1/5 CAPER: Computerized Assessment of ProdromE Risk

1/5 CAPER：ProdromE 风险的计算机化评估

• 批准号: 10371050
• 负责人: James M. Gold
• 金额: $ 59.09万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10371050
• 关键词: Address; American; Attenuated; Automobile Driving; Behavioral; Biological Markers; Clinical; Collaborations; Computing Methodologies; Detection; Deterioration; Diagnosis; Dimensions; Early Diagnosis; Early Intervention; Early identification; Foundations; Frequencies; Functional disorder; Generations; Goals; Human Resources; Individual; Internet; Intervention Trial; Interview; Joints; Link; Longitudinal Studies; Machine Learning; Measures; Methods; Modeling; Neurobiology; Outcome; Participant; Patient Self-Report; Performance; Persons; Population; Predictive Value; Primary Prevention; Psychopathology; Psychoses; Public Health; Publishing; Recording of previous events; Research; Research Personnel; Risk; Role; Sample Size; Secondary Prevention; Sensitivity and Specificity; Severities; Site; Specificity; Symptoms; System; Techniques; Test Result; Testing; Training; Translating; United States; Work; Youth; base; clinical high risk for psychosis; clinical practice; cognitive testing; computerized; design; follow-up; functional decline; functional outcomes; help-seeking behavior; high risk; high risk population; improved; machine learning classification; machine learning method; new therapeutic target; next generation; online delivery; prevent; preventive intervention; psychosis risk; psychotic symptoms; recruit; relating to nervous system; screening; social; trait

Summary Research suggests that early identification of individuals at clinical high risk (CHR) for psychosis may be able to improve illness course. Studies suggest that early identification of CHR using specialized interviews with help-seeking individuals (with attenuated psychosis symptoms) is a useful approach. This work has two major limitations: 1) interview methods have limited specificity as only 20% of CHR individuals convert to psychosis, and 2) the expertise needed to make CHR diagnosis is only accessible in a few academic centers. We propose to develop a new psychosis symptom domain sensitive (PSDS) battery, prioritizing tasks that show correlations with the symptoms that define psychosis and are tied to the neurobiological systems and computational mechanisms implicated in these symptoms. To promote accessibility, we utilize behavioral tasks that could be administered over the internet; this will set the stage for later research testing widespread screening that would identify those most in need of in-depth assessment. To reach that goal we first need determine which tasks are effective for predicting illness course and how this strategy compares to published prediction methods. We propose to recruit 500 CHR participants, 500 help-seeking individuals, and 500 healthy controls across 5 sites with the following Aims: Aim 1A) To develop a psychosis risk calculator through the application of machine learning (ML) methods to the measures from the PSDS battery. In an exploratory ML analysis, we will determine the added value of combining the PSDS with self-report measures and historical predicators; Aim 1B) We will evaluate group differences on the risk calculator score and hypothesize that the risk calculator score of the CHR group will differ from help-seeking and healthy controls. We further hypothesize that the risk calculator score of the CHR converters will differ significantly from groups of CHR nonconverters, help-seeking and healthy controls. The inclusion of a help-seeking group is critical for translating the risk-calculator into clinical practice, where the goal is to differentiate those at greatest risk for psychosis from those with other forms of psychopathology; Aim 1C): Evaluate how baseline PSDS performance relates to symptomatic outcome 2 years later examining: 1) symptomatic worsening treated as a continuous variable, and 2) conversion to psychosis. We hypothesize that the PSDS calculator: 1) will predict symptom course and, 2) that the differences observed between converters and nonconverters will be larger on the PSDS calculator than on the NAPLS calculator. Aim 2) Use ML methods, as above, to develop calculators that predict: 2A) social, and, 2B) role function deterioration, both observed over two years. Because negative symptoms are more strongly linked to functional outcome than positive symptoms, we predict that negative symptom mechanism tasks will be the strongest predictor of functional decline in both domains. This project will provide a next-generation CHR battery, tied to illness mechanisms and powered by cutting-edge computational methods that can be used to facilitate the earliest possible detection of psychosis risk.

总结 研究表明，早期识别精神病临床高风险个体可能是 能改善病程。研究表明，使用专门的访谈进行早期识别 与寻求帮助的个人（精神病症状减轻）是一个有用的方法。这项工作有两个 主要局限性：1）访谈方法的特异性有限，因为只有20%的受访者会转换为 精神病，和2）所需的专业知识，使精神病诊断是只有在少数学术中心。 我们建议开发一种新的精神病症状领域敏感（PSDS）电池，优先考虑的任务， 显示出与定义精神病的症状的相关性，并与神经生物学系统有关， 与这些症状有关的计算机制。为了促进可访问性，我们利用行为任务 可以通过互联网管理;这将为以后的研究测试奠定基础。 筛选，以确定最需要深入评估的人。为了实现这一目标，我们首先需要 确定哪些任务对预测疾病进程有效，以及该策略与已发表的 预测方法我们建议招募500名参与者，500名寻求帮助的人，500名 5个地点的健康对照，目的如下：目的1A）通过以下方法开发精神病风险计算器 机器学习（ML）方法在PSDS电池测量中的应用。以探索性 ML分析，我们将确定PSDS与自我报告措施相结合的附加值， 历史预测因素;目的1B）我们将评估风险计算器评分的组间差异，并假设 风险计算器的得分将不同于寻求帮助和健康对照组。我们进一步 假设转换者的风险计算器得分与转换者的风险计算器得分显著不同。 非转换者、求助者和健康对照者。包括一个寻求帮助的小组是至关重要的， 将风险计算器转化为临床实践，其目标是区分那些风险最大的人， 精神病与其他精神病理学形式的患者;目标1C）：评价基线PSDS 性能与2年后的症状结局相关，检查：1）症状恶化作为 连续变量; 2）转化为精神病。我们假设PSDS计算器：1）将预测 症状过程，2）转换器和非转换器之间观察到的差异将更大， PSDS计算器比NAPLS计算器。目标2）使用ML方法，如上所述，开发计算器 预测：2A）社会，和，2B）角色功能恶化，都观察了两年。因为消极 症状比阳性症状与功能结果的联系更紧密，我们预测阴性症状 症状机制任务将是这两个领域功能下降的最强预测因子。这个项目 将提供下一代锂离子电池，与疾病机制相关联，并由尖端的 计算方法，可用于促进尽早检测精神病风险。