CAPER: Computerized Assessment of Psychosis Risk

CAPER：精神病风险的计算机化评估

• 批准号: 10794659
• 负责人: LAUREN M ELLMAN
• 金额: $ 2.54万
• 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/10794659
• 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; Severities; Site; Specificity; Symptoms; System; Techniques; Test Result; Testing; Training; Translating; United States; Work; Youth; 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; neural; new therapeutic target; next generation; online delivery; prevent; preventive intervention; psychosis risk; psychotic symptoms; recruit; 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.

项目总结/摘要 研究表明，早期识别精神病临床高风险个体可能能够 改善病程。研究表明，通过专门采访来早期识别CHR 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内容 局限性：1）访谈方法的特异性有限，因为只有20%的精神病患者转变为精神病， 2）只有少数几个学术中心才能提供进行乳腺癌诊断所需的专业知识。我们提出 开发新的精神病症状领域敏感（PSDS）电池，优先考虑显示相关性的任务 精神病的症状与神经生物学系统和计算系统有关， 与这些症状有关的机制。为了促进可访问性，我们利用行为任务， 通过互联网管理;这将为以后的研究测试广泛的筛查奠定基础， 确定最需要深入评估的领域。为了达到这个目标，我们首先需要确定哪些任务是 预测疾病进程的有效性以及该策略与已发表的预测方法的比较。我们 拟在5个研究中心招募500名受试者、500名求助者和500名健康对照 目标1A）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PSDS与自我报告措施和历史预测因素相结合的附加值; 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 抑郁症组的得分与求助者和健康对照组有差异。我们进一步假设， 转换者的计算器得分与非转换者的计算器得分显著不同， 健康的对照。包括一个寻求帮助的小组对于将风险计算器转化为 临床实践，其目标是区分那些患有精神病的风险最大的人与其他 精神病理学形式;目的1C）：评价基线PSDS表现与症状性 2年后检查结果：1）症状恶化作为连续变量处理，2） 转化为精神病我们假设PSDS计算器：1）将预测症状过程，2） 在PSDS计算器上观察到的转换器和非转换器之间的差异将更大 在NAPLS计算器上。目标2）使用ML方法，如上所述，开发预测以下内容的计算器：2A） 社会，和，2B）角色功能恶化，都观察了两年。因为阴性症状是 与功能结局密切相关，我们预测阴性症状 任务将是这两个领域功能下降的最强预测因素。该项目将提供 下一代可充电电池，与疾病机制相关，由尖端的计算技术提供动力， 可用于促进尽早检测精神病风险的方法。