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

骨关节炎（OA）影响美国1400万人和全球3亿多成年人。的 疾病的特征是关节疼痛和功能限制，并与健康状况不佳有关， 相关的生活质量和医疗保健利用率的提高。髋关节和膝关节OA排在第11位， 是全球残疾人的最大贡献者。尽管膝关节OA的临床和经济影响， 目前可获得疾病调节剂;目前的治疗仅限于症状控制， 只是适度有效。虽然有几种有前途的治疗方法正在酝酿中， 测试OA的治疗因疾病异质性而复杂化。我们迫切需要找出 正确的患者进行正确的治疗，以确保新疗法在适当的 人口这一提议旨在利用机器学习方法来解决我们理解上的差距 膝关节OA的疾病异质性。我们将使用来自FNIH OA生物标志物的公开数据， 财团项目。这项对600名膝关节OA受试者的研究包括200多个参数， 描述关节结构和疾病严重程度，包括软骨，骨，韧带， 恶心和炎症。使用基于模型的聚类的无监督学习方法将 用于区分疾病表型。为了在实践中实现表型分型， 必须鉴定满足预测准确性和可行性的挑战的生物标志物。 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， 识别重要变量并开发预测模型以确定表型。最后 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 进展申请人Jamie柯林斯博士是骨科和关节炎中心的生物统计学家 布里格姆妇女医院的成果研究柯林斯医生是一名忠诚的调查员 流变学研究，在该领域发表了8篇第一作者论文。她拥有一个职业发展 流变学研究基金会的奖励和布里格姆研究中心的试点资金 院该提案将提供受保护的时间和严格的培训，以便申请人能够 扩展她目前的生物统计技能，以涵盖数据科学的新兴领域， 机器学习她将在哈佛TH Chan公共卫生学院学习课程， 有机会获得由布里格姆研究所和 哈佛催化剂项目。申请人将得到Elena Losina博士的指导， Tuhina Neogi，以及Tianxi Cai、Jeffrey杜里亚、Ali Guermazi博士咨询委员会的意见， 蒂娜·卡普尔，弗吉尼亚·克劳斯，凯瑟琳·廖，和科特·斯平德勒。建议的研究和培训 该奖项将解决我们对OA异质性的理解中的关键研究空白， 进展这将使柯林斯博士走上独立和长期职业生涯的道路 目标是成为一名独立的调查员，专注于应用先进的分析方法， OA研究。

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.