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Multicenter Study
. 2016 Oct;177:121-127.e1.
doi: 10.1016/j.jpeds.2016.06.047. Epub 2016 Jul 29.

A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure

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Free PMC article
Multicenter Study

A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure

Patrick K Goh et al. J Pediatr. .
Free PMC article

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.

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

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Decision tree to identify youth as alcohol-exposed (AE) or non-exposed (Non-AE). Ovals, actors; diamonds, action steps; circles, each action step; and boxes, ending points in the decision tree. If a child presents directly to a pediatrician with a suspicion of prenatal alcohol exposure or concerning physical or behavioral findings, the pediatrician would work through the decision tree model by assessing for dysmorphology and adaptive behavior using the VABS. A child assessed by a psychologist would need to be referred to a pediatrician only if IQ was less than 92 or there were CBCL scores in the clinical range. 1CBCL domains included Somatic Complaints, Social Problems, Thought Problems, Attention Problems, Rule-Breaking Behavior, and Aggressive Behavior 2FAS diagnoses requires at least two of three KEY facial features (palpebral fissure length ≤10th percentile; philtrum lipometer Score=4 or 5; vermilion border lipometer score=4 or 5), and either presence of head circumference ≤10th percentile OR height and/or weight ≤10th percentile [–26] 3If a child was not diagnosed with FAS, they would be assessed for the number of key facial features present (palpebral fissure length ≤10 th percentile; philtrum lipometer Score=4 or 5; vermilion border lipometer score=4 or 5) 4VABS (1) and VABS (2) refer to the same variables from the Vineland Adaptive Behavior Scale (Communication, Socialization, and Daily Living Skills), but used at two different decision points in the decision tree model. The VABS has been identified by the American Academy of Pediatrics as a valid assessment procedure for pediatricians to assess adaptive behavior in clinical populations (https://www.aap.org/en-us/advocacy-and-policy/aap-health-initiatives/fetal-alcohol-spectrum-disorders-toolkit/Pages/Diagnostic-Tools.aspx) 5Extended features are specified as ptosis and incomplete extension of one or more digits [24]

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