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

使用自动 EHR 衍生的 AI 算法开发一个程序来评估和治疗青光眼患者的痛苦

• 批准号: 10282287
• 负责人: Samuel Isaac Berchuck
• 金额: $ 11.53万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10282287
• 关键词: Address; Algorithms; Anxiety; Artificial Intelligence; Automation; Award; Behavior Therapy; Behavioral Sciences; Bioinformatics; Biometry; Blindness; Calibration; Caring; Characteristics; Chronic; Clinic; Clinical; Clinical Data; Clinical Research; Comprehension; Coping Skills; Data; Data Collection; Data Science; Data Set; Database Management Systems; Databases; Development; Discrimination; Disease; Distress; Effectiveness; Electronic Health Record; Ensure; Familiarity; Focus Groups; Glaucoma; Goals; Gold; Health; Health Care Research; Health Professional; Health Sciences; Healthcare; Image; Intervention; Laboratories; Lead; Left; Light; Measures; Medical; Medical Research; Mental Depression; Mentors; Nature; Oncology; Ophthalmologist; Outcome; Outcome Measure; Patient Care; Patient Outcomes Assessments; Patient Self-Report; Patients; Performance; Personal Satisfaction; Phase; Population; Property; Protocols documentation; Provider; Psychiatry; Quality of life; Questionnaires; Randomized; Randomized Clinical Trials; Recommendation; Records; Registries; Research; Research Design; Research Personnel; Risk Estimate; Risk Factors; Severity of illness; Stress; Supervision; Surveys; Techniques; Telephone; Testing; Time; Training; Validation; Vision; Visit; Visual Fields; Work; algorithm training; base; career development; clinical care; clinical decision-making; clinical practice; clinical risk; comorbidity; compliance behavior; coping; cost; design; diagnosis standard; evidence base; experience; eye center; follow-up; high risk; improved; innovation; instrument; intelligent algorithm; intervention program; medication compliance; mindfulness-based stress reduction; model development; multidisciplinary; novel strategies; outcome prediction; patient screening; population health; predictive modeling; prevent; programs; prospective; psychosocial; retention rate; screening; screening program; skills; standard measure; statistical and machine learning

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.

项目摘要/摘要 青光眼是一种导致不可逆转的失明的疾病，由于其慢性，进步性，不可能 对患者的社会心理燃烧。适当地，眼科医生的重点是控制疾病 防止视力丧失。然而，病人在治疗期间和之后的心理困扰尚未经常是常规的 解决，是护理的另一个重要目标。心理痛苦（即焦虑，抑郁）负面 影响青光眼的所有结果，并且与随访和药物依从性差有关，更糟 与视觉相关的生活质量和疾病严重程度，以及视野进展的速度更快。直接的 对社会心理困扰的评估和治疗可能会改善青光眼结果。而罕见 在青光眼诊所中，心理社会苦恼筛查在其他方面一直保持一致性 十多年的医疗环境（例如肿瘤学），导致了干预和 改善社会心理困扰以及随后的整体健康状况。我们的总体科学前提是 青光眼诊所的心理社会困扰（即焦虑，抑郁）的筛查计划将 增强患者对医疗建议和生活质量的遵守，最终导致 与视觉相关结果的改善（例如，视野进程）。患者报告的结果指标 是黄金标准的苦难度量，但是在青光眼患者应有的情况下常规收集 为了解决这个问题，PI提出一个自动筛选框架， 由初步分析的动机，证明可以使用预测性可靠地确定困扰 基于电子健康记录（EHR）数据的青光眼临床风险因素的建模。这种预测 模型将使用现有的EHR数据库，杜克青光眼注册表中的AIM 1开发，并将产生 可以用来为临床决策告知临床决策的遇险的自动风险估计 管理遇险调查；因此，将遇险评估限制为一部分高危患者。 次要目标将集中于自动化技术的外部验证，并衡量可接受性 青光眼诊所（AIM 2）的遇险筛查以及行为干预以改进的改进 青光眼患者的痛苦技巧（AIM 3）。这项研究将对患者造成积极影响 在青光眼中，是对遇险筛查计划的基于证据的评估。提案 还详细介绍了一项培训计划，以帮助PI从博士后科学过渡到独立研究人员。 该奖项的修订阶段将由主要导师Felipe Medeiros博士和 多学科指导团队包括塔玛拉·萨默斯（Tamara Somers）博士（精神病学和行为科学），博士 David Page（生物统计学和生物信息学）和Kevin Weinfurt博士（人口健康科学）。表演 拟议的研究，正式课程和修改职业发展将为PI提供高度的 寻求帮助的技能和经验，以帮助确保成功过渡到独立性。