished children affected by prenatal alcohol exposure from nonexposed control subjects, including those with other behavioral concerns or conditions. Improving identification of this population will streamline access to clinical services, including multidisciplinary evaluation and treatment.

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Keywords : fetal alcohol spectrum disorders (FASD), fetal alcohol syndrome (FAS), clinical identification, prenatal alcohol exposure, screening tool

Abbreviations : ADHD, AE, CIFASD, FAS, FASD, FSIQ, GCA, M, Non-AE

Plan

Methods

Model Development Sample

Model Validation Sample

Decision Tree Development

Results

Accuracy Rate Analyses

Misclassified Subjects

Post-Hoc Analyses

Discussion

Supported by the Collaborative Initiative on Fetal Alcohol Spectrum Disorders, which is funded by National Institute on Alcohol and Alcohol Abuse (U01 AA014834, U01 AA014815, U01 AA014811, F31 AA022261, and T32 AA013525). Additional information about CIFASD can be found at www.cifasd.org. The authors declare no conflicts of interest.