Elucidating symptoms clusters in multiple sclerosis using patient reported outcomes and unsupervised machine learning

使用患者报告的结果和无监督的机器学习来阐明多发性硬化症的症状群

• 批准号: 10440701
• 负责人: Farren B. S. Briggs
• 金额: $ 15.63万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-24 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10440701
• 关键词: Abnormal coordination; Academy; Address; Affect; American; Bladder; COVID-19 pandemic; Caring; Characteristics; Chronic Disease; Clinic; Clinical; Clinical Management; Clinical Trials; Cognition; Cognitive; Complement; Complex; Consult; Data; Data Analytics; Data Set; Decision Making; Development; Diagnostic; Disease Management; Electronic Health Record; Emotional; Ensure; European; Fatigue; Foundations; Functional disorder; Gait; Goals; Guidelines; Health; Health Status; Healthcare; Impairment; Incentives; International; Intervention; Intestines; Knowledge; Location; Machine Learning; Measures; Mediator of activation protein; Mental Depression; Methods; Modeling; Multiple Sclerosis; Muscle Weakness; Neurologic; Neurology; Optic Nerve; Outcome; Outcome Measure; Pain; Patient Outcomes Assessments; Patients; Pattern; Performance; Persons; Phenotype; Process; Publishing; Radiologic Finding; Registries; Research; Resolution; Resources; Rural Community; Scientist; Self Management; Sensory; Services; Severities; Software Tools; Source; Subgroup; Surveys; Symptoms; Techniques; Telemedicine; Treatment outcome; Tremor; Validation; Vision; Visit; base; clinical care; clinical practice; comorbidity; dashboard; data resource; dexterity; disability; electronic structure; experience; improved; individual patient; insight; interest; machine learning method; member; multidisciplinary; multiple sclerosis treatment; novel; novel strategies; pandemic disease; personalized decision; personalized medicine; predictive modeling; predictive tools; prognostic; response; rural dwellers; simulation; simulation environment; sociodemographics; software development; spasticity; symptom cluster; teleneurology; tool; trait; treatment research; unsupervised learning; young adult

PROJECT SUMMARY Multiple sclerosis (MS) is a chronic disease affecting 900,000 persons in the U.S, and it is a leading cause of disability among young adults. Persons with MS (PwMS) experience wide-ranging symptoms across multiple domains, alone or in combination, with varied severity. Some of these symptoms include optic nerve dysfunction and vision problems, muscle weakness, bladder/bowel dysfunction, tremors, cognitive and emotional problems, and incoordination. The objectives of the current application are to identify and characterize symptom patterns and clusters in PwMS, which are aligned with PA-17-462, that states: “multiple sclerosis (is a) … model condition to advance (symptom) cluster research”. Thus, our analytical framework will inform research in other poly-symptomatic conditions. Stakeholders agree that the benefits of patient reported outcomes (PROs) and measures (PROMs) have not reached their full potential for PwMS. PROs, which provide invaluable insight into the patients’ perspective, are increasingly used in MS clinical trials and clinical practice as standard clinical measures fail to adequately measure impairment across domains or lack sensitivity to detect subtle but meaningful change. Aligning with ongoing global MS initiatives, the current proposal will focus on identifying and characterizing symptom patterns and clusters for PROs; which is also directly aligned with NOT-OD-20-079, a Notice of Special Interest to stimulate “research to improve the interpretation of PROs at the individual patient level for use in the clinical practice”. Furthermore, there is an additional incentive to maximize the use and interpretation of PROs considering the shift to telemedicine service in response to the COVID-19 pandemic. We have assembled a multi-disciplinary team of research scientists and clinical experts with access to two unparalleled data resources (discovery and validation data sets), the 1st being the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry’s survey data for 21,558 PwMS spanning an average of 8.4 years (0.5 to 14 years) and 269,468 biannual surveys, and the 2nd being the structured electronic health records (EHRs) for 8,687 PwMS see at the Mellen Center for MS Treatment and Research (MCMS) at the Cleveland Clinic, spanning an average of 4 years (0.5-8.4 years) and 67,932 visits. In both resources, 11 MS-specific PROMs (MS-PROMs) were longitudinally captured including measures of mobility, dexterity, vision, fatigue, cognition, bladder/bowel, sensory, spasticity, depression, tremor/coordination, and pain. We propose the four complementary aims that will: 1. Characterize overall longitudinal impairment patterns for each 11 MS-PROMs; 2. Identify distinct clusters of Pw MS with similar symptom patterns within and across functional domains using machine learning approaches; 3. Develop new approaches to assess the strength of causal inference and identify sources of model prediction errors in unsupervised machine learning; and 4. Create a dynamic simulation dashboard for predicting MS phenotypes based on the findings of aims 1-3. With these aims, we seek to advance MS phenotyping to facilitate improvements in research, clinical care, and approaches to self-management. By focusing on PROMs, we will leverage the experience of PwMS which is independent of their location (i.e. applications to rural residents) and ideal for telemedicine. We hope that our findings will advance care and empower PwMS to engage in health decisions where personalized phenotypic characterization is necessary.

项目总结 多发性硬化症(MS)是一种慢性疾病，在美国影响着90万人，它是导致 年轻人中的残疾问题。患有多发性硬化症(PWMS)的人在多个领域都会出现广泛的症状， 单独的或组合的，具有不同的严重程度。其中一些症状包括视神经功能障碍和视力问题， 肌肉无力、膀胱/肠道功能障碍、震颤、认知和情绪问题以及协调性失调。这个 当前应用程序的目标是识别和描述PWMS的症状模式和聚集性，这些症状模式和聚集性是 与PA-17-462相一致，声明：“多发性硬化症(是一种)…促进(症状)集群研究的示范条件“。 因此，我们的分析框架将为其他多症状条件下的研究提供参考。 利益相关者一致认为，患者报告的结果(PRO)和措施(PROM)的好处尚未达到 他们对PWMS的全部潜力。PRO，它提供了对患者视角的宝贵见解，越来越多地被使用 在多发性硬化症临床试验和临床实践中，作为标准的临床措施不能充分衡量跨域的损害 或者缺乏敏感度，无法发现细微但有意义的变化。与正在进行的全球移动通信计划保持一致，当前提案 将专注于识别和描述专业人员的症状模式和集群；这也直接与 NOT-OD-20-079，一个特别感兴趣的通知，以刺激“研究，以改善个人对专业人员的解释” 患者水平，用于临床实践“。此外，还有一个额外的动机来最大限度地利用和 为应对新冠肺炎大流行而考虑转向远程医疗服务的专业人士解读。 我们已经组建了一个由研究科学家和临床专家组成的多学科团队，可以访问两个 无与伦比的数据资源(发现和验证数据集)，第一个是北美研究委员会 多发性硬化症(NARCOMS)登记处对21,558个PWMS的调查数据，平均跨度为8.4年(0.5至14年) 和269,468项半年期调查，第二项是针对8,687名PWMS的结构化电子健康记录(EHR)，请参阅 克利夫兰诊所的梅伦多发性硬化症治疗和研究中心(MCMS)，平均跨度为4年(0.5-8.4 年)和67,932次访问。在这两个资源中，纵向捕获了11个MS特定PROM(MS-PROM)，包括 测量活动，灵巧，视力，疲劳，认知，膀胱/肠道，感觉，痉挛，抑郁， 震颤/协调和疼痛。我们提出了四个相辅相成的目标：1.描述总体纵向特征 每11个MS-PROM的损害模式；2.识别具有相似症状模式的PW MS的不同集群 并使用机器学习方法跨功能领域；3.开发新的方法来评估 在无监督机器学习中进行因果推理并识别模型预测误差的来源；以及4.创建动态的 根据AIMS 1-3的研究结果预测MS表型的模拟仪表板。 有了这些目标，我们寻求推进MS表型鉴定，以促进研究、临床护理和 自我管理的方法。通过将重点放在PROM上，我们将利用PWMS的独立经验 它们的地理位置(即适用于农村居民)，是远程医疗的理想选择。我们希望我们的发现将促进医疗保健 并使PWMS能够在需要个性化表型表征的情况下参与卫生决策。