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预后 迟发性运动障碍 (TDD) 是抗精神病药物使用中常见的一种使人衰弱的副作用，其最显着的特征是。 不自主的面部运动，例如做鬼脸、不自主的嘴唇、嘴巴和舌头运动以及眨眼， TDD 很难治疗，并且可能是不可逆转的。 意识到患有 TDD 的患者所经历的损害和残疾是 TDD 的早期发现。 至关重要，以便采取适当的干预措施。 与了解患者的用药依从性有关，最有资格的诊断医生很难做到这一点。 每年 4 到 6 次，投入 20 到 25 分钟的面对面时间，以提供每个 患者 1) “主动监测”，2) 结果讨论，3) 预期药物和说明的变化 当今对每一位心理健康专业人员的迫切要求越来越具有挑战性。 由于大流行，远程医疗和患者人数增加，人力资源减少。 不幸的是，尽管专业人员尽了最大努力，但在这个过程中往往为时已晚，并且非自愿运动 目前，有 200,000 人正在服用价值 6 万美元和 10.5 万美元的抗 TDD 药物。 自动 TDD 检测和准确遵守的方法每年都在快速增长。 将能够及时干预并避免患者耻辱、生活质量下降和昂贵的持续治疗 永久 TDD。 2020 年，抗精神病药物处方超过 5000 万张，据报道 TDD 患病率在 13% 至 24%。随着年龄的增长、超适应症使用和长期接触抗精神病药物，风险会增加。 预防和早期发现是管理 TDD 的关键。然而，目前监测患者的方法。 需要不频繁的亲自就诊或由警惕但训练不足的患者进行自我报告来观察患者 因此，自动化远程依从性监测和治疗存在巨大的市场潜力。 TDD 检测系统。我们的上市策略已在商业化计划中提出。 该第二阶段项目建议利用现有的远程精神病学和视频访谈数据收集技术 在第一阶段，与 3- 由训练有素的临床专业人员组成的小组根据对相同视频材料的功效分析进行评估。 第一阶段数据，我们在此建议扩展对另外 300 个视频记录 AIMS 的收集和分析 对服用抗精神病药物的人进行 5 分钟的视频采访。 患有 TDD 的个体和未诊断出 TDD 的个体将参与研究。 被招募以确保女性和男性的平等分配以及民族和种族的平等 代表性样本。 拟议的数据收集策略将提供最终确定和部署强大的数据收集所需的源材料。 监督机器学习衍生的视频和音频分析工具将用于检测TDD。 使用 80% 收集的视频数据作为训练集创建，并在剩余 20% 保留的数据上进行验证 控制集基于其他监督机器学习训练集的行业经验。 由于要收集的数据量很大，我们设定了识别 TDD 阳性和 TDD 阴性的成功率达到 90% 的目标 控制集中的参与者。 一旦检测工具完成，该项目将通过将对工具的访问合并到现有的 智能手机应用程序 iRxReminder，用于数据收集和监测药物依从性，另一个 iRxReminder 平台将患者直接连接到临床干预所需的关键组件。 研究人员及其电子记录将在实验室进行测试，以确保界面良好。 可以轻松使用。第二阶段项目将使用 iRxReminder 平台来支持自助服务。 慢性病患者服用药物的管理和 TDD 及其他症状监测 随着第一阶段的可行性确定，我们建议进行为期六个月的临床试验，其中 参与者将 1) 受到监控以尽早发现 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) 医疗团队的更积极和更频繁的治疗反应。 将评估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进行的药物更改）将评估算法的成功程度。 与人类评估相比，在 6 个月的监测结束后检测 TDD 将是最终的现场测试 技术。