5/5 CAPER: Computerized Assessment of Psychosis Risk

5/5 CAPER：精神病风险的计算机化评估

• 批准号: 10574998
• 负责人: PHILIP CORLETT
• 金额: $ 5.99万
• 依托单位: YALE UNIVERSITY
• 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 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10574998
• 关键词: Address; American; Attenuated; Automobile Driving; Behavioral; Biological Markers; Characteristics; Clinical; Collaborations; Computing Methodologies; Detection; Diagnosis; Dimensions; Early Diagnosis; Early Intervention; Etiology; Foundations; Frequencies; Functional disorder; Generations; Goals; Gold; Human Resources; Individual; Internet; Intervention Trial; Interview; Joints; Link; Longitudinal Studies; Machine Learning; Measures; Methods; Modeling; Neurobiology; Onset of illness; Outcome; Participant; Patient Self-Report; Performance; Persons; Population; Predictive Value; Primary Prevention; Process; Psychopathology; Psychoses; Psychotic Disorders; Public Health; Recording of previous events; Research; Research Personnel; Risk; Risk Assessment; 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; comparison group; computerized; design; disability; disorder risk; follow-up; functional decline; functional outcomes; help-seeking behavior; high risk; high risk population; improved; machine learning classification; machine learning method; metropolitan; new therapeutic target; next generation; novel strategies; online delivery; prevent; preventive intervention; psychosis risk; psychotic symptoms; recruit; relating to nervous system; screening; social; tool; trait

Summary Research suggests that if we can identify individuals at-risk for these disorders early, we may be able to improve the course of illness and hopefully prevent illness onset all together. A first generation of studies suggest that the approach of identifying those at clinical high-risk (CHR), through the use of specialized interviews with help-seeking individuals (with attenuated psychosis symptoms) is a promising strategy for exploring mechanisms associated with illness progression, understanding etiology, and identifying new treatment targets. This work has two major limitations: 1) interview methods have limited specificity as only 15-20% of CHR individuals convert to psychosis, and 2) the expertise needed to make CHR diagnosis is only accessible in a handful of metropolitan centers, and requires extensively trained staff. Here, we aim to lay the foundation for a new approach to CHR assessment that will increase accessibility, and positive predictive value. We propose to develop a new psychosis symptom domain sensitive (PSDS) battery, prioritizing tasks that show correlations with the symptoms that define psychosis (actively tapping into psychotic disorder-specific processes, rather than to trait vulnerability signs) and relatedly, that are tied to the neurobiological systems and computational mechanisms implicated in these symptoms. To promote accessibility, we utilize inexpensive behavioral tasks that could be administered over the internet; this will set the stage for later research testing widespread screening in help-seeking as well as non-help seeking populations, that would identify those most in need of in-depth assessment. Before this can be accomplished however, it is necessary to determine which tasks are effective for predicting illness course and how this strategy compares to the first-generation prediction methods. We propose to recruit 500 CHR participants, 500 help-seeking individuals, and 500 healthy controls across 5 sites and in Aim 1, develop a PSDS battery risk calculator based on measures that prove to be most sensitive to imminent conversion. Further, the inclusion of a help-seeking comparison group is critical for translating the PSDS calculator into clinical practice, where the goal is to differentiate those at greatest risk for developing a psychotic disorder from others forms of psychopathology. In Aim 2, we will compare the sensitivity and specificity of the PSDS risk-calculator to the North American Prodromal Study (NAPLS) risk-calculator (a gold-standard first-generation tool) in the prediction of psychosis conversion over a 2 year- period. Last, in Aim 3, the study will determine if the PSDS predicts functional outcomes over the course of 2 years. Predicting diagnosis is important but being able to provide early intervention to limit the disability characteristic of psychosis is a priority. This project will answer the preliminary questions necessary for 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，如果我们能尽早确定这些疾病的人，我们也许可以 改善疾病的进程，并希望可以一​​起疾病开始。第一代研究 建议通过使用专业人士来识别临床高风险（CHR）的方法 与寻求帮助的人（患有减弱精神病症状）的访谈是一种有前途的策略 探索与疾病进展，理解病因和识别新的机制 治疗目标。这项工作有两个主要局限性：1）访谈方法的特异性有限 15-20％的CHR个人转化为精神病，2）进行CHR诊断所需的专业知识是 只能在少数大都会中心访问，并且需要经过广泛培训的员工。在这里，我们的目标是 为一种新的CHR评估方法奠定了基础，以提高可访问性，并积极 预测价值。我们建议开发一种新的精神病症状域敏感（PSD）电池， 优先考虑与定义精神病的症状相关的任务（主动利用 精神病特定的过程，而不是特质脆弱性迹象），而与之相关的是 神经生物学系统和计算机制与这些症状有关。促进 可访问性，我们利用可以通过Internet管理的廉价行为任务；这将设置 以后研究测试在寻求帮助以及非求助的阶段 人群，这将确定最需要深入评估的人。在此之前可以 但是，完成的是，有必要确定哪些任务有效地预测疾病课程 以及该策略与第一代预测方法的比较。我们建议招募500 chr 参与者，500名寻求帮助的人，以及500个网站上的500个健康对照，在AIM 1中，开发了一个 PSD电池风险计算器基于对即将转换最敏感的措施。 此外，包含寻求帮助比较组对于将PSD计算器转换为 临床实践，目标是区分患有精神病的风险最大的人 来自其他形式的心理病理学。在AIM 2中，我们将比较PSD的灵敏度和特异性 北美前驱研究的风险计算机 （NAPL）在预测精神病转化的风险计算器（金标准的第一代工具） 2年。最后，在AIM 3中，研究将确定PSD是否预测 课程2年。预测诊断很重要，但能够提供早期干预以限制 精神病的残疾特征是当务之急。该项目将回答初步问题 下一代CHR电池所需的必要 计算方法，可用于促进最早可能检测精神病风险。