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型人格障碍（BPD）的症状异质性 通过先进的潜变量模型检查 BPD 表型。第二个创新目标是 通过日常生活中的深入纵向评估来验证这些发现。 BPD 与高利率相关 急诊室就诊和昂贵的医疗服务使用，影响了 10-20% 的精神科门诊患者 20-40% 的精神病住院患者。 BPD 还会导致社交和职业功能受损， 显着的自杀风险，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典型模式来提高 BPD 的治疗效果 可能会建议表意处理目标。然而，没有研究同时包括： 足够大的患者样本； b) 对结果进行生态无害验证； c) 使用适当的统计方法 技术。拟议的项目以这项研究为基础，有两个目标。目标 1：利用模型比较 通过半结构化评估在大型精神病门诊样本中识别 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 研究 1 中针对已完成 21 天瞬时治疗的较小样本患者生成的算法 关于症状和临床结果的调查（研究 2）。为了实现目标 1，因子混合模型 (FMM) — 新颖、灵活且综合的潜变量建模方法——将与标准因子分析进行比较 和潜在类别分析，以评估 BPD 的维度和类别结构。我们期望 与其他来源相比，单因素、多类 FMM 最能解释 BPD 的异质性 异质性（例如性别、合并症）。为了实现目标 2，我们将使用原型匹配方法来 通过算法将验证样本中的患者分配给目标 1 中确定的表型，并确定他们的表型 日常临床结果的预测有效性。该项目的结果将提供基于经验的 用于 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……用于个性化风险和轨迹预测以及 干涉。”该奖学金将使申请人能够获得以下领域专家的量身定制咨询： 方法论、数据分析、BPD 理论和评估，以及高级统计培训和 资助课程和讲习班。丰富的资源环境和 宾夕法尼亚州立大学以及宾夕法尼亚州立大学提供的学生研究和资助的明确支持 Kenneth Levy 博士及其实验室的支持。这位有前途的年轻研究人员将接受计算方面的培训 建模、熟练使用“大数据”、增强对概念和分类学的理解 BPD 模型，以及通过出版和演示传播研究成果的进一步技能，如 迈向转化临床科学独立研究生涯的重要一步。