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CAPER: Computerized Assessment of Psychosis Risk

CAPER：精神病风险的计算机化评估

基本信息

批准号：
10569011
负责人：
LAUREN M ELLMAN
金额：
$ 31.7万
依托单位：
TEMPLE UNIV OF THE COMMONWEALTH
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/10569011
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

项目摘要

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/文摘