Trajectories and Predictors in the Clinical High Risk for Psychosis Population: Prediction Scientific Global Consortium (PRESCIENT)

精神病临床高风险人群的轨迹和预测因素：预测科学全球联盟 (PRESCIENT)

• 批准号: 10256746
• 负责人: Christopher Barnaby Nelson
• 金额: $ 602.5万
• 依托单位: UNIVERSITY OF MELBOURNE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-08 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10256746
• 关键词: Australia; Biological; Biological Markers; Caring; Cessation of life; Clinic; Clinical; Clinical Data; Clinical Research; Clinical Services; Clinical Trials; Clinical assessments; Collaborations; Collection; Country; Data; Data Collection; Data Set; Decision Making; Denmark; Disease Progression; Distress; Early Intervention; Early treatment; Economic Burden; Ensure; Funding; Future; Genetic; Germany; Goals; Health; Heterogeneity; Hong Kong; Impairment; Infrastructure; International; Intervention; Korea; Measures; Mental Health Services; Methodology; Modeling; Monitor; National Institute of Mental Health; Netherlands; Neurobiology; Neurocognition; Neurocognitive; Outcome; Patients; Performance; Population; Predictive Value; Primary Health Care; Procedures; Psychoses; Psychotic Disorders; Recovery; Registries; Research; Research Infrastructure; Resource Allocation; Risk; Risk Estimate; Sampling; Service setting; Services; Singapore; Site; Specialist; Speech; Standardization; Stratification; Switzerland; Symptoms; System; Targeted Research; Technology; Testing; Time; Trust; United Kingdom; Validation; Work; Youth; base; biopsychosocial; clinical care; clinical infrastructure; clinical practice; clinical translation; cohort; comparison group; computerized data processing; digital; disability; follow-up; functional outcomes; health care service; health care settings; help-seeking behavior; high risk; high risk population; improved; individual patient; international center; multimodal data; multimodality; neuroimaging; neurophysiology; novel strategies; outcome prediction; patient stratification; personalized medicine; polygenic risk score; predictive modeling; premature; programs; recruit; symptomatology; theories; therapy development; tool; treatment effect; treatment response; treatment strategy; treatment trial

PROJECT SUMMARY/ABSTRACT Psychotic illnesses usually first emerge in young people and result in widespread suffering, protracted disability, premature death, and a huge economic burden. Early intervention represents a vital strategy to reduce this burden. Psychotic disorders are preceded by a prodromal period of distress, impaired functioning and subthreshold symptomatology. Our original research operationally defined the Clinical High Risk (CHR) state, which predicts a substantially increased risk of incipient psychosis. There is substantial heterogeneity in clinical trajectories in the CHR population. The field is currently unable to reliably identify these trajectories early on, particularly on an individual patient level. The models to date (using clinical, neurocognitive, neuroimaging, neurobiological and genetic data) have yielded only modest predictive value for conversion to psychotic disorder and other outcomes. This presents a challenge for targeted intervention development and developing robust aetiological models. The current project seeks to develop more robust prediction models for a range of outcomes in the CHR population (conversion to psychotic disorder, persistent and incident non-psychotic disorder, non-remission of CHR status, persistent negative symptoms, full recovery, functional outcome) and introduce validated tools for use in clinical practice. These prediction models and associated clinical tools will be developed using multimodal data consisting of biomarkers (neuroimaging, neurocognition, neurophysiology, biospecimens), clinical data, and digital momentary assessments. The prediction models will facilitate selection of CHR patients for enrolment in clinical trials, serve as measures of early treatment effects, and monitor disease progression and clinical and functional outcomes. The project is based on four pillars: 1. An existing nationwide clinical infrastructure (network) to support recruitment and follow up of a large cohort of CHR young people (n=1000) over a short timeframe (2 year recruitment period, 2 year follow up), as well as a clinical comparison group (n=300). 2. Use of this dataset to: validate existing and forthcoming prediction models and develop new, more refined prediction models using recent methodological advances and exploratory biomarkers. 3. Recruitment of an independent CHR sample across international centres for external validation of models generated in the Australian network to ensure generalizability of findings. Alternatively, these sites could be used as additional spokes in the network, with alternative data sets used for external validation purposes (see 2.5.4). This network of sites and research specialization will provide the clinical research infrastructure for future treatment trials in this clinical population informed by findings of the current program of work. 4. Unique track record as pioneers of the CHR field and expertise in state-of-the-art predictive modelling, including pioneering new approaches to prediction (dynamic prediction, multimodal probabilistic prediction, network theory), and use of digital technologies to support collection of requisite data. We also have unrivalled track record in management of multisite research networks in this clinical population and rapid recruitment locally, nationally, and internationally.

项目总结/摘要 精神疾病通常首先出现在年轻人中，导致广泛的痛苦、长期残疾、过早死亡和巨大的经济负担。早期干预是减轻这一负担的重要战略。精神障碍之前有一个前驱期的痛苦，受损的功能和阈下痴呆症。我们最初的研究在操作上定义了临床高风险（Clinical High Risk）状态，该状态预测了早期精神病的风险大幅增加。在慢性阻塞性肺病人群中，临床轨迹存在实质性异质性。该领域目前无法在早期可靠地识别这些轨迹，特别是在个体患者水平上。迄今为止的模型（使用临床，神经认知，神经影像学，神经生物学和遗传数据）仅对转化为精神病性障碍和其他结果产生了适度的预测价值。这对有针对性的干预措施的发展和发展强大的病因学模型提出了挑战。目前的项目旨在开发更强大的预测模型，用于一系列的结果在精神病性障碍人群（转换为精神病性障碍，持续性和偶发性非精神病性障碍，非缓解的精神病性障碍状态，持续的阴性症状，完全恢复，功能结果），并引入验证工具用于临床实践。这些预测模型和相关的临床工具将使用多模态数据开发，包括生物标志物（神经成像、神经认知、神经生理学、生物标本）、临床数据和数字瞬时评估。这些预测模型将有助于选择入组临床试验的慢性胰腺炎患者，作为早期治疗效果的衡量标准，并监测疾病进展以及临床和功能结局。该项目基于四大支柱： 1.现有的全国性临床基础设施（网络），以支持在短时间内（2年招募期，2年随访）招募和随访大量年轻人（n=1000），以及临床对照组（n=300）。 2.使用此数据集：验证现有和即将推出的预测模型，并利用最新的方法学进展和探索性生物标志物开发新的、更精细的预测模型。 3.在各国际中心招募独立的澳大利亚网络样本，对澳大利亚网络生成的模型进行外部验证，以确保调查结果的普遍性。或者，这些站点可以用作网络中的额外辐条，并将替代数据集用于外部验证目的（见2.5.4）。该研究中心和研究专业化网络将为该临床人群的未来治疗试验提供临床研究基础设施，这些临床试验由当前工作计划的结果提供信息。 4.作为预测领域的先驱者和最先进的预测建模专业知识的独特记录，包括开创新的预测方法（动态预测，多模态概率预测，网络理论），以及使用数字技术来支持必要数据的收集。我们在管理临床人群的多中心研究网络以及在当地、国内和国际上快速招募方面也有着无与伦比的记录。