Refining and Validating Borderline Personality Disorder Phenotypes Through Factor Mixture Modeling

通过因子混合模型细化和验证边缘性人格障碍表型

• 批准号: 9911299
• 负责人: Benjamin Norman Johnson
• 金额: $ 2.54万
• 依托单位: PENNSYLVANIA STATE UNIVERSITY, THE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-10 至 2020-07-01
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9911299
• 关键词: Address; Affect; Algorithms; Archives; Assessment tool; Behavior; Big Data; Biological Markers; Borderline Personality Disorder; Categories; Cellular Phone; Clinical; Clinical Sciences; Computer Models; Consultations; Data; Data Analyses; Diagnostic; Dimensions; Disease; Ecological momentary assessment; Educational workshop; Emergency department visit; Emotional; Ensure; Environment; Factor Analysis; Fellowship; Foundations; Funding; Gender; Goals; Health Care Costs; Heterogeneity; Impairment; Individual; Inpatients; Intervention; Interview; Life; Machine Learning; Mental disorders; Methodology; Modeling; Monitor; National Institute of Mental Health; Occupational; Onset of illness; Outcome; Outcome Study; Outpatients; Patients; Pattern; Pennsylvania; Phenotype; Prediction of Response to Therapy; Process; Psychopathology; Public Health; Publications; Research; Research Personnel; Resources; Risk; Sampling; Scientist; Self Concept; Severities; Source; Structure; Students; Suicide; Surveys; Symptoms; Techniques; Testing; Time; Training; Treatment Effectiveness; Treatment outcome; Universities; Validation; Variant; Work; accomplished suicide; career; classification algorithm; comorbidity; cost; data archive; effective therapy; flexibility; health care service utilization; improved; innovation; novel; personalized intervention; personalized medicine; personalized predictions; prospective; prototype; psychologic; recruit; response; skills; social; sound; success; suicidal risk; theories; therapy development; tool

The proposed research seeks to clarify the symptomatic heterogeneity of borderline personality disorder (BPD) by examining BPD phenotypes through advanced latent variable modeling. A second, innovative aim is to validate these findings through intensive longitudinal assessment in daily life. BPD is associated with high rates of emergency room visits and costly healthcare service utilization, affecting 10-20% of psychiatric outpatients and 20-40% of psychiatric inpatients. BPD also contributes to impaired social and occupational functioning and significant suicide risk, with 1 in 10 individuals with BPD completing suicide. Recent research has aimed to enhance treatment effectiveness for BPD by identifying prototypical patterns of symptom manifestation that may suggest ideographic treatment targets. However, no research has simultaneously included: a) a sufficiently large patient sample; b) ecologically sound validation of results; and c) use of appropriate statistical techniques. The proposed project builds on this research through two aims. Aim 1: Utilize a model comparison approach to identify BPD phenotypes in a large psychiatric outpatient sample assessed via semi-structured diagnostic interviews (Study 1). Aim 2: Validate the results of Study 1 by applying phenotype classification algorithms produced in Study 1 to a smaller sample of patients who have completed 21 days of momentary surveys on symptoms and clinical outcomes (Study 2). To address Aim 1, factor mixture modeling (FMM)—a novel, flexible, and integrative latent variable modeling approach—will be compared to standard factor analysis and latent class analysis in order to evaluate the dimensional and categorical structure of BPD. We expect a single-factor, multi-class FMM will best explain heterogeneity in BPD, over and above other sources of heterogeneity (e.g., gender, comorbidity). To address Aim 2, we will use a prototype-matching approach to algorithmically assign patients in the validation sample to phenotypes identified in Aim 1 and determine their predictive validity in terms of daily clinical outcomes. Results of this project will provide empirically grounded personalized prediction tools for BPD intervention and treatment development, in line with the NIMH’s goal of “developing, testing, and refining tools and methodologies… for personalized risk and trajectory prediction and intervention.” This fellowship will allow the applicant to receive tailored consultation from experts in methodology, data analysis, and BPD theory and assessment, as well as advanced statistical training and grantsmanship courses and workshops. This training will be enhanced by the resource-rich environment and explicit support of student research and funding provided by the Pennsylvania State University, as well as the support of Dr. Kenneth Levy and his lab. This promising young researcher will gain training in computational modeling, proficiency in working with “big data,” increased understanding of conceptual and nosological models of BPD, and further skills in disseminating research findings through publication and presentation, as vital steps towards an independent research career in translational clinical science.

拟议的研究旨在阐明边缘人格障碍（BPD）的症状异质性 通过通过高级潜在变量建模检查BPD表型。第二个创新的目标是 通过日常生活中的密集纵向评估来验证这些发现。 BPD与高率有关 急诊室就诊和昂贵的医疗保健服务利用率，影响10-20％的精神病院门诊患者 和20-40％的精神病患者。 BPD还有助于社会和占用功能受损 显着的自杀风险，有十分之一的BPD患者完成自杀。最近的研究旨在 通过识别症状表现的原型模式来提高BPD的治疗效果 可能表明意识形态治疗目标。但是，没有研究包括：a） 足够大的患者样本； b）对结果的生态验证； c）使用适当的统计数据 技术。拟议的项目通过两个目标建立在这项研究的基础上。目标1：使用模型比较 通过半结构化评估的大精神病院门诊样本中识别BPD表型的方法 诊断访谈（研究1）。目标2：通过应用表型分类验证研究1的结果 在研究1中生成的算法至较小的较小的患者样本，这些患者已完成21天 症状和临床结果的调查（研究2）。要解决目标1，要素混合物建模（FMM） - A 新颖，灵活和集成的潜在变量建模方法 - 将与标准因子分析进行比较 和潜在类别分析，以评估BPD的维度和分类结构。我们期望一个 单因素，多级FMM可以最好地解释BPD中的异质性，超过其他来源 异质性（例如性别，合并症）。要解决AIM 2，我们将使用一种原型匹配方法 算法将验证样本中的患者分配给AIM 1中鉴定的表型，并确定其 预测有效性就每日临床结果而言。该项目的结果将迫切地扎根 BPD干预和治疗开发的个性化预测工具，符合NIMH的目标 “开发，测试和完善工具和方法……用于个性化的风险和轨迹预测以及 干预。”该奖学金将使申请人可以从专家那里获得量身定制的咨询 方法论，数据分析以及BPD理论和评估以及高级统计培训以及 授予技巧课程和讲习班。资源丰富的环境和 宾夕法尼亚州立大学提供的学生研究和资金的明确支持以及 Kenneth Levy博士和他的实验室的支持。这承诺年轻的研究人员将获得计算的培训 建模，在使用“大数据”方面的熟练程度，对概念和典型学的了解增加了 BPD的模型，以及通过出版和演示来传播研究结果的进一步技能， 迈向翻译临床科学独立研究职业的重要步骤。