Individualized approaches to determining likelihood of ASD caseness

确定 ASD 病例可能性的个体化方法

• 批准号: 10522317
• 负责人: Somer L. Bishop
• 金额: $ 82.33万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522317
• 关键词: Affect; Age; Assessment tool; Attention deficit hyperactivity disorder; Autism Diagnosis; Autism diagnostic; Behavioral; Case Study; Characteristics; Child; Child Behavior Checklist; Classification; Clinical; Clinical Research; Data; Data Analyses; Decision Making; Decision Trees; Development; Developmental Delay Disorders; Diagnosis; Diagnostic; Evaluation; Explosion; Female; Goals; Guidelines; Health; Heterogeneity; Individual; Infant; Intellectual functioning disability; Interview; Knowledge; Language; Language Disorders; Light; Literature; Machine Learning; Male Adolescents; Measures; Methods; Paper; Parents; Participant; Performance; Persons; Phenotype; Population; Population Characteristics; Predictive Value; Problem behavior; Procedures; Process; Property; Psychometrics; Questionnaires; Recommendation; Reporting; Research; Research Personnel; Research Project Grants; Resources; Risk; Sampling; Screening procedure; Sensitivity and Specificity; Services; Specific qualifier value; Speech; Standardization; Stretching; Subgroup; Symptoms; System; Techniques; Testing; Time; Toddler; Triage; Update; Validation; Weight; Work; Youth; artificial neural network; autism diagnostic observation schedule; autism spectrum disorder; autistic children; base; behavioral phenotyping; biological sex; clinical diagnosis; clinically significant; cognitive ability; comorbidity; diagnostic accuracy; diagnostic platform; diagnostic tool; ethnic minority; girls; improved; individual variation; instrument; next generation; personalized approach; phrases; prevent; racial and ethnic; recruit; screening; skills; social; social communication; study characteristics; tool

PROJECT SUMMARY Ongoing development and validation of screening and diagnostic tools has been a major focus of research in autism spectrum disorders (ASD) during the past 30 years. Diagnostic validity of several widely used tools has been established by showing that, in most cases, children with ASD score above established cut-offs, whereas children with non-ASD score below cut-offs. However, a growing body of literature indicates that sensitivity and specificity of ASD symptom measures varies significantly based on the characteristics of the study population. This means that the ability of a given tool to differentiate children with and without ASD is variable, and is affected by an individual's demographic (age, sex), developmental (cognitive ability, language level), and/or behavioral (clinically significant behavior problems) profile. The long-term goal of the proposed research is to transform screening and diagnostic practices through increased individualization of measure selection and interpretation. Phenotypic heterogeneity of youth referred for possible ASD is a well-recognized challenge that prevents one-size-fits-all assessment approaches from being validly employed. Yet, with ever-growing demands for such services, clinical and research entities increasingly rely on specified batteries to make decisions about triage and diagnosis. While use of standardized tools offers many advantages, uniform application or interpretation of specific instruments disregards the vast individual heterogeneity that is a hallmark of ASD. Thus, the field is in need of updated practices, wherein tools are selected, combined, and interpreted in the context of an individual child's presentation, with specific reference to how likely it is that scores on a given test or combination of tests will indicate ASD caseness for that child. Toward this end, the proposed secondary data analysis will identify which tools, and combinations of tools, work best for identifying ASD in sub-groups of youth with shared demographic, developmental, and/or behavioral phenotypes (e.g., in toddler girls with phrase speech vs. verbally fluent adolescent boys with clinically elevated behavior problems). We will analyze data from several widely-used ASD measures, aggregated from more than 17,500 children between the ages of 18 months and 17 years, 11 months. These youth were either clinically referred for ASD diagnostic assessment, assessed due to heightened risk for ASD or other developmental delays, or recruited for ASD-focused research projects. They were assigned a best-estimate diagnosis of ASD or non-ASD (e.g., intellectual disability, ADHD, language disorder) by expert clinicians or clinical-researchers following a comprehensive assessment. By considering the interplay between individual characteristics and instrument scores in the largest sample to date, the proposed study will move the field toward more individualized approaches for establishing ASD caseness. Findings from this study will also directly inform the development of the next generation of tools, procedures, and electronic platforms for diagnostic assessment of ASD.

项目摘要 筛查和诊断工具的持续开发和验证一直是研究的主要焦点， 过去30年自闭症谱系障碍（ASD）。几种广泛使用的工具的诊断有效性 通过显示，在大多数情况下，ASD评分高于既定截止值的儿童， 非ASD评分低于临界值的儿童。然而，越来越多的文献表明，敏感性和 ASD症状测量的特异性基于研究人群的特征而显著变化。 这意味着，一个给定的工具，以区分儿童与非ASD的能力是可变的， 根据个人的人口统计学（年龄，性别），发展（认知能力，语言水平）和/或行为 （临床显著行为问题）特征。 拟议研究的长期目标是通过以下方式改变筛查和诊断实践： 措施选择和解释更加个性化。青年的表型异质性 对于可能的ASD是一个公认的挑战，防止一刀切的评估方法， 有效就业。然而，随着对此类服务的需求不断增长，临床和研究实体 越来越多地依赖指定的电池来做出有关分诊和诊断的决策。使用标准化 工具提供了许多优点，统一应用或解释特定的文书无视广大 个体异质性是ASD的标志。因此，该领域需要更新的做法， 是在个别儿童的陈述中选择、组合和解释的， 一项测试或多项测试的分数表明该儿童患有ASD的可能性有多大。 为此，拟议的二级数据分析将确定哪些工具和工具组合有效 最适合在具有相同人口统计学、发育和/或行为特征的青少年亚组中识别ASD 表型（例如，在有短语言语的幼儿女孩与有临床升高的言语流利的青春期男孩中， 行为问题）。我们将分析来自几个广泛使用的ASD测量的数据，这些数据来自超过 17 500名18个月至17岁11个月的儿童。这些年轻人要么在临床上 由于ASD或其他发育风险增加而被转诊进行ASD诊断评估 延迟，或招募ASD为重点的研究项目。他们被指定为ASD的最佳估计诊断 或非ASD（例如，智力残疾、ADHD、语言障碍）的治疗 经过全面评估。通过考虑个人特征和 仪器分数在最大的样本日期，拟议的研究将朝着更加个性化的领域 建立ASD病例的方法。这项研究的结果也将直接告知发展， 下一代ASD诊断评估的工具、程序和电子平台。