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J Pediatr. Author manuscript; available in PMC 2017 October 01. Published in final edited form as: J Pediatr. 2016 October ; 177: 121–127.e1. doi:10.1016/j.jpeds.2016.06.047.

A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure Patrick K. Goh, B.S.a, Lauren R. Doyle, B.S.a, Leila Glass, M.S.a, Kenneth L. Jones, M.D.b, Edward P. Riley, Ph.D.a, Claire D. Coles, Ph.D.c,d,j, H. Eugene Hoyme, M.D.e, Julie A. Kable, Ph.D.d,j, Philip A. May, Ph.D.f,g,j, Wendy O. Kalberg, Ph.D.g,j, R. Sowell Elizabeth, Ph.D.h,j, Jeffrey R. Wozniak, Ph.D.i,j, and Sarah N. Mattson, Ph.Da,j

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aCenter

for Behavioral Teratology, Department of Psychology, San Diego State University, San Diego, California bDepartment

of Pediatrics, University of California, San Diego, School of Medicine, San Diego,

California cDepartment

of Psychiatry and Behavior Sciences, Emory University School of Medicine, Atlanta,

Georgia dDepartment

of Pediatrics, Emory University School of Medicine, Atlanta, Georgia

eSanford

Research and Sanford School of Medicine of the University of South Dakota, Sioux Falls, South Dakota

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fDepartment

of Nutrition, Gillings School of Global Public Health, University of North Carolina Nutrition Research Institute, Kannapolis gCenter

on Alcoholism, Substance Abuse and Addictions, The University of New Mexico, Albuquerque, New Mexico

hDevelopmental

Cognitive Neuroimaging Laboratory, Department of Pediatrics, Keck School of Medicine, University of Southern California; Division of Research on Children, Youth, and Families, Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, California

iDepartment

of Psychiatry, University of Minnesota, Minneapolis, Minnesota

jCollaborative

Initiative on Fetal Alcohol Spectrum Disorders

Abstract Author Manuscript

Objective—To develop and validate a hierarchical decision tree model, combining neurobehavioral and physical measures, for identification of children affected by prenatal alcohol exposure even when facial dysmorphology is not present.

Address correspondence to: Sarah N. Mattson, Ph.D., 6330 Alvarado Court, Suite 100, San Diego CA, 92120. [email protected]. Phone: 619-594-7228. Fax: 619-594-1895. Reprint request author: Sarah N. Mattson, Ph.D. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. The authors declare no conflicts of interest.

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Study design—Data were collected as part of a multisite study across the United States. The model was developed after evaluating over 1000 neurobehavioral and dysmorphology variables collected from 434 children (8–16y) with prenatal alcohol exposure, with and without fetal alcohol syndrome (FAS), and non-exposed controls, with and without other clinically-relevant behavioral or cognitive concerns. The model was subsequently validated in an independent sample of 454 children in two age ranges (5–7y or 10–16y). 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.

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Conclusions—The decision tree model distinguished children affected by prenatal alcohol exposure from non-exposed controls, including those with other behavioral concerns or conditions. Improving identification of this population will streamline access to clinical services, including multidisciplinary evaluation and treatment. Keywords Fetal Alcohol Spectrum Disorders (FASD); Fetal Alcohol Syndrome (FAS); Clinical Identification; Prenatal Alcohol Exposure; Screening Tool

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Fetal alcohol spectrum disorders (FASD) are characterized by a range of physical and neurobehavioral changes caused by prenatal alcohol exposure [1, 2] and occur in as many as 4.8% of school-age children [3]. Although fetal alcohol syndrome (FAS) is readily identifiable by trained clinicians [4], the majority of children affected by prenatal alcohol exposure present with significant neurobehavioral impairments without sufficient dysmorphology to merit the diagnosis of FAS. This heterogeneity of physical presentation coupled with an overlap in clinical presentation with other neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder (ADHD), results in under-identification, misdiagnosis, and misunderstanding of the etiology of difficulties faced by children with FASD [5, 2]. Failure to identify alcohol-affected children can adversely affect prognosis and access to tailored services [6]. However, although efforts to identify affected individuals have been moderately successful [7–10], they have not translated easily to clinical settings, perhaps due to the difficulty in differentiating alcohol-affected children from those with similar behavioral concerns without prenatal alcohol exposure and the extensive neuropsychological testing required.

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Thus, there is a need for an efficient but specific primary screening tool for use by pediatricians to identify children with neurobehavioral impairments that may be due to prenatal alcohol exposure. Positive screens would compel referral for additional medical or developmental evaluations or to an FASD clinic for full evaluation. We aimed to develop and validate a clinically relevant, feasible, and accurate (>80%) [11] decision tree model that could differentiate children affected by prenatal alcohol exposure from non-exposed children with and without other clinically relevant behavioral or cognitive concerns or conditions.

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Methods

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Data were collected as part of the Collaborative Initiative on Fetal Alcohol Spectrum Disorders (CIFASD), a multisite, multimethod study of FASD. Data for model development and model validation samples were collected in the second (CIFASD II; 2007–2012) and third (CIFASD III, ongoing) phases of the study, respectively. Both phases included standardized neuropsychological assessment and dysmorphology evaluations [12]. Many of the same measures were included in both phases, although different assessments of general cognitive ability were used (CIFASD II: Wechsler Intelligence Scale for Children-Fourth Edition [WISC-IV] [13]; CIFASD III: Differential Ability Scales-Second Edition [DAS-II] [14]). Both phases included a heterogeneous comparison group (Non-AE group) comprising non-exposed (2 drinks/occasion) typically-developing children and non-exposed children with clinical conditions or concerns (approximately 30–50% of comparison subjects). The most common concerns in the comparison group were related to ADHD, other internalizing and externalizing problems, low IQ, and learning disorders, which are relatively representative of the populations of an outpatient pediatric psychology or psychiatry clinic.

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Prenatal alcohol exposure histories were obtained through retrospective maternal report or social service, legal, or medical records. Subjects were included in the alcohol-exposed (AE) group if a history of heavy prenatal alcohol exposure (>13 drinks/week or >4 drinks/ occasion during pregnancy) was known or when such exposure was suspected in a child with an FAS diagnosis. In many cases, accurate exposure history was unattainable; children were included in the AE group if mothers were known to be “alcoholic” or alcohol abusing during pregnancy. All subjects were evaluated using a standardized dysmorphology examination conducted by a member of the CIFASD Dysmorphology Core [4, 12]. Informed consent and assent were obtained from caregivers and subjects prior to testing. Financial incentive for participation and written feedback was provided after evaluation. The Institutional Review Boards at San Diego State University and other CIFASD sites approved this study. Demographic information is presented in Table I. Model Development Sample

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Data from 434 subjects aged 8–16 years (Mean [M]=12.3) were used in the development of the decision tree model. Thirty-nine (26.7%) subjects in the AE group met criteria for FAS [4, 12]. One hundred forty-seven (51.0%) subjects in the Non-AE group had clinically concerning behaviors or conditions, including 93 (32.3%) subjects who met criteria for ADHD based on the Computerized Diagnostic Interview Schedule for Children- Fourth Edition (C-DISC-4.0) [15–17]. Model Validation Sample Data from 454 subjects aged either 5–7 years (Child; M=6.6, n=165) or 10–16 years (Adolescent; M=13.4, n=289) were used for model validation. 7 (12.7%) subjects in the Child age range and 11 (11.2%) subjects in the Adolescent age range met criteria for FAS. Forty-four (40.0%) subjects in the Child age range and 73 (38.2%) subjects in the Adolescent age range had clinically concerning behaviors or conditions, including 33

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(30.0%) children and 38 (19.9%) adolescents who met criteria for ADHD based on the CDISC-4.0. Decision Tree Development

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Over 1000 composite and subtest scores from measures assessing verbal and nonverbal memory, executive functioning, verbal comprehension, perceptual reasoning, working memory, processing speed, psychopathology, adaptive behavior, and dysmorphology were considered for inclusion in the model. For continuous variables (i.e., neuropsychological measures), cut-off scores based on normed clinical levels of impairment were sequentially refined into binary scores that maximized each measure’s ability to differentiate between AE and Non-AE groups. Categorical variables (i.e., dysmorphology measures) were retained as binary values indicating presence or absence of a feature. Additionally, composite variables that included multiple subscales from a single measure were created (e.g., combining the binary indicators from multiple indexes in the WISC-IV into one variable). These variables were retained as binary values indicating whether criteria was met on a specific number of included subscales, with cutoffs determined through logistic regression.

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Between-subject analyses of variance (ANOVAs) were conducted to obtain the effect size (partial η2) corresponding to the group differences (AE vs. Non-AE) for each variable. Variables with partial η2

A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure.

To develop and validate a hierarchical decision tree model that combines neurobehavioral and physical measures to identify children affected by prenat...
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