Remote Monitoring and Detecting of Tardive Dyskinesia for Improving Patient Outcomes

远程监测和检测迟发性运动障碍以改善患者预后

• 批准号: 10603982
• 负责人: Anthony Sterns
• 金额: $ 87.12万
• 依托单位: IRX REMINDER, LLC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-05 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10603982
• 关键词: Acceleration; Acute; Adherence; Affect; Algorithms; Antiemetics; Antipsychotic Agents; Awareness; Behavioral Sciences; Blinking; Brain; Caring; Cellular Phone; Certification; Characteristics; Chinese; Chronic; Clinical; Cognitive Science; Collection; Computer software; Creativeness; Data; Data Analyses; Data Collection; Detection; Development; Diagnosis; Diagnostic; Discrimination; Disease remission; Distress; Dopamine Receptor; Early Diagnosis; Elderly; Electronics; Elements; Ensure; Ethnic Origin; Exposure to; Eye; FDA approved; Face; Family; Female; Frequencies; Funding; Generations; Goals; Health; Health Insurance Portability and Accountability Act; Health Personnel; Health Professional; Human; Human Resources; Impaired cognition; Impairment; Incidence; Individual; Industry; Instruction; Intervention; Interview; Involuntary Movements; Laboratories; Limb structure; Link; Lip structure; Machine Learning; Malaysian; Marketing; Masks; Medical Care Team; Mental Health; Meta-Analysis; Methods; Metoclopramide; Minority; Monitor; Movement; Mydriasis; Neurologic; Oral cavity; Participant; Patient Care; Patient Monitoring; Patient Self-Report; Patient observation; Patient-Focused Outcomes; Patients; Pattern; Persons; Pharmaceutical Preparations; Phase; Prevalence; Prevention; Process; Psychiatric therapeutic procedure; Psychiatrist; Qualifying; Quality of life; Race; Records; Reporting; Research; Research Personnel; Risk; Risperidone; Sampling; Secure; Self Administration; Self Management; Singapore; Software Tools; Source; Speech; Symptoms; Syndrome; System; Systems Analysis; Tablets; Tardive Dyskinesia; Technology; Technology Assessment; Telemedicine; Testing; Time; Tongue; Training; Tremor; United States National Institutes of Health; Video Recording; Visit; Voice; Woman; Work; Writing; aging population; chronic care model; clinical trial participant; collaborative care; commercialization; compliance behavior; cost; detection platform; disability; experience; feasibility testing; field study; improved; interest; mHealth; male; medication compliance; men; monitoring device; new technology; off-label use; olanzapine; pandemic disease; patient population; power analysis; recruit; remote monitoring; severe mental illness; side effect; smartphone application; social stigma; software development; success; supervised learning; telepsychiatry; tool; treatment response; trend

Abstract - Remote Monitoring and Detecting of Tardive Dyskinesia for Improving Patient Outcomes Tardive dyskinesia (TDD) is a common debilitating side effect of antipsychotic use. Characterized most notably by involuntary facial movements such as grimacing, involuntary lip, mouth, and tongue movements, and eye blinking, TDD is difficult to treat and potentially irreversible. Psychiatrists and other mental health professionals are acutely aware of the impairment and disability experienced by patients who develop TDD. Early detection of TDD is critical so that appropriate interventions can be instituted. What interventions are implemented is intimately tied to knowing the patient’s medication adherence. It is difficult for the most qualified diagnosticians to devote the 20-25 minutes of in-person time at the 4 to 6 times per year frequency necessary to provide every patient the 1) “active monitoring,” 2) discussion of results, 3) changes to medication and instructions expected with the urgent demands on every mental health professional today. This is increasingly challenging with the increase in telemedicine and patient populations and decreasing human resources due to the pandemic. Unfortunately, despite professionals’ best efforts, it is often too late in the process and the involuntary movements are permanent. Currently, there are 200,000 individuals taking anti-TDD medications costing $60K and $105K annually and this is increasing rapidly each year. A method for automatic TDD detection and accurate adherence would enable timely intervention and avoid patient stigma, lower quality of life, and expensive ongoing treatment for permanent TDD. Antipsychotic prescriptions exceeded 50 million in 2020 and the reported prevalence of TDD is between 13% and 24%. Risk grows with advancing age, off-label uses, and chronic exposure to antipsychotics. Therefore, prevention and early detection are key to managing TDD. However, current methods for monitoring patients require observation of patients at infrequent in-person visits or self-reporting by vigilant but undertrained patients and their families. Therefore, strong market potential exists for an automated remote adherence monitoring and TDD detection system. Our go-to-market strategy is presented in the commercialization plan. This Phase II project proposes to leverage existing telepsychiatry and video interview data gathering technologies that in Phase I demonstrated up to 77% discrimination in categorizing individuals with TDD compared to a 3- person panel of trained clinical professionals evaluating the same video materials. Based on a power analysis of the Phase I data, we propose here to extend collection and analysis of an additional 300 video recorded AIMS and 5-minute video interviews with individuals taking anti-psychotic medications. Half of the interviews will be with individuals living with diagnosed TDD and the other without a diagnosis of TDD. The participants in the study will be recruited to ensure an equal distribution of females and males as well as an ethnically and racially representative sample. The proposed data gathering strategy will provide the source material necessary to finalize and deploy a powerful supervised machine learning derived video and audio analysis tool to detect TDD. The detection tool will be created using 80% of the collected video data as a training set and validated on the remaining 20% reserved as the control set. Based on industry experience with other supervised machine learning training sets and the amount of data to be collected, we set a goal of a 90% success rate in identifying TDD positive and TDD negative participants in the control set. Once the detection tool is complete the project will conclude by incorporating access to the tool into an existing smartphone app, iRxReminder, that is used for data gathering and monitoring of medication adherence, the other critical component required for clinical intervention. The iRxReminder platform links patients directly to researchers and their electronic records. The modified app will be tested in the laboratory to ensure the interface can be easily used. This Phase II project will then use the iRxReminder platform for use in supporting the self- management and TDD and other symptoms monitoring of medication taking by individuals living with chronic mental illnesses. With feasibility established in Phase I, we propose a six-month long clinical trial where participants will 1) be monitored for early detection of TDD (and confirmation of not having TDD, thus avoiding unnecessary diagnostician time) along with 2) goals for high adherence, 3) improved control of symptoms and side effects, and 4) more aggressive and frequent treatment responses by the healthcare team. Statistical tests of the ease-of-use by patients and the care team will be conducted. The impact on revenue, treatment trajectory (number of side effects detected and medication changes made) will be assessed. The success of the algorithm to detect TDD compared to a human assessment at the end of 6-months of monitoring will be a final field test of the technology.

摘要 - 远程监测和检测迟发性运动障碍，以改善患者的预后 迟发性运动障碍（TDD）是抗精神病药物的常见衰弱副作用。最著名的是 非自愿的面部运动，例如痛苦，非自愿嘴唇，嘴和舌头运动，眼睛闪烁， TDD很难治疗，并且可能不可逆。精神科医生和其他精神卫生专业人员非常敏锐 意识到发展中TDD的患者所经历的障碍和残疾。 TDD的早期发现是 至关重要，以便可以进行适当的干预措施。实施哪些干预措施是密切的 与知道患者的药物依从性有关。最合格的诊断师很难很难 将20-25分钟的面对面时间献给每年4至6次提供的频率 患者1）“主动监测”，2）讨论结果，3）对药物的更改和预期的说明 今天对每位心理健康专业人员的紧急需求。这对 远程医疗和患者人群的增加，并因大流行而减少人力资源。 不幸的是，任务专业人士的最大努力通常在此过程和非自愿运动中为时已晚 是永久的。目前，有200,000个人服用抗TDD药物的费用为$ 60,000，$ 105K 一个，每年都在迅速增加。一种自动TDD检测和准确粘附的方法 将及时干预并避免患者污名，较低的生活质量以及昂贵的持续治疗 对于永久性TDD。 抗精神病药处方在2020年超过5000万，报告的TDD患病率在13％至 24％。风险随着年龄的增长，标签外用途和抗精神病药的长期暴露而增长。所以， 预防和早期检测是管理TDD的关键。但是，目前监测患者的方法 需要观察到很少见的访问或受到警惕但训练不足的患者自我报告的患者 和他们的家人。因此，具有强大的市场潜力，用于自动远程依从性监视和 TDD检测系统。我们的进入市场策略在商业化计划中提出。 该第二阶段项目的建议旨在利用现有的远程精神病学和视频访谈数据收集技术 在第I期中，在对TDD的个体进行分类中，与3--相比，最多有77％的歧视 训练有素的临床专业人员小组评估相同的视频材料。基于对 第一阶段数据，我们在这里建议扩展收集和分析300个录制的视频目的 以及对服用抗精神药物的人进行5分钟的视频访谈。一半的采访将与 患有诊断性TDD的人，另一个没有TDD诊断的人。研究的参与者将 招募以确保女性和男性以及种族和种族的平等分配 代表性样本。 拟议的数据收集策略将为最终确定和部署强大的材料提供必要的材料 监督机器学习派生的视频和音频分析工具可检测TDD。检测工具将是 使用80％的收集视频数据作为培训集创建，并在其余20％的保留下验证 控制集。根据其他监督机器学习培训集的行业经验， 要收集的数据数量，我们设定了确定TDD正和TDD负面的成功率90％成功率的目标 控制集的参与者。 一旦检测工具完成，该项目将通过将对工具的访问编码为现有 智能手机应用程序，irxreminder，用于数据收集和监测药物依从性，另一种用于监测 临床干预所需的关键成分。 Irxreminder平台将患者直接链接到 研究人员及其电子记录。修改后的应用程序将在实验室中进行测试，以确保接口 可以轻松使用。然后，此II阶段项目将使用Irxreminder平台用于支持自我 管理和TDD以及其他慢性个人服用药物服用药物的症状 精神疾病。在第一阶段建立的可行性时，我们提出了一项六个月的临床试验，其中 参与者将受到监视以提早检测到TDD（并确认没有TDD，从而避免 不必要的诊断时间）以及2）高依从性的目标，3）改善症状的控制和 副作用，以及4）医疗团队更具侵略性和经常的治疗反应。统计测试 将进行患者和护理团队的易用性。对收入，治疗轨迹的影响 （检测到的副作用数量和更改用药）将被评估。算法的成功 与在6个月结束时进行人体评估相比，检测TDD将是对 技术。