Detecting and Monitoring Tardive Dyskinesia to Improve Patient Outcomes

检测和监测迟发性运动障碍以改善患者预后

• 批准号: 9410244
• 负责人: Anthony Alexander Sterns
• 金额: $ 26.09万
• 依托单位: CREATIVE ACTION, 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 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9410244
• 关键词: Acute; Adherence; Adverse effects; Affect; Algorithms; Antipsychotic Agents; Apple; Awareness; Behavioral Sciences; Blinking; Brain; Cellular Phone; Characteristics; Chinese People; Chronic; Clinical; Cognitive Science; Collection; Communication; Computer software; Data; Data Analyses; Detection; Development; Diagnosis; Diagnostic; Distress; Drug usage; Early Diagnosis; Elderly; Elements; Ensure; Exposure to; Eye; FDA approved; Face; Family; Female; Frequencies; Funding; Future; Generations; Goals; Health Insurance Portability and Accountability Act; Health Personnel; Health Professional; Human; Human Resources; Impaired cognition; Impairment; Incidence; Individual; Industry; Institutes; International; Intervention; Interview; Involuntary Movements; Label; Laboratories; Learning; Limb structure; Link; Lip structure; Machine Learning; Malaysian; Medical Care Team; Mental Health; Meta-Analysis; Methods; Metoclopramide; Minority; Monitor; Monitoring Clinical Trials; Movement; Mydriasis; Neurologic; Oral cavity; Participant; Patient Care; Patient Monitoring; Patient Self-Report; Patient observation; Patient-Focused Outcomes; Patients; Pattern; Persons; Pharmaceutical Preparations; Pharmacotherapy; Phase; Prevalence; Prevention; Process; Psychiatrist; Quality of life; Records; Recruitment Activity; Reporter; Reporting; Research; Research Personnel; Risk; Risperidone; Sampling; Secure; Self Management; Self-Administered; Singapore; Small Business Innovation Research Grant; Software Tools; Source; Speech; Supervision; Symptoms; Syndrome; System; Systems Analysis; Tardive Dyskinesia; Technology; Testing; Tongue; Training; Tremor; United States National Institutes of Health; Visit; Voice; Woman; aging population; base; chronic care model; cloud based; collaborative care; compliance behavior; disability; experience; field study; improved; interest; mHealth; male; medication compliance; men; new technology; olanzapine; racial and ethnic; severe mental illness; success; tool; treatment response

Tardive dyskinesia (TD) 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, TD 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 TD. Early detection of TD is critical so that appropriate interventions can be instituted. Unfortunately, despite professionals’ best efforts, it is often too late in the process and the involuntary movements are permanent. Antipsychotic prescriptions exceeded 50 million in 2011 and the reported incidence of TD 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 TD. However, current methods for monitoring patients require observation of patients at infrequent in-person visits or self-reporting by vigilant patients and their families. Therefore strong market potential exists for an automated detection system. This Phase I project proposes to leverage existing telepsychiatry and video interview data gathering technologies available commercially to efficiently collect and analyze two hundred 5-minute video interviews with individuals taking anti-psychotic medications. Half of the interviews will be with individuals living with diagnosed TD and the other without a diagnosis of TD. 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 create a powerful supervised machine learning derived video and audio analysis tool to detect TD. 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 TD positive and TD 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 clinical trials. The iRxReminder system 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. In Phase II the iRxReminder system will be validated for use in supporting the self-management and symptom monitoring of medication taking by individuals living with chronic mental illnesses. Once feasibility is established, we propose a year-long RCT where participants will be monitored for early detection of TD along with goals for high adherence, improved control of symptoms and side effects, and more aggressive and frequent treatment responses by the healthcare team.

迟发性运动障碍（TD）是抗精神病药物的常见衰弱副作用。表征最多 值得注意的是，通过非自愿的面部运动，例如痛苦，非自愿嘴唇，嘴和舌头 动作和眼睛眨眼，TD难以治疗且可能不可逆。精神科医生和其他人 精神卫生专业人员敏锐地意识到患者所经历的障碍和残疾 谁开发TD。 TD的早期检测至关重要，因此可以进行适当的干预措施。 不幸的是，任务专业人士的最大努力通常在此过程中为时已晚，而不是自愿的 运动是永久的。 2011年，抗精神病药处方超过5000万，报告的TD事件介于 13％和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。 因此，预防和早期检测是管理TD的关键。但是，当前的方法 监测患者需要在不经常进行的访问或自我报告时观察患者 警惕的患者及其家人。因此，自动检测存在强大的市场潜力 系统。 该阶段我项目提出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 商业上可用的技术有效地收集和分析了两百个5分钟的视频 接受服用抗精神药物的人的访谈。一半的采访将与 患有诊断性TD的人，另一个没有TD诊断的人。参与者 将招募研究以确保女性和男性以及种族和 种族代表性样本。 拟议的数据收集策略将为创建强大的资料提供必要的材料 监督机器学习派生的视频和音频分析工具可检测TD。检测工具将 使用80％的收集视频数据作为培训集创建，并在其余20％上进行验证 保留为控制集。基于其他监督机器学习的行业经验 培训集和要收集的数据量，我们设定了确定成功率90％成功率的目标 对照组中的TD阳性和TD负参与者。 一旦检测工具完成，该项目将通过将对工具的访问转换为一个 现有的智能手机应用程序Irxreminder，用于数据收集和监测临床试验。 Irxreminder系统将患者直接与研究人员及其电子记录联系起来。这 修改后的应用程序将在实验室中进行测试，以确保可以轻松使用接口。在第二阶段 Irxreminder系统将被验证以用于支持自我管理和症状 监测患有慢性精神疾病的人服用药物。一旦可行性 成立后，我们提出了为期一年的RCT，将监控参与者以及早检测到TD 加上高度依从性的目标，改善症状和副作用的控制以及更多 医疗团队积极进取的治疗反应。