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

项目摘要 多发性硬化症（MS）是一种慢性疾病，在美国影响90万人，并且它是多发性硬化症的主要原因。 年轻人的残疾。MS（PwMS）患者在多个领域经历广泛的症状， 单独或组合，具有不同的严重性。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/肠功能障碍、震颤、认知和情绪问题以及不协调。的 本申请的目的是识别和表征PwMS中的症状模式和集群， 与PA-17-462一致，其指出：“多发性硬化（是）. 因此，我们的分析框架将为其他多症状条件的研究提供信息。 利益相关者一致认为，患者报告结局（PRO）和措施（PROM）的受益尚未达到 PwMS的全部潜力。PRO提供了对患者观点的宝贵见解， 在MS临床试验和临床实践中，作为标准临床指标，无法充分测量跨领域的损伤 或缺乏敏感性，无法察觉细微但有意义的变化。根据正在进行的全球会员国举措， 将侧重于识别和表征PRO的症状模式和集群;这也直接与 NOT-OD-20-079，一份特别关注的通知，旨在促进“研究，以改善个人对PRO的解释 用于临床实践的患者水平”。此外，还有一个额外的激励措施，以最大限度地利用和 为应对2019冠状病毒病大流行而考虑转向远程医疗服务的PRO的解释。 我们组建了一个由研究科学家和临床专家组成的多学科团队， 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） 和269，468项一年两次的调查，第二项是8，687项PwMS的结构化电子健康记录（EHR）见 克利夫兰诊所的梅伦MS治疗和研究中心（MCMS），平均时间为4年（0.5-8.4 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 活动性、灵活性、视力、疲劳、认知、膀胱/肠、感觉、痉挛、抑郁的测量， 震颤/协调和疼痛。我们提出了四个互补的目标，将：1。表征整体纵向 每11个MS-PROM的损伤模式; 2.识别Pw MS的不同集群，其中具有相似的症状模式， 和跨功能域使用机器学习方法; 3.制定新的方法来评估 因果推理和识别无监督机器学习中模型预测误差的来源;以及4.创建动态 基于目标1-3的发现预测MS表型的模拟仪表板。 有了这些目标，我们寻求推进MS表型，以促进研究，临床护理， 自我管理的方法。通过专注于PROM，我们将利用PwMS的经验， 这是远程医疗的理想选择（即应用于农村居民）。我们希望我们的发现能促进护理 并使PwMS能够参与需要个性化表型表征的健康决策。

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.