Data-driven approaches in defining knee osteoarthritis phenotypes and factors associated with fast progression

定义膝骨关节炎表型和与快速进展相关的因素的数据驱动方法

• 批准号: 10208726
• 负责人: Jamie E. Collins
• 金额: $ 13.1万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-02 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10208726
• 关键词: Address; Adult; Advisory Committees; Affect; Algorithms; Arthralgia; Arthritis; Award; Biochemical; Biological Markers; Biometry; Cartilage; Catabolism; Chronic; Clinic; Clinical; Clinical Trials; Complex; Data; Data Science; Degenerative polyarthritis; Development; Disease; Disease Progression; Economic Burden; Ensure; Foundations; Funding; Genetic; Health Expenditures; Heterogeneity; Hip Osteoarthritis; Hospitals; Image; Individual; Inflammation; Inflammatory; Intervention; Investigation; Joints; K-Series Research Career Programs; Knee Osteoarthritis; Lead; Learning; Ligaments; Machine Learning; Magnetic Resonance Imaging; Measures; Mechanics; Meniscus structure of joint; Mentorship; Metabolic; Methods; Modeling; Muscle; Obesity Epidemic; Orthopedics; Outcomes Research; Patient Rights; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Population; Prevalence; Probability; Process; Public Health Schools; Publications; Reporting; Research; Research Institute; Research Personnel; Research Training; Rheumatology; Right to Treatments; Risk Factors; Serum; Severity of illness; Structure; Symptoms; Synovial Membrane; Testing; Therapeutic Intervention; Time; Tissues; Training; United States; Urine; Virginia; Woman; Work; advanced analytics; aging population; analytical method; base; bone; career; career development; catalyst; complex data; disability; disabling disease; disease heterogeneity; disease phenotype; disorder subtype; economic impact; effective therapy; health care service utilization; health related quality of life; imaging biomarker; insight; machine learning method; novel; novel therapeutics; outcome prediction; predictive modeling; programs; quantitative imaging; risk prediction; skills; supervised learning; therapy development; treatment effect; unsupervised learning

Osteoarthritis (OA) affects 14 million individuals in the US and over 300 million adults worldwide. The disease is characterized by joint pain and functional limitations and is associated with poor health- related quality of life and increased healthcare utilization. OA of the hips and knees ranks as the 11th highest contributor to global disability. Despite the clinical and economic impact of knee OA, no disease-modifying agents are currently available; current treatments are limited to symptom control and are only modestly efficacious. While several promising treatments are in the pipeline, developing and testing treatments for OA is complicated by disease heterogeneity. We urgently need to identify the right patient for the right treatment to ensure that new therapies are being tested on the appropriate population. This proposal aims to use machine learning methods to address gaps in our understanding of disease heterogeneity in knee OA. We will use publicly available data from the FNIH OA Biomarkers Consortium project. This study of 600 subjects with knee OA includes over 200 parameters that describe the joint structure and disease severity, including measures of cartilage, bone, ligaments, menisci, and inflammation. An unsupervised learning approach using model-based clustering will be used to distinguish disease phenotypes. To implement phenotyping in practice a minimal set of biomarkers must be identified that meets the challenges of both predictive accuracy and feasibility. Thus the second aim will investigate variable selection methods in model-based clustering in order to identify important variables and develop a prediction model to determine phenotype. Finally, a supervised machine learning approach via super learning will investigate algorithms to predict disease progression. The applicant, Dr. Jamie Collins, is a biostatistician at the Orthopedic and Arthritis Center for Outcomes Research at Brigham and Women’s Hospital. Dr. Collins is a committed investigator in rheumatology research with eight first author publications in the field. She holds a career development award from the Rheumatology Research Foundation and pilot funding from the Brigham Research Institute. This proposal will provide protected time and rigorous training so that the applicant can expand her current biostatistical skill set to encompass the burgeoning fields of data science and machine learning. She will take coursework at the Harvard TH Chan School of Public Health and will have access to courses, seminars, and training provided by the Brigham Research Institute and the Harvard Catalyst Program. The applicant will be supported by mentorship from Drs. Elena Losina and Tuhina Neogi, and input from the advisory committee of Drs. Tianxi Cai, Jeffrey Duryea, Ali Guermazi, Tina Kapur, Virginia Kraus, Katherine Liao, and Kurt Spindler. The research and training proposed in this award will address critical research gaps in our understanding of OA heterogeneity and progression. This will set Dr. Collins on the path towards independence and her long-term career objective of being an independent investigator with a focus on applying advanced analytic methods in OA research.

骨关节炎(OA)在美国影响1400万人，在全球范围内影响超过3亿成年人。这个 疾病的特征是关节疼痛和功能受限，并与健康状况不佳有关- 相关的生活质量和更高的医疗保健利用率。髋关节和膝盖的骨性关节炎排在第11位 全球残疾的最大贡献者。尽管膝骨性关节炎对临床和经济有影响，但没有 疾病改良剂目前是可用的；目前的治疗仅限于症状控制和 只能起到一定的效果。虽然有几种有希望的治疗方法正在酝酿之中，但正在开发和 OA的测试治疗因疾病的异质性而变得复杂。我们迫切需要确定 为正确的治疗选择合适的患者，以确保新的治疗方法在适当的 人口。这项提议旨在使用机器学习方法来解决我们理解上的差距 膝骨性关节炎的疾病异质性。我们将使用来自FNIH OA生物标记物的公开数据 财团项目。这项对600名膝骨性关节炎患者的研究包括200多个参数，这些参数 描述关节结构和疾病严重程度，包括软骨、骨骼、韧带、 半月板和炎症。一种使用基于模型的聚类的无监督学习方法将是 用于区分疾病表型。要在实践中实现表型分析，最小集 生物标记物必须满足预测准确性和可行性的挑战。 因此，第二个目标将研究基于模型的聚类中的变量选择方法，以便 确定重要的变量并开发预测模型来确定表型。最后，一个 通过超级学习的监督机器学习方法将研究预测疾病的算法 进步。申请人杰米·柯林斯博士是整形外科和关节炎中心的生物统计学家 在布里格姆妇女医院进行结果研究。柯林斯博士是一名致力于 风湿病研究，在该领域有8篇第一作者出版物。她拥有一份职业发展 风湿病研究基金会颁发的奖项和布里格姆研究中心提供的试点资金 研究所。这项提议将提供受保护的时间和严格的培训，以便申请者能够 扩展她目前的生物统计学技能集，以涵盖新兴的数据科学和 机器学习。她将在哈佛大学陈德熙公共卫生学院学习课程，并将 能够参加由布里格姆研究所和 哈佛大学催化剂计划。申请者将由Elena Losina博士和 Tuhina Neogi，以及蔡天喜博士、Jeffrey Duryea博士、Ali Guermazi博士咨询委员会的投入， 蒂娜·卡普尔、弗吉尼亚·克劳斯、凯瑟琳·廖和库尔特·斯平德勒。中提出的研究和培训 该奖项将解决我们在理解办公自动化异构性和 进步。这将使柯林斯博士走上独立的道路和她长期的职业生涯 目标是成为一名独立调查员，专注于应用先进的分析方法 办公自动化研究。