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.

概括 研究表明，对精神病的临床高风险（CHR）的个人早期识别可能是 能够改善疾病课程。研究表明，使用专业访谈早期识别CHR 对于寻求帮助的人（患有减弱的精神病症状）是一种有用的方法。这项工作有两个 主要限制：1）访谈方法的特异性有限，因为只有20％的CHR个人转换为 精神病和2）只有在几个学术中心才能获得进行CHR诊断所需的专业知识。 我们建议开发一种新的精神病症状域敏感（PSD）电池，将其优先考虑任务 显示与定义精神病的症状并与神经生物学系统相关的症状的相关性 与这些症状有关的计算机制。为了促进可访问性，我们使用行为任务 可以通过互联网管理；这将为以后的研究测试奠定舞台 筛选将确定最需要深入评估的人。为了达到这个目标，我们首先需要 确定哪些任务可有效预测疾病课程，以及该策略与已发布的方式相比 预测方法。我们建议招募500名CHR参与者，500名寻求帮助的人和500个人 跨5个站点的健康控制，具有以下目的：目标1A）通过 机器学习（ML）方法的应用到PSD电池的措施。在探索性中 ML分析，我们将确定将PSD与自我报告措施相结合的附加值和 历史鉴定者；目标1b）我们将评估在风险计算器评分方面的群体差异并假设 CHR组的风险计算器得分将与寻求帮助和健康的对照组不同。我们进一步 假设CHR转换器的风险计算器评分将与CHR组显着不同 非会员，寻求帮助和健康的控制。包括寻求帮助小组对 将风险计算器转化为临床实践，其目标是区分最大风险的人 来自具有其他形式的心理病理学的人的精神病； AIM 1C）：评估基线PSD的方式 表现与症状结局有关2年后的检查：1）被视为症状恶化 连续变量和2）转换为精神病。我们假设PSD计算器：1）将预测 症状课程以及2）在转换器和非会员之间观察到的差异将更大 PSD计算器比NAPLS计算器上的计算器。目标2）使用上述ML方法来开发计算器 预测：2a）社会，以及2b）角色功能恶化，两年都在观察到。因为负面 症状与功能性预后相比，症状与阳性症状更密切相关，我们预测 症状机制任务将是两个领域功能下降的最强预测指标。这个项目 将提供下一代CHR电池，与疾病机构相关，并由前沿供电 可用于最早可能检测精神病风险的计算方法。