Refining and Validating Borderline Personality Disorder Phenotypes Through Factor Mixture Modeling

这项拟议的研究试图澄清边缘型人格障碍(Bpd)的症状异质性。 通过先进的潜变量模型检验BPD的表型。第二个创新目标是 通过日常生活中密集的纵向评估来验证这些发现。石油日产量与高利率相关 急诊室就诊和昂贵的医疗服务利用，影响10%-20%的精神科门诊患者 和20%-40%的精神科住院患者。BPD还会损害社会和职业功能，并 严重的自杀风险，每10个患有BPD的人中就有1人自杀。最近的研究旨在 通过确定以下症状表现的典型模式来提高BPD的治疗效果 可能会建议表意的治疗目标。然而，没有一项研究同时包括：a) 足够大的患者样本；b)对结果进行合理的生态验证；c)使用适当的统计方法 技巧。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：利用模型比较 通过半结构评估在精神科门诊大样本中鉴定BPD表型的方法 诊断性访谈(研究1)。目的2：通过应用表型分类来验证研究1的结果 研究1中产生的算法适用于完成21天瞬时治疗的较小样本患者 关于症状和临床结果的调查(研究2)。为实现目标1，因子混合建模(FMM)-a 新的、灵活的、综合的潜变量建模方法-将与标准因素分析进行比较 和潜在类别分析，以评估BPD的维度结构和范畴结构。我们预计会有一个 单因素、多类别FMM将最好地解释BPD中的异质性，而不是其他来源 异质性(例如，性别、共病)。为了解决目标2，我们将使用原型匹配方法来 算法将验证样本中的患者分配到目标1中确定的表型，并确定他们的 在每日临床结果方面的预测有效性。该项目的结果将提供有经验依据的 用于BPD干预和治疗开发的个性化预测工具，与NIMH的目标一致 “开发、测试和改进工具和方法…用于个性化风险和轨迹预测以及 干预。“这项奖学金将允许申请者接受专家的量身定做的咨询 方法、数据分析、业务发展理论和评估，以及高级统计培训和 培训课程和研习班。这一培训将因资源丰富的环境和 明确支持宾夕法尼亚州立大学提供的学生研究和资金，以及 肯尼斯·利维博士和他的实验室的支持。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”，增加了对概念和病因学的理解 BPD的模式，以及通过出版和演示传播研究成果的进一步技能，如 在转化型临床科学领域迈向独立研究生涯的重要步骤。

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.