Division of Rheumatology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Division of Clinical Immunology and Rheumatology, The University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, USA.
Lupus Sci Med. 2024 Oct 14;11(2):e001329. doi: 10.1136/lupus-2024-001329.
This study aimed to validate claims-based algorithms for identifying SLE and lupus nephritis (LN) in Medicare data, enhancing the use of the Lupus Index for geospatial research on SLE prevalence and outcomes.
We retrospectively evaluated the performance of rule-based algorithms using the International Classification of Diseases, 10th Revision (ICD-10) codes to identify SLE and LN in a well-defined prospective longitudinal cohort of patients with and without SLE from a South Carolina registry and rheumatology outpatient clinics. The analysis included comparison of algorithms based on Medicare fee-for-service claims data to these rigorously phenotyped populations. The primary classification for SLE cases was based on the American College of Rheumatology and Systemic Lupus Erythematosus International Collaborating Clinics criteria for SLE and LN. Algorithms were based on the number of ICD-10 codes with and without a 30-day separation in the observation period, including all of 2016-2018.
The algorithm using two ICD-10 codes for SLE, with or without a 30-day separation, showed the best overall performance. For LN, specific ICD-10 codes outperformed combinations of SLE and renal/proteinuria codes that were found in ICD-9.
The findings of this study highlight the performance of specific ICD-10 code algorithms in identifying SLE and LN cases within Medicare data, providing a valuable tool for informing use of the Lupus Index. This index allows for improved geographical targeting of clinical resources, health disparity studies and clinical trial site selection. The study underscores the importance of algorithm selection based on research objectives, recommending more specific algorithms for precise tasks like clinical trial site identification and less specific ones for broader applications such as health disparities research.
本研究旨在验证基于索赔的算法在医疗保险数据中识别系统性红斑狼疮（SLE）和狼疮性肾炎（LN）的有效性，增强了使用狼疮指数进行 SLE 患病率和结局的地理空间研究。
我们回顾性地评估了基于国际疾病分类，第 10 版（ICD-10）代码的规则算法的性能，以在南卡罗来纳州登记处和风湿病门诊诊所的明确前瞻性纵向队列中识别有和没有 SLE 的患者中的 SLE 和 LN。分析包括比较基于医疗保险按服务收费索赔数据的算法与这些严格表型人群的算法。SLE 病例的主要分类基于美国风湿病学会和系统性红斑狼疮国际合作临床的 SLE 和 LN 标准。算法基于观察期内没有或有 30 天间隔的 ICD-10 代码数量，包括 2016-2018 年的所有代码。
使用两个 ICD-10 代码（无论是否有 30 天间隔）的算法显示出最佳的整体性能。对于 LN，特定的 ICD-10 代码优于在 ICD-9 中发现的 SLE 和肾脏/蛋白尿代码的组合。
这项研究的结果强调了特定 ICD-10 代码算法在医疗保险数据中识别 SLE 和 LN 病例的性能，为使用狼疮指数提供了有价值的工具。该指数允许更精确地针对临床资源、健康差异研究和临床试验地点选择进行地理定位。该研究强调了根据研究目标选择算法的重要性，建议更具体的算法用于精确的任务，如临床试验地点识别，而更不具体的算法用于更广泛的应用，如健康差异研究。
