Data-driven approaches in defining knee osteoarthritis phenotypes and factorsassociated 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: 10407625
• 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/10407625
• 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; Persons; 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）影响美国的1400万个人和全球超过3亿成人。这 疾病的特征是关节疼痛和功能局限性，与健康状况不佳有关 相关的生活质量和增加的医疗保健利用。臀部和膝盖的OA排名第11 全球残疾的最高贡献者。尽管膝盖OA产生了临床和经济影响，但没有 目前可以使用改良疾病的剂；当前治疗仅限于症状控制和 虽然正在进行几种有希望的治疗方法，但正在发展和 OA的测试处理因疾病异质性而复杂。我们迫切需要确定 正确的患者进行正确的治疗，以确保对适当的新疗法进行测试 人口。该建议旨在使用机器学习方法来解决我们理解中的差距 膝盖OA中的疾病异质性。我们将使用FNIH OA生物标志物中的公开数据 财团项目。这项对600名膝盖OA受试者的研究包括200多个参数 描述关节结构和疾病的严重程度，包括软骨，骨骼，韧带， 半月板和炎症。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。在实践中实施表型 必须确定生物标志物既应对预测精度和可行性的挑战。 第二个目标将研究基于模型的聚类中的可变选择方法，以便 确定重要变量并开发一个预测模型以确定表型。最后，一个 通过超级学习监督机器学习方法将调查算法以预测疾病 进展。申请人杰米·柯林斯（Jamie Collins）博士是骨科和关节炎中心的生物统计学家 在杨百翰和妇女医院进行结果研究。柯林斯博士是一位致力于的调查员 风湿病学研究该领域的八个第一作者出版物。她拥有职业发展 风湿病研究基金会和Brigham Research的试点资金奖 研究所。该建议将提供受保护的时间和严格的培训，以便申请人可以 扩展她当前的生物统计技能设置，以涵盖数据科学和 机器学习。她将在哈佛大学的公共卫生学院参加课程，并将 可以访问Brigham Research Institute和 哈佛催化剂计划。申请人将得到DRS的Mentalship的支持。 Elena Losina和 Tuhina Neogi和Dr咨询委员会的意见。天西·凯（Tianxi Cai），杰弗里·杜里亚（Jeffrey Duryea），阿里·格拉马西（Ali Guermazi Tina Kapur，Virginia Kraus，Katherine Liao和Kurt Spindler。提出的研究和培训 该奖项将解决我们对OA异质性和 进展。这将使柯林斯博士走上通往独立的道路和她的长期职业 成为独立研究者的目标，重点是应用高级分析方法 OA研究。