Clinically useful and evidence-based mental health assessment requires the identification of strategies that maximize diagnostic accuracy, inform treatment planning, and make efficient use of clinician and patient time and resources. This study uses classification tree analyses to determine whether parent- and child-report instruments, alone or in combination, can accurately predict diagnoses as measured by the Anxiety Disorders Interview Schedule (ADIS). The ADIS, which is the gold-standard semistructured interview for anxiety disorders in children and adolescents, requires formal training and lengthy administration. Data were collected as part of the standard diagnostic assessment process for 201 patients (ages 5 to 17 years) in an urban outpatient psychiatry specialty clinic. Analyses examined 2 models to determine which predictors reached an acceptable level of diagnostic accuracy for generalized anxiety, social anxiety, and separation anxiety disorders. The first model used scores on a parent- and child-report anxiety measure combined with demographic factors, and the second model incorporated a broad-band measure of child psychopathology and a depression measure into the analysis. Although demographic factors did not emerge as accurate predictors in either model, particular measures, either alone or in combination, were able to predict specific ADIS diagnoses in some cases, allowing for the potential streamlining of ADIS administration. These results suggest that a classification-tree analysis lends itself to the construction of simple algorithms that have high clinical utility and may advance the feasibility and utility of evidence-based assessment strategies in real-world practice settings by balancing cost effectiveness, administration demands, and accuracy. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

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在临床环境中进行循证评估：减轻评估负担，以结构化方式评估儿童和青少年的焦虑状况。

临床上有用且基于证据的心理健康评估需要确定能够最大化诊断准确性，为治疗计划提供信息并有效利用临床医生和患者时间和资源的策略。这项研究使用分类树分析来确定父母和儿童报告工具（单独或组合使用）是否可以准确预测根据焦虑症访谈计划表（ADIS）进行的诊断。ADIS是针对儿童和青少年焦虑症的金标准半结构式访谈，需要接受正规培训和长期管理。在城市门诊精神病专科诊所，对201例患者（5至17岁）进行标准诊断评估，收集了相关数据。分析检查了2个模型，以确定哪些预测因子达到了广泛性焦虑，社交焦虑和分离性焦虑障碍的诊断准确性的可接受水平。第一个模型使用父母和孩子报告焦虑度量的得分并结合人口统计学因素，第二个模型将儿童心理病理学的宽带度量和抑郁症度量纳入了分析。尽管在这两个模型中人口统计学因素都不能作为准确的预测指标，但在某些情况下，单独或组合使用特定的测量方法可以预测特定的ADIS诊断，从而有可能简化ADIS管理。这些结果表明，分类树分析有助于构建具有较高临床实用性的简单算法，并且可以通过平衡成本效益，管理要求和成本，在现实世界中提高基于证据的评估策略的可行性和实用性。准确性。（PsycINFO数据库记录（c）2019 APA，保留所有权利）。