A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure.
A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure.
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DOI：
10.1016/j.jpeds.2016.06.047
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Mattson SN
中科院分区：
文献类型：
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作者：
Goh PK;Doyle LR;Glass L;Jones KL;Riley EP;Coles CD;Hoyme HE;Kable JA;May PA;Kalberg WO;Sowell ER;Wozniak JR;Mattson SN
开发和验证一个分层决策树模型，结合神经行为和生理测量，即使在没有面部畸形的情况下，也可以识别受产前酒精暴露影响的儿童。数据是作为全美多地点研究的一部分收集的。该模型是在评估了从434名产前酒精暴露(8-16岁)儿童(8-16岁)收集的1000多个神经行为和畸形变量后开发的，这些儿童有或没有胎儿酒精综合征(FAS)，以及没有暴露在酒精环境中的对照组，有或没有其他与临床相关的行为或认知问题。该模型随后在两个年龄段(5-7岁或10-16岁)的454名儿童的独立样本中得到验证。在所有的分析中，用Logistic回归检验每个模型步骤的判别能力。计算分类准确率和阳性预测值和阴性预测值。该模型由4个测量变量组成(2份家长问卷、一份智商分数和一次体检)。开发和验证样本的总体准确率都达到或超过了我们总体准确率80%的目标。决策树模型将受产前酒精暴露影响的儿童与未接触酒精的儿童区分开来，包括那些有其他行为问题或疾病的儿童。改善对这一人群的识别将简化获得临床服务的机会，包括多学科评估和治疗。
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. 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. 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. 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.