Bayesian approaches to identify persons with osteoarthritis in electronic health records and administrative health data in the absence of a perfect reference standard

项目总结/摘要 骨关节炎（OA）是全球疾病负担研究的残疾年数的主要贡献者 （YLDs）衡量，由于老龄化和肥胖率的增加，它的排名正在稳步上升。没有 虽然目前有延缓OA进展的治疗方法，但关节置换术的费用使医疗保险预算紧张 并造成巨大的经济影响和公共卫生负担。我们迫切需要一个更好的 了解预防或延缓OA恶化的因素及其对人群的影响。电子健康 电子病历（EHR）和行政卫生数据是现实世界中数据驱动方法的主要资源 证据研究，并越来越多地用于研究疾病的风险因素和治疗方法以及遗传学， 疾病，如来自英国生物库的那些，其中这些数据库用于识别 与遗传易感性有关这些数据库拥有数百万患者的数据， 护理利用和成本，护理不公平，OA患病率及其负担的增加，治疗，依从性 以及伴随的合并症。使用行政数据进行研究的有效性;然而， 依赖于对疾病病例的准确描述和识别。这项研究的长期目标是 改进EHR和行政健康数据中OA病例确定。这项提议的核心假设是 通过使用多个数据元素，包括不完善的诊断和程序代码， 数据元素之间的条件依赖性、OA病例发现的准确性和预测值 算法可以得到实质性的改进。使用保险索赔数据从一个最大的行政 美国的健康数据库、MarketScan和波士顿医疗中心的EHR数据，该提案旨在（1） 在大型管理数据库中开发一种算法，以估计个人患OA的概率 考虑到其多个诊断和程序代码的条件依赖性;（2）在EHR中 数据库，比较我们的方法与传统的诊断/程序代码为基础的算法验证 反对病历审查这项工作的贡献是显着的，因为它是第一个OA算法利用 其数据元素之间的条件依赖关系，以提高准确性。此外，拟议的 该方法的重要性在于，它可以广泛地应用于其他病症和疾病， 使用行政卫生数据，大幅提高真实世界证据观察性研究的质量。

PROJECT SUMMARY/ABSTRACT Osteoarthritis (OA) is a leading contributor to the Global Burden of Disease Study’s Years Lived with Disability (YLDs) measure, and due to aging and increasing rates of obesity, its ranking is steadily rising. With no treatments that delay progression of OA available, the cost of joint replacements is straining Medicare budgets and contributing to enormous economic impact and public health burdens. We are in critical need of a better understanding of factors that prevent or delay worsening OA and of its population impact. Electronic health records (EHR) and administrative health data are a major resource for data-driven approaches in real-world evidence studies and are increasingly used to study disease risk factors and treatments and the genetics of disease such as those from the UK Biobank, where these databases are used to identify cases of disease that tied to genetic susceptibilities. With data on millions of patients, these databases also allow inquiries into health care utilization and costs, inequities of care, growing prevalence of OA and its burden, treatments, adherence of care to guidelines and attendant comorbidities. The validity of research using administrative data; however, relies on accurate characterization and identification of disease cases. The long-term goal of this research is to improve OA case ascertainment in EHR and administrative health data. The central hypothesis of this proposal is that by using multiple data elements including imperfect diagnosis and procedures codes and understanding the conditional dependence among the data elements, the accuracy and predictive values of OA case finding algorithms can be substantially improved. Using insurance claims data from one of the largest administrative health databases in the US, MarketScan, and EHR data from Boston Medical Center, this proposal aims (1) to develop an algorithm in a large administrative database to estimate the probability of OA in an individual accounting for the conditional dependence of its multiple diagnosis and procedure codes; (2) in an EHR database, to compare our approach with conventional diagnosis/procedure code-based algorithms validated against chart review. The contribution of this work is significant because it is the first OA algorithm to exploit the conditional dependencies between its data elements to improve accuracy. Further, the proposed methodology is significant in that it can be broadly applied to other conditions and diseases that can substantially improve the quality of real-world evidence observational studies using administrative health data.