CAPER: Computerized Assessment of Psychosis Risk

CAPER：精神病风险的计算机化评估

• 批准号: 9980111
• 负责人: LAUREN M ELLMAN
• 金额: $ 31.7万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 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/9980111
• 关键词: Address; American; Attenuated; Automobile Driving; Behavioral; Biological Markers; Classification; 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; Population; Predictive Value; Preventive Intervention; Primary Prevention; Psychopathology; Psychotic Disorders; 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 practice; cognitive testing; computerized; design; follow-up; functional decline; functional outcomes; help-seeking behavior; high risk; high risk population; improved; machine learning method; new therapeutic target; next generation; prevent; psychotic symptoms; recruit; relating to nervous system; screening; social; trait

Project Summary/Abstract 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 determine an exploratory ML analysis, we will 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 strongly linked t o functional outcome than positive symptoms, we predict that negative symptom 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.

项目摘要/摘要 研究表明，及早发现精神病的临床高危人群或许能够 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 寻求帮助的个人(精神病症状减轻)是一种有用的方法。这项工作主要有两个方面 局限性：1)面谈方法的特异性有限，因为只有20%的CHR个人会转化为精神病， 2)只有少数几个学术中心才能获得进行慢性阻塞性肺疾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，对显示相关性的任务进行优先排序 具有定义精神病的症状，并与神经生物系统和计算 与这些症状有关的机制。为了提高可访问性，我们利用行为任务 通过互联网进行管理；这将为以后的研究测试广泛的筛查奠定基础 找出最需要深入评估的人员。为了实现这一目标，我们首先需要确定哪些任务是 有效地预测疾病的进程，以及这种策略与已发表的预测方法相比有何不同。我们 建议在5个地点招募500名社区卫生责任参与者、500名寻求帮助的个人和500名健康对照人员 其目标如下：目标1)通过机器的应用开发精神病风险计算器 学习(ML)方法，从PSDS电池的措施。在确定探索性ML分析时，我们将 将PSD与自我报告措施和历史预测指标相结合的附加值；目的 1b)我们将评估风险计算器得分的组差异，并假设风险计算器 CHR组的得分与求助组和健康对照组的得分不同。我们进一步假设这种风险 CHR转换器的计算器分数将与CHR非转换器组显著不同，寻求帮助 和健康的对照组。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，其中的目标是区分那些精神病风险最大的人和那些患有其他疾病的人 精神病理学的形式；目标1C)：评估基线PSD表现与症状的关系 2年后的检查结果：1)将症状恶化作为一个连续变量处理；2) 转化为精神病。我们假设PSDS计算器：1)将预测症状进程，2) 在PSDS计算器上观察到的转换器和非转换器之间的差异会更大 而不是在NAPLS计算器上。目标2)如上所述，使用ML方法开发预测：2a的计算器) 社会，和，2B)角色功能退化，都在两年内观察到。因为阴性症状是 与阳性症状相比，与功能结果密切相关，我们预测阴性症状 任务将是这两个领域中功能下降的最强预测因素。该项目将提供 下一代CHR电池，与疾病机制捆绑在一起，由尖端计算提供动力 可用于促进尽早检测精神病风险的方法。