4/5 CAPER: Computerized assessment of psychosis risk

4/5 CAPER：精神病风险的计算机化评估

• 批准号: 10361301
• 负责人: GREGORY P STRAUSS
• 金额: $ 40.23万
• 依托单位: UNIVERSITY OF GEORGIA
• 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/10361301
• 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.

摘要 研究表明，对精神病临床高危人群的早期识别可能是 能够改善病程。研究表明，通过专门的访谈及早识别慢性阻塞性肺疾病 与寻求帮助的个人(精神病症状减轻)是一种有用的方法。这部作品有两部 主要限制：1)面谈方法的特异性有限，因为只有20%的CHR个人转换为 2)只有少数几个学术中心才能获得进行慢性阻塞性肺病诊断所需的专业知识。 我们建议开发一种新的精神病症状领域敏感(PSDS)电池，对以下任务进行优先排序 显示出与定义精神病的症状的相关性，并与神经生物系统和 与这些症状有关的计算机制。为了提高可访问性，我们利用行为任务 这可以通过互联网进行管理；这将为以后的广泛研究测试奠定基础 进行筛选，以确定哪些人最需要进行深入评估。要实现这一目标，我们首先需要 确定哪些任务对预测病程是有效的，以及该策略与已公布的策略相比如何 预测方法。我们建议招募500名社区责任参与者，500名寻求帮助的个人和500名 5个地点的健康对照，目标如下：目标1)开发一个精神病风险计算器，通过 机器学习(ML)方法在PSDS电池检测中的应用。在探索性的 ML分析，我们将确定PSDS与自我报告措施相结合的附加值 历史预测者；目标1B)我们将评估风险计算器得分和假设方面的组差异 CHR组的风险计算器得分将与求助和健康对照组不同。我们进一步 假设CHR转换器的风险计算器分数将与CHR组显著不同 不转换、寻求帮助和健康控制。加入一个寻求帮助的小组对于 将风险计算器转化为临床实践，目标是区分那些风险最高的人 来自其他形式精神病理学的精神病；目标1C)：评估基线PSD 表现与2年后检查的症状结果有关：1)症状恶化被视为 连续变量，以及2)转化为精神病。我们假设PSD计算器：1)将预测 症状进程和，2)在变流器和非变流器之间观察到的差异将在 PSDS计算器比NAPLS计算器更好。目的2)如上所述，使用ML方法开发计算器 这预示着：社交功能和角色功能退化，这两种情况都是在两年内观察到的。因为负面的 症状与功能结局的联系比阳性症状更强，我们预测阴性症状 症状机制任务将是这两个领域中功能下降的最强预测因子。这个项目 将提供下一代CHR电池，与疾病机制捆绑在一起，由尖端技术提供动力 可用于促进尽早检测精神病风险的计算方法。