Development of a program to assess and treat distress in glaucoma patients using an automated EHR-derived AI algorithm

项目摘要/摘要 青光眼是一种导致不可逆性失明的疾病，由于其慢性、进行性， 患者的心理社会负担。适当地，眼科医生的重点是控制疾病以 防止视力丧失。然而，患者在治疗期间和治疗后的心理社会痛苦并不是常规的 这一问题得到了解决，也是护理的另一个重要目标。心理社会苦恼(即焦虑、抑郁) 影响青光眼的所有结局，并与随访和服药依从性差有关，更糟 与视力相关的生活质量和疾病严重程度，以及更快的视野进展速度。直接 心理社会困扰的评估和治疗可能会改善青光眼的预后。虽然不常见 在青光眼诊所，心理社会窘迫筛查在其他方面也有一定的一致性。 十多年的医疗环境(例如肿瘤学)，导致转诊进行干预和 改善心理社会痛苦，进而改善整体健康状况。我们最重要的科学前提是 在青光眼诊所进行心理社会困扰(即焦虑、抑郁)的筛查计划将 提高患者对医疗建议的遵从性和生活质量，最终导致 改善与视力相关的结果(例如，视野进展)。患者报告的结果衡量标准 然而，痛苦的黄金标准是不是在青光眼患者中常规收集 感知到的时间和成本负担。为了纠正这一点，PI提出了一个自动预筛选框架， 受初步分析的激励，这些分析表明，可以使用预测性方法可靠地识别痛苦 基于来自电子健康记录(EHR)数据的青光眼临床风险因素的建模。这一预测 模型将在AIM 1中使用现有的EHR数据库Duke青光眼注册表开发，并将产生 可用于为临床决策提供信息的自动窘迫风险估计，涉及 管理痛苦调查；因此，将痛苦评估限制在高危患者的子集。 次要目标将重点放在自动化技术的外部验证上，并衡量对 青光眼诊所的窘迫筛查(目标2)，以及改进行为干预的改进 青光眼患者应对痛苦的技巧(目标3)。这项研究将对患者产生积极的影响- 处于青光眼中，作为对求救筛查计划的循证评估。这项建议 还详细介绍了帮助PI从博士后学者过渡到独立研究人员的培训计划。 该奖项的指导阶段将由主要导师Felipe Medeiros博士监督，以及 多学科指导团队包括塔玛拉·萨默斯博士(精神病学和行为科学)、 大卫·佩奇(生物统计学和生物信息学)和凯文·温富特博士(人口健康科学)。表演 建议的研究、正式的课程工作和指导的职业发展将为PI提供高度的 帮助确保成功过渡到独立的抢手技能和经验。

PROJECT SUMMARY/ABSTRACT Glaucoma is a disease that results in irreversible blindness and due to its chronic, progressive nature, imposes a psychosocial burden on patients. Appropriately, the focus of ophthalmologists is on controlling the disease to prevent vision loss. Yet, patient’s psychosocial distress during and after therapy has not been routinely addressed and is another important target of care. Psychosocial distress (i.e., anxiety, depression) negatively impacts all outcomes in glaucoma and is associated with poor follow-up and medication adherence, worse vision-related quality-of-life and disease severity, and faster rates of visual field progression. Direct assessment and treatment of psychosocial distress is likely to improve glaucoma outcomes. While uncommon in glaucoma clinics, psychosocial distress screening has been occurring with some consistency in other medical settings (e.g., oncology) for more than a decade, leading to referrals for intervention and improvements in psychosocial distress and subsequently overall health. Our overarching scientific premise is that a screening program for psychosocial distress (i.e., anxiety, depression) in glaucoma clinics would enhance the patient’s adherence to medical recommendations, and quality-of-life, ultimately leading to improvements in vision-related outcomes (e.g., visual field progression). Patient-reported outcome measures are the gold standard measures of distress, however are not routinely collected in patients with glaucoma due to perceived time and cost burdens. To remedy this, the PI proposes an automated pre-screening framework, motivated by preliminary analyses that demonstrate that distress can be reliably identified using predictive modeling based on glaucoma clinical risk factors from electronic health records (EHR) data. This predictive model will be developed in aim 1 using an existing EHR database, the Duke Glaucoma Registry, and will yield automated risk estimates of distress that can be used to inform clinical decision making, regarding the administration of a distress survey; therefore, limiting distress assessment to a subset of high-risk patients. Secondary aims will focus on external validation of the automated technique, and gauging acceptability to distress screening in a glaucoma clinic (aim 2), and the refinement of a behavioral intervention to improve coping skills for distress in patients with glaucoma (aim 3). This research will positively impact patient well- being in glaucoma, serving as an evidence-based assessment of a distress screening program. The proposal also details a training plan to help the PI transition from a postdoctoral scholar to an independent researcher. The mentored phase of the award will be supervised by the primary mentor, Dr. Felipe Medeiros, and multidisciplinary mentoring team including Dr. Tamara Somers (Psychiatry and Behavioral Sciences), Dr. David Page (Biostatistics & Bioinformatics), and Dr. Kevin Weinfurt (Population Health Sciences). Performing the proposed research, formal coursework, and mentored career development will provide the PI with highly sought-after skills and experiences to help ensure a successful transition to independence.