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

基本信息

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
项目类别：
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

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

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项目摘要

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.

项目摘要多发性硬化症（MS）是一种慢性疾病，影响美国90万人，这是年轻人的残疾。具有MS（PWM）的人会在多个领域经历大范围症状，单独或组合，严重程度各不相同。其中一些符号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，认知和情感问题以及不协调。当前应用程序的目标是识别和表征PWMS中的符号模式和簇，与PA-17-462保持一致，其中指出：“多发性硬化症（IS A）……促进（症状）群集研究的模型条件”。这是我们的分析框架将在其他多重症状条件下为研究提供信息。利益相关者同意，患者报告的结果（PRO）和措施（PROM）的好处尚未达到它们对PWM的充分潜力。专业人士对患者的观点提供了宝贵的见解，越来越多地使用在MS临床试验和临床实践中，作为标准临床测量结果无法充分测量跨领域的损伤或缺乏检测细微但有意义的变化的敏感性。与正在进行的全球MS计划保持一致，当前建议将专注于确定和表征专业人士的症状模式和簇；也直接与 Not-OD-20-079，刺激“研究以改善个人的解释的研究”的特殊关注通知在临床实践中使用的患者水平”。此外，还有一种额外的动力来最大化使用和考虑到对远程医疗服务的转变，对远程医疗服务的解释是响应于19日大流行的。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），第一是北美研究委员会多发性硬化症（NARCOMS）注册表的调查数据平均为8.4岁（0.5至14年）和269,468双双年展调查，第二个是8,687个PWM的结构性电子健康记录（EHRS），请参见克利夫兰诊所的MELLEN MS治疗与研究中心（MCMS）平均占4年（0.5-8.4 年）和67,932次访问。在这两个资源中，11个MS特异性舞会（MS-Prom）都被纵向捕获衡量机动性，敏捷性，视力，疲劳，认知，膀胱/肠，感觉，痉挛，抑郁，震颤/协调和痛苦。我们提出的四个完整目标将：1。表征总体纵向每11 ms-prom的损伤模式； 2。确定具有相似症状模式的PW MS的不同簇并使用机器学习方法跨越功能域； 3。开发新方法来评估因果推断并确定无监督的机器学习中模型预测错误的来源；和4。创建动态基于目标1-3的发现，用于预测MS表型的模拟仪表板。以这些目的，我们试图进步MS表型，以促进研究，临床护理和自我管理的方法。通过关注舞会，我们将利用独立的PWM的经验其位置（即向粗糙居民申请），是远程医疗的理想选择。我们希望我们的发现能够促进护理并使PWMS有必要进行健康决策，其中必须进行个性化表型表征。