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，以改善患者的治疗效果 迟发性运动障碍（TDD）是抗精神病药物使用的常见衰弱副作用。最显著的特点是 无意识的面部运动，例如鬼脸，无意识的嘴唇、嘴和舌头运动，以及眨眼， TDD很难治疗，并且可能不可逆。精神科医生和其他心理健康专业人员 了解患有TDD的患者所经历的损伤和残疾。TDD的早期检测是 这是至关重要的，以便采取适当的干预措施。实施的干预措施与 与了解病人的药物依从性有关。对于最有资格的诊断医生来说， 以每年4至6次的频率，投入20至25分钟的面对面时间， 患者1）“主动监测”，2）结果讨论，3）预期药物和说明的变更 对每一位心理健康专业人员的迫切要求。这是越来越具有挑战性的， 远程医疗和患者人数增加，而由于这一流行病，人力资源减少。 不幸的是，尽管专业人士尽了最大努力，但在这个过程中往往为时已晚， 是永久性的目前，有20万人服用抗TDD药物，费用为6万美元和10.5万美元 而且每年都在快速增长。用于自动TDD检测和精确粘附的方法 能够及时干预，避免患者耻辱感，降低生活质量和昂贵的持续治疗 永久TDD 2020年，抗精神病药物处方超过5000万张，报告的TDD患病率在13%和10%之间。 百分之二十四风险随着年龄的增长，标签外使用和长期暴露于抗精神病药物而增加。因此，我们认为， 预防和早期检测是管理TDD的关键。然而，目前用于监测患者的方法 需要在不频繁的亲自访视中观察患者或由警惕但培训不足的患者自我报告 和他们的家人。因此，对于自动化远程遵守监测存在强大的市场潜力， TDD检测系统。我们的市场战略在商业化计划中提出。 这个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，在对TDD患者进行分类时， 由经过培训的临床专业人员组成的专家组对相同的视频材料进行评估。基于对 第一阶段的数据，我们建议在这里扩大收集和分析额外的300个视频记录的AIMS 以及与服用抗精神病药物的个人进行5分钟的视频访谈。一半的面试将与 患有诊断的TDD的个体和另一个没有诊断TDD的个体。研究的参与者将 招聘时应确保男女平等分配， 代表性样品。 拟议的数据收集战略将提供必要的源材料，以最终确定和部署一个强大的 有监督的机器学习衍生的视频和音频分析工具来检测TDD。检测工具将是 使用收集的80%的视频数据作为训练集创建，并在保留的剩余20%上进行验证， 控制设置。基于其他监督式机器学习训练集的行业经验， 由于需要收集大量的数据，我们设定了一个目标，即在识别TDD阳性和TDD阴性方面的成功率为90 控制组的参与者。 一旦检测工具完成，该项目将通过将工具的访问纳入现有的 智能手机应用程序iRxReminder，用于数据收集和药物依从性监测，另一个 临床干预所需的关键组件。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进行测试，以确保界面 可以很容易地使用。该第二阶段项目将使用iRxReminder平台，用于支持自 管理和TDD和其他症状监测的个人服用药物的慢性 精神疾病在第一阶段确定了可行性后，我们建议进行为期六个月的临床试验， 参与者将1）被监测以早期检测TDD（并确认没有TDD，从而避免 不必要的诊断时间），沿着2）高依从性的目标，3）改善症状控制， 副作用，以及4）医疗团队更积极和频繁的治疗反应。统计检验 将对患者和护理团队进行易用性评估。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变更）。算法的成功 在6个月的监测结束时，与人类评估相比，检测TDD将是最终的现场测试， 技术。