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: 10576406
• 负责人: PHILIP CORLETT
• 金额: $ 58.51万
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10576406
• 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; 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; 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; neural; new therapeutic target; next generation; novel strategies; online delivery; prevent; preventive intervention; psychosis risk; psychotic symptoms; recruit; 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.

总结 研究表明，如果我们能及早识别出这些疾病的高危人群，我们也许能够 改善疾病的进程，并希望预防疾病的发作。第一代研究 建议通过使用专门的 与寻求帮助的个人（精神病症状减轻）进行面谈是一种很有前途的策略， 探索与疾病进展相关的机制，了解病因，并确定新的 治疗目标。这项工作有两个主要的局限性：1）采访方法的特异性有限， 15-20%的精神分裂症患者转变为精神病，2）进行精神分裂症诊断所需的专业知识是 只有在少数大都市中心才能进入，需要经过广泛培训的工作人员。在这里，我们的目标是 为一种新的可持续发展评估方法奠定基础，这将增加可及性， 预测值我们建议开发一种新的精神病症状领域敏感（PSDS）电池， 优先考虑与定义精神病的症状相关的任务（积极利用 精神障碍的具体过程，而不是特质脆弱性标志）和相关的，这是联系在一起， 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，我们利用廉价的行为任务，可以通过互联网管理;这将设置 后期研究阶段测试在寻求帮助和非寻求帮助中的广泛筛查 这将确定哪些人最需要深入评估。在此之前， 然而，完成这项任务后，有必要确定哪些任务对预测疾病进程有效 以及该策略与第一代预测方法的比较。我们计划招募500名 参与者，500名寻求帮助的人，500名健康对照，在5个地点和目标1中，制定一个 PSDS电池风险计算器基于被证明对即将发生的转换最敏感的措施。 此外，将寻求帮助的比较组包括在内对于将PSDS计算器转换为 临床实践，其目标是区分那些发展为精神障碍的风险最大的人 其他形式的精神病理学。在目标2中，我们将比较PSDS的敏感性和特异性 北美前驱期研究的风险计算器 （NAPLS）风险计算器（黄金标准的第一代工具）用于预测精神病转化 一个2年期。最后，在目标3中，该研究将确定PSDS是否预测了 2年的课程。预测诊断是重要的，但能够提供早期干预，以限制 具有精神病特征的残疾是一个优先事项。这个项目将回答初步的问题 这是下一代锂电池所必需的，与疾病机制有关，由尖端的 计算方法，可用于促进尽早检测精神病风险。