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.

项目概要 多发性硬化症 (MS) 是一种慢性疾病，影响着美国 90 万人，也是导致以下疾病的主要原因 年轻人的残疾。多发性硬化症 (PwMS) 患者会经历多个领域的广泛症状， 单独或组合，严重程度各异。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不协调。这 当前应用程序的目标是识别和表征 PwMS 中的症状模式和集群，它们是 与 PA-17-462 一致，其中指出：“多发性硬化症（是）……推进（症状）集群研究的模型条件”。 因此，我们的分析框架将为其他多症状病症的研究提供信息。 利益相关者一致认为，患者报告结果 (PRO) 和措施 (PROM) 的益处尚未达到 充分发挥 PwMS 的潜力。 PRO 可以提供对患者观点的宝贵见解，因此得到越来越多的使用 在 MS 临床试验和临床实践中，因为标准临床测量无法充分衡量跨领域的损害 或者缺乏敏感度来发现微妙但有意义的变化。与正在进行的全球 MS 举措相一致，当前提案 将重点识别和描述 PRO 的症状模式和集群；这也直接与 NOT-OD-20-079，一份特别感兴趣的通知，旨在刺激“改善个人对 PRO 解释的研究” “在临床实践中使用的患者水平”。此外，还有一个额外的激励措施来最大化使用和 PRO 的解释考虑到为应对 COVID-19 大流行而转向远程医疗服务。 我们组建了一支由研究科学家和临床专家组成的多学科团队，拥有两名 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 例 PwMS 的调查数据，平均跨度为 8.4 年（0.5 至 14 年） 以及 269,468 项半年调查，第二项是 8,687 项 PwMS 的结构化电子健康记录 (EHR)，请参见 克利夫兰诊所梅伦多发性硬化症治疗和研究中心 (MCMS)，平均跨度 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 特定的 PROM (MS-PROM)，包括 活动性、灵活性、视力、疲劳、认知、膀胱/肠道、感觉、痉挛、抑郁的测量， 震颤/协调性和疼痛。我们提出了四个互补的目标，这些目标将： 1. 描述总体纵向特征 每 11 个 MS-PROM 的损伤模式； 2. 识别具有相似症状模式的不同 Pw MS 簇 以及使用机器学习方法跨功能领域； 3. 开发评估实力的新方法 因果推理并识别无监督机器学习中模型预测错误的来源； 4. 创建动态 用于根据目标 1-3 的结果预测 MS 表型的模拟仪表板。 出于这些目标，我们寻求推进 MS 表型分析，以促进研究、临床护理和治疗方面的改进。 自我管理的方法。通过专注于 PROM，我们将利用独立的 PwMS 的经验 其位置（即对农村居民的应用）并且非常适合远程医疗。我们希望我们的发现能够促进护理 并授权 PwMS 参与需要个性化表型表征的健康决策。