Validating claims-based algorithms for a systemic lupus erythematosus diagnosis in Medicare data for informed use of the Lupus Index: a tool for geospatial research.

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusions: 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.