Data-driven identification of co-morbidities associated with rheumatoid arthritis in a large US health plan claims database
Data-driven identification of co-morbidities associated with rheumatoid arthritis in a large US health plan claims database
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DOI：
10.1186/1471-2474-11-247
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发表时间：
2010-10-25
影响因子：
2.3
通讯作者：
Robinson, Noah Jamie
中科院分区：
文献类型：
--
作者：
Petri, Hans;Maldonato, Debra;Robinson, Noah Jamie
背景资料：在药物开发中，重要的是要了解患有感兴趣疾病的患者组中预期的全部合并症。识别与目标疾病相关的不太常见的事件通常是一个挑战，即使这些事件很严重。本研究的目的是确定与类风湿性关节炎（RA）的合并症相比，与对照组，使用大型医疗database.Methods：Marketscan美国索赔数据库，用于本回顾性队列研究。选择的是至少16岁的人的记录，至少有两个索赔RA，并在2007年6月30日积极的保险状态。对照组至少有两个湿疹/皮炎索赔。对照组按年龄、性别和保险状况（医疗保险与否）进行匹配。在一年的时间窗内，在RA组和对照组中确定了ICD 9诊断代码的所有合并症。计算相对风险（RR）。根据RR的大小对诊断进行排序。排除了涵盖RA和关节病的代码。为了得到稳定的估计，排名排序进行诊断发生在至少20人在control group.Results：记录选择62,681人RA（平均年龄为59.0岁，73.8%的女性，医疗保险覆盖35%）。共记录了6897个不同的ICD 9诊断代码，其中对照组至少20人中有2220个代码[与相对风险一起列出]。除关节/骨骼相关疾病外，未另分类的药物和生物物质的不良反应、未指定的不良反应药物的正确给药、特发性纤维化肺泡炎、骨髓炎、免疫缺陷、沉降率升高、骨钙素试验反应异常或阳性、贫血和库欣综合征与RA密切相关（RR > 3）。大量（> 60，000）诊断为RA的患者的数据用于分析和列出大量（> 2，000）合并症。诊断代码RR的排序是快速识别与RA相关的许多状况的工具。
Background: In drug development, it is important to have an understanding of the full spectrum of co-morbidities to be expected in the group of patients with the disease of interest. It is usually a challenge to identify the less common events associated with the target disease, even if these events are severe. The purpose of this study is to identify co-morbidities associated with rheumatoid arthritis (RA) as compared with a control group, using a large health care database.Methods: Marketscan US claims database was used for this retrospective cohort study. Selected were records of persons aged at least 16 Y with at least two claims for RA, and with active insurance status on June 30,2007. The control group had at least two claims for eczema/dermatitis. Controls were matched by age, gender and insurance status (Medicare or not). All co-morbidities with an ICD9 diagnostic code were identified in the RA and control groups, during a one-year window. Relative risks (RRs) were calculated. Diagnoses were rank-ordered by magnitude of RR. Codes covering RA and arthropathy were excluded. In order to get stable estimates, rank-ordering was performed for diagnoses occurring in at least 20 persons in the control group.Results: Records were selected of 62,681 persons with RA (mean age was 59.0 Y, with 73.8% female, Medicare-covered 35%). A total of 6897 different ICD9 diagnostic codes were recorded, with 2220 codes in at least 20 persons of the control group [listed with Relative Risk]. Apart from joint/bone related conditions, strong associations with RA (RR > 3) were found for Adverse effect medicinal and biological substance not elsewhere classified, Unspecified adverse effect drug properly administered, Idiopathic fibrosing alveolitis, Osteomyelitis, Immune deficiency, Elevated sedimentation rate, Tuberculin test reaction abnormal or positive, Anemia and Cushing syndrome.Conclusions: Data on a large number (> 60,000) of patients with a diagnosis of RA were used to analyze and to list a large number (>2,000) of co-morbidities. Rank-ordering of RRs of diagnostic codes is a tool to identify quickly many conditions associated with RA.