Predicting populations at-risk of developing pathological hoarding

摘要 囤积症（HD），在《诊断和统计手册》中被认为是一种独立的疾病。 精神障碍不到十年，是一种使人衰弱的精神疾病，具有深刻的社会经济 影响。新的数据显示，囤积的严重程度与大量的医疗负担相关。之时尚 临床上显著的囤积行为估计在2%到4%之间，老年人的负担更高。 人口然而，人们认为囤积症的诊断不足。母体R01MH117114 将临床、神经心理学和医学脆弱性评估与独特的流行病学 在线脑健康注册（BHR） 17，评估老年人患有残疾的程度 囤积症状到目前为止，已有超过24，000名受试者接受了与囤积相关的问卷调查。此外，1554 参与者完成了用于验证囤积症状的额外调查。而且 BHR包括认知的纵向客观和主观测量，以及儿童和医疗 历史我们将分类纵向趋势的措施囤积行为学的一个亚群， BHR与囤积障碍和其他精神病理学的临床评估。然后我们将投影整个 BHR人群对纵向趋势进行分类。然后，我们将应用统计推断和技术， 人工智能，以确定囤积心理学的各种趋势的预测因素， 出现了严重的囤积症通过父R01进行的持续招募将允许验证 通过这项工作做出的预测。此外，由于精神病患者人数迅速增加， 使用基于网络的数据收集方法而不是亲自进行临床评估的研究， 研究这些数据收集方法的时间趋势和保真度超出了本报告的范围。 目前的研究。本研究将提供一个概念验证的方法来分析这些数据。 这项工作将由一位受过训练的数学家进行，他的职业生涯将过渡到系统医学。 目标是在计算精神病学领域建立独立的职业生涯。通过以下途径提供的培训 该补助金将准备补充候选人提交一个独立的K01奖的互动 晚年抑郁症和囤积症之间的联系为此将 通过由囤积障碍和系统医学专家提供量身定制的指导， 与各种精神病理学有关，在撰写补助金和负责任的研究行为方面进行了结构化培训， 精神病理学的结构化课程，以及佛罗里达大学的一流计算资源。

ABSTRACT Hoarding Disorder (HD), recognized as an independent illness in the Diagnostic and Statistical Manual of Mental Disorders for less than a decade, is a debilitating psychiatric disorder with profound socioeconomic impacts. Emerging data shows that hoarding severity correlates with substantial medical burden. Prevalence of clinically significant hoarding behavior is estimated to be between 2 and 4%, with a higher burden in the older population. However, it is believed that hoarding disorder is underdiagnosed. The parent R01MH117114 combines in-person clinical, neuropsychological, and medical frailty assessments with a unique epidemiologic resource, the online Brain Health Registry (BHR) 17, to assess the extent of disability in older adults suffering with hoarding symptoms. To date, over 24,000 subjects have taken hoarding-related questionnaires. In addition, 1554 participants have completed additional surveys performed for validation of hoarding symptoms. Moreover, the BHR includes longitudinal objective and subjective measures of cognition, as well as childhood and medical history. We will classify longitudinal trends of measures of hoarding symptomatology in a subpopulation of the BHR with clinical assessments of hoarding disorders and other psychopathologies. We will then project the whole BHR population to classify longitudinal trends. We will then apply statistical inference and techniques from artificial intelligence to identify predictors of various trends of hoarding symptomatology to find predictors of developing severe hoarding symptoms. Ongoing recruitment through the parent R01 will allow for validation of the predictions made through this work. Moreover, as there is a rapid increase in the number of psychiatric studies using web-based data collection methods rather than in-person clinical assessments, the importance of studying the temporal trends and fidelity of these data collection methods extends beyond the scope of the current study. The present study will provide a proof-of-concept approach for analyzing such data. The work will be carried out by a trained mathematician transitioning into systems medicine whose career goal is to establish an independent career in the field of computational psychiatry. The training provided through this grant will prepare the supplement candidate to submit an independent K01 award on the interactions between late life depression and hoarding disorder to the National Institute of Mental Health. This will be done by providing tailored mentoring by experts in hoarding disorder and systems medicine, access to unique datasets related to various psychopathologies, structured training in grant writing and responsible conduct in research, a structured course in psychopathology, and premier computational resources at the University of Florida.