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: 10469533
• 负责人: Samuel Isaac Berchuck
• 金额: $ 11.47万
• 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/10469533
• 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 and Coping; Supervision; Surveys; Techniques; Telephone; Testing; Time; Training; Validation; Vision; Visit; Visual Fields; Work; algorithm training; artificial intelligence algorithm; base; career development; clinical care; clinical decision-making; clinical practice; clinical risk; comorbidity; compliance behavior; cost; design; diagnosis standard; evidence base; experience; eye center; follow-up; high risk; improved; innovation; instrument; 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.

项目总结/摘要 青光眼是一种导致不可逆失明的疾病，并且由于其慢性、进行性的性质， 给病人带来心理负担适当地，眼科医生的重点是控制疾病， 防止视力丧失。然而，患者在治疗期间和治疗后的心理社会困扰并不常见。 这是另一个重要的护理目标。心理社会困扰（即，焦虑、抑郁） 影响青光眼的所有结局，并与不良随访和药物依从性相关， 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 评估和治疗心理困扰可能会改善青光眼的预后。虽然不常见 在青光眼诊所，心理社会困扰筛查已经发生，在其他一些一致性， 医疗环境（例如，肿瘤学）十多年，导致转诊进行干预， 改善心理社会困扰，进而改善整体健康状况。我们首要的科学前提是 一个针对心理社会困扰的筛查项目（即，焦虑，抑郁）在青光眼诊所将 提高患者对医疗建议的依从性和生活质量，最终导致 视力相关结果的改善（例如，视野进展）。患者报告的结局指标 是衡量痛苦的金标准，然而，由于青光眼患者的症状， 时间和成本负担。为了解决这个问题，PI提出了一个自动化的预筛选框架， 初步分析表明，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。该预测 将在目标1中使用现有的EHR数据库（杜克青光眼登记处）开发一个模型，并将产生 自动化的痛苦风险估计，可用于通知临床决策，关于 管理的痛苦调查;因此，限制痛苦评估的一个子集的高风险患者。 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， 青光眼诊所的痛苦筛查（aim 2），以及行为干预的改进， 青光眼患者痛苦的应对技巧（目的3）。这项研究将对患者产生积极影响- 作为一个以证据为基础的评估，一个痛苦的筛选程序。该提案 还详细介绍了培训计划，以帮助PI从博士后学者过渡到独立研究员。 该奖项的指导阶段将由主要导师Felipe Medeiros博士监督， 多学科的指导团队，包括塔玛拉索默斯博士（精神病学和行为科学），博士。 大卫页（生物统计学和生物信息学），和凯文Weinwein博士（人口健康科学）。执行 拟议的研究，正式的课程，和指导的职业发展将提供PI高度 他们需要技能和经验，以帮助确保成功过渡到独立。