Aydin Semiha, Siebelink Bart M, Crone Matty R, van Ginkel Joost R, Numans Mattijs E, Vermeiren Robert R J M, Michiel Westenberg P
Department of Developmental and Educational Psychology, Leiden University, The Netherlands; Department of Child and Adolescent Psychiatry, Leiden University Medical Centre, The Netherlands; and Department of Public Health and Primary Care, Leiden University Medical Centre, The Netherlands.
Department of Child and Adolescent Psychiatry, Leiden University Medical Centre, The Netherlands.
BJPsych Open. 2022 Apr 7;8(3):e81. doi: 10.1192/bjo.2022.47.
A variety of information sources are used in the best-evidence diagnostic procedure in child and adolescent mental healthcare, including evaluation by referrers and structured assessment questionnaires for parents. However, the incremental value of these information sources is still poorly examined.
To quantify the added and unique predictive value of referral letters, screening, multi-informant assessment and clinicians' remote evaluations in predicting mental health disorders.
Routine medical record data on 1259 referred children and adolescents were retrospectively extracted. Their referral letters, responses to the Strengths and Difficulties Questionnaire (SDQ), results on closed-ended questions from the Development and Well-Being Assessment (DAWBA) and its clinician-rated version were linked to classifications made after face-to-face intake in psychiatry. Following multiple imputations of missing data, logistic regression analyses were performed with the above four nodes of assessment as predictors and the five childhood disorders common in mental healthcare (anxiety, depression, autism spectrum disorders, attention-deficit hyperactivity disorder, behavioural disorders) as outcomes. Likelihood ratio tests and diagnostic odds ratios were computed.
Each assessment tool significantly predicted the classified outcome. Successive addition of the assessment instruments improved the prediction models, with the exception of behavioural disorder prediction by the clinician-rated DAWBA. With the exception of the SDQ for depressive and behavioural disorders, all instruments showed unique predictive value.
Structured acquisition and integrated use of diverse sources of information supports evidence-based diagnosis in clinical practice. The clinical value of structured assessment at the primary-secondary care interface should now be quantified in prospective studies.
在儿童和青少年心理健康护理的最佳证据诊断程序中，使用了多种信息来源，包括转诊者的评估和针对家长的结构化评估问卷。然而，这些信息来源的增量价值仍未得到充分研究。
量化转诊信、筛查、多信息提供者评估和临床医生远程评估在预测心理健康障碍方面的附加预测价值和独特预测价值。
回顾性提取了1259名转诊儿童和青少年的常规病历数据。将他们的转诊信、对长处和困难问卷（SDQ）的回答、发育与幸福感评估（DAWBA）的封闭式问题结果及其临床医生评定版本与精神病学面对面接诊后的分类结果相关联。在对缺失数据进行多次插补后，以上述四个评估节点为预测因素，以心理健康护理中常见的五种儿童疾病（焦虑症、抑郁症、自闭症谱系障碍、注意力缺陷多动障碍、行为障碍）为结果进行逻辑回归分析。计算似然比检验和诊断比值比。
每种评估工具都能显著预测分类结果。除临床医生评定的DAWBA对行为障碍的预测外，依次添加评估工具可改善预测模型。除抑郁和行为障碍的SDQ外，所有工具均显示出独特的预测价值。
结构化获取和综合使用多种信息来源有助于临床实践中的循证诊断。现在应在前瞻性研究中量化初级-二级护理界面结构化评估的临床价值。
