RTSS-Voice: Towards a unified system to classify treatments for muscle tension dysphonia

RTSS-Voice：建立一个统一的系统来对肌张力性发声障碍的治疗进行分类

• 批准号: 10705828
• 负责人: Jarrad Van Stan
• 金额: $ 41.98万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-16 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705828
• 关键词: Address; Adopted; Adoption; Anatomy; Behavior Therapy; Behavioral; Categories; Classification; Clinical; Clinical Data; Communication; Comparative Effectiveness Research; Data; Data Set; Diagnosis; Diagnostic; Discipline; Disease; Dysphonia; Effectiveness; Expert Opinion; Expert Systems; Focus Groups; Goals; Individual; Influentials; Label; Language; Link; Machine Learning; Medical; Methods; Modification; Muscle Tension; Operative Surgical Procedures; Outcome; Patient-Focused Outcomes; Patients; Periodicals; Physiology; Process; Publishing; Rehabilitation therapy; Research; Research Personnel; Shapes; Site; Specific qualifier value; System; Taxonomy; Terminology; Testing; Therapeutic Studies; Time; Translations; Variant; Voice; Voice Disorders; Work; clinical care; common treatment; data mining; evidence base; improved; improved outcome; individualized medicine; innovation; large datasets; novel; programs; response; theories; therapy outcome; voice therapy

Project Summary Systematically improving upon current voice therapy outcomes is problematic as the specific clinician actions (i.e., ingredients) responsible for improved patient functioning (i.e., targets) are unknown. For example, Comparative Effectiveness Research (CER) can show that therapy A works better than therapy B on global outcome X. But why did therapy A provide better outcomes? Why did some patients in therapy B significantly improve with the “worse” program; and some patients in therapy A remain unchanged with the “better” program? A theory-based system is needed to scientifically identify a program’s ingredients associated with improved outcomes across patients. And standard labels are needed to make the identified active ingredients generalizable across therapy programs. Therefore, this project will use a theory-driven framework for describing the ingredients/targets of rehabilitation treatments—called the Rehabilitation Treatment Specification System (RTSS)—and standard voice-specific terminology/definitions—called the RTSS-Voice— to standardly describe (Aim 1) and compare (Aim 2) variations in treatment across 9 well-known and diverse voice therapies. Also, we will create/test an implementation toolkit to facilitate RTSS-Voice adoption in clinical care across 5 Voice Centers (Aim 3). It is hypothesized that the RTSS and RTSS-Voice will characterize all therapies without needing revisions (Aim 1) and identify ingredients/targets that are unique to one therapy and/or common across multiple therapies (Aim2). And since the RTSS-Voice will help clinicians think about their treatment more specifically and in relation to 9 evidence-based therapies, we hypothesize adoption will be associated with improved outcomes at all 5 Voice Centers. The resulting list of mutually exclusive ingredient/targets across therapy programs will obviously improve the state of CER by enabling the identification/comparison of active ingredients across therapy programs; instead of the current practice of studying/comparing entire programs. Clinical adoption will result in large datasets with standard ingredients linked to outcomes, which will facilitate innovative hypotheses and interpretable datamining/machine learning to realistically improve voice therapy effectiveness. This work is likely to generalize to other Centers due to the involvement of >20 influential clinicians and the implementation toolkit.

项目摘要 由于特定的临床动作 （即成分）负责改善患者功能（即目标）的成分是未知的。例如， 比较有效性研究（CER）可以表明，治疗A的作用比全球治疗B更好 结果X。但是为什么治疗A提供更好的结果？为什么在治疗B中有一些患者显着 通过“糟糕”计划改进；一些治疗中的患者A保持不变 程序？需要一个基于理论的系统来科学地识别与程序相关的程序的归纳。 改善了患者的结果。需要标准标签来制作已识别的活性成分 可以在整个治疗计划中推广。因此，该项目将使用理论驱动的框架 描述康复治疗的罪行/目标 - 称呼康复治疗 规范系统（RTSS）以及标准的语音特定术语/定义 - 称为RTSS-VOICE- 标准描述（AIM 1）并比较（AI​​M 2）在9个知名和潜水员中的治疗中的变化（AIM 2） 语音疗法。此外，我们将创建/测试实施工具包，以促进RTSS-VOICE在临床上采用 在5个语音中心进行护理（AIM 3）。假设RTSS和RTSS-Voice将表征所有 疗法无需修订（目标1）并确定一种疗法独有的成分/靶标 和/或多种疗法中常见（AIM2）。而且由于RTSS-Voice将帮助临床医生考虑 他们的治疗更具体地，在9种循证疗法方面，我们假设采用将是 与所有5个语音中心的结果改善相关。由此产生的互斥清单 跨疗法计划的成分/靶标显然可以通过实现CER的状态来改善CER的状态 跨疗法计划的活跃侵袭者的识别/比较；而不是当前的做法 研究/比较整个程序。临床采用将导致具有标准成分的大数据集 与结果相关联，这将促进创新的假设和可解释的数据安装/机器学习 现实地提高语音疗法的有效性。由于 参与> 20位有影响力的临床医生和实施工具包。