2/5 CAPER Computerized assessment of psychosis risk

2/5 CAPER 精神病风险的计算机化评估

基本信息

批准号：
10399414
负责人：
VIJAY A MITTAL
金额：
$ 39.57万
依托单位：
NORTHWESTERN UNIVERSITY
依托单位国家：
美国
项目类别：
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/10399414
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

项目摘要

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 historical analysis, we will determine the added value of combining the PSDS with self-report measures and 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 are more strongly linked to functional outcome than positive symptoms, we predict that negative 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，早期识别精神病临床高风险（CHR）个体可能是能够改善病程。研究表明，通过专门访谈可以早期识别 CHR 与寻求帮助的人（精神病症状减轻）联系是一种有用的方法。这部作品有两个主要局限性：1) 访谈方法的特异性有限，因为只有 20% 的 CHR 个体转变为精神病，2) 只有少数学术中心才能获得 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（PSDS）电池，优先考虑以下任务：显示与定义精神病的症状的相关性并与神经生物系统相关与这些症状有关的计算机制。为了促进可访问性，我们利用行为任务可以通过互联网进行管理；这将为以后广泛的研究测试奠定基础筛选将确定那些最需要深入评估的人。为了实现这个目标，我们首先需要确定哪些任务可有效预测病程以及该策略与已发表的策略相比如何预测方法。我们建议招募 500 名 CHR 参与者、500 名寻求帮助的个人和 500 名跨 5 个地点的健康控制，目标如下：目标 1A) 通过以下方式开发精神病风险计算器：机器学习 (ML) 方法在 PSDS 电池测量中的应用。在一次探索性的 ML历史分析，我们将确定PSDS与自我报告措施相结合的附加值预测因子；目标 1B）我们将评估风险计算器分数的组间差异并做出假设 CHR 组的风险计算器分数将不同于寻求帮助组和健康对照组。我们进一步假设 CHR 转换器的风险计算器分数将与 CHR 组显着不同非皈依者、寻求帮助和健康控制。纳入寻求帮助小组对于将风险计算器转化为临床实践，其目标是区分那些风险最大的人患有其他形式精神病理学的精神病；目标 1C)：评估基线 PSDS 表现与 2 年后检查的症状结果相关：1) 症状恶化被视为连续变量，以及 2) 转变为精神病。我们假设 PSDS 计算器： 1) 将预测症状过程，2) 转化者和非转化者之间观察到的差异会更大 PSDS 计算器优于 NAPLS 计算器。目标 2) 使用如上所述的 ML 方法来开发计算器预测：2A）社交，以及，2B）角色功能恶化，两者均在两年内观察到。因为消极与积极症状相比，与功能结果的联系更紧密，我们预测消极机制任务将是这两个领域功能衰退的最强预测因子。该项目将提供下一代 CHR 电池，与疾病机制相关并由尖端技术提供动力可用于促进尽早检测精神病风险的计算方法。