Division of Rheumatology, Immunology and Allergy, Brigham & Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Lupus. 2010 May;19(6):741-3. doi: 10.1177/0961203309356289. Epub 2010 Feb 23.
Large administrative databases such as Medicaid billing databases could be used to study care and outcomes of lupus nephritis if these patients could be correctly identified from claims data. We aimed to develop and validate an algorithm for the correct identification of cases of lupus nephritis using ICD-9 billing codes. We used the Research Patient Data Resource query tool at our institution to identify patients with potential lupus nephritis. We compared four ICD-9 code based strategies, identifying patients seen between 2000 and 2007 with Medicaid medical insurance with greater than two claims for a diagnosis of SLE (ICD-9 code 710.0) and a combination of greater than two nephrologist visits and/or renal ICD-9 codes. We assessed performance using the positive predictive value. Two hundred and thirty four subjects were identified and medical records reviewed. Our third strategy, based on a combination of lupus and renal ICD-9 codes and nephrologist encounter claims, had the highest positive predictive value (88%) for the identification of patients with lupus nephritis. This strategy may offer a sound method of identifying patients with lupus nephritis for health services research in addition to serving as a model for using claims data as a way to better understand rare diseases such as lupus.
大型管理式数据库(如医疗补助账单数据库)可用于研究狼疮肾炎的护理和结果,如果这些患者能够从索赔数据中正确识别出来的话。我们旨在开发并验证一种使用 ICD-9 计费代码正确识别狼疮肾炎病例的算法。我们使用本机构的 Research Patient Data Resource 查询工具来确定有潜在狼疮肾炎的患者。我们比较了四种基于 ICD-9 代码的策略,即确定在 2000 年至 2007 年间使用医疗补助医疗保险的患者,这些患者的 SLE(ICD-9 代码 710.0)诊断有两个以上的索赔,以及两个以上的肾科医生就诊和/或肾脏 ICD-9 代码的组合。我们使用阳性预测值来评估性能。确定了 234 名受试者并审查了病历。我们的第三种策略基于狼疮和肾脏 ICD-9 代码以及肾科医生就诊的索赔,对识别狼疮肾炎患者具有最高的阳性预测值(88%)。除了作为使用索赔数据更好地了解狼疮等罕见疾病的一种方式的模型之外,这种策略还可以为卫生服务研究提供一种识别狼疮肾炎患者的可靠方法。