Article Text
Abstract
Background/Purpose Systemic lupus erythematosus (SLE) is a chronic autoimmune disease in which clinical symptoms and laboratory measurements are heterogeneous across patients.1 2 Such clinical heterogeneity complicates diagnosis and management. To characterize the phenotype of lupus patients before and after diagnosis and also determine the most common clinical management paradigms for lupus patients, we investigated claims data with particular attention to the year before and after diagnosis, focusing on patients with general SLE, SLE patients with renal involvement [lupus nephritis (LN)] and patients with only cutaneous manifestations [cutaneous lupus erythematosus (CLE)].
Methods Data were acquired using both adjudicated (Closed) and non-adjudicated (Open) commercial databases (from EVERSANA) of patients across the United States. Both databases include claims for diagnoses, procedures, prescriptions, and physician specialties. Analyses were conducted between April 2022 and March 2023. To increase stringency in the identification of lupus patients, cohorts created for LN, SLE, and CLE required two of the specified diagnoses within a six-month period. Altogether, over 100,000 lupus patients were identified by our specifications in the Closed Claims database, and ~38,000 lupus patients in the Open Claims database.
Results The cumulative percentages of claims for diagnoses, laboratory testing, procedures and medication generally increased in the year before diagnosis and over the subsequent year. Although we observed differences among the cohorts with respect to concomitant diagnoses and laboratory testing, the basis for diagnosis of patients in each cohort was not always apparent. For example, at index date only 53.4% of SLE patients had received an ANA test and only 43.4% had received an anti-dsDNA test, with comparably low frequencies in LN and CLE. Moreover, at diagnosis, only 8.9% of LN patients had received a kidney biopsy and 23.3% of CLE patients had received a skin biopsy. Subspecialty care by rheumatologists, nephrologists, and dermatologists was associated with increased testing in many instances. Anti-dsDNA and complement testing were increased in patients who had encountered a rheumatologist, kidney biopsies were increased in patients who had encountered a nephrologist, and skin biopsies were increased in patients who had encountered a dermatologist. In addition, there were also differences among cohorts with regard to drug management and emergency department (ED) visits. Of the drug prescriptions examined, at index opioids had the greatest cumulative frequency in LN and SLE, whereas hydroxychloroquine had the highest cumulative frequency in CLE. Among other standard of care drugs, cyclophosphamide was prescribed minimally, mycophenolate mofetil/mycophenolic acid was prescribed more in LN, and methotrexate was prescribed more in SLE. Moreover, when a matched control population was examined, opioid prescription was higher among all lupus cohorts than controls. Notably, LN patients had a greater frequency of ED visits; LN patients with an encounter with a rheumatologist had fewer ED visits, whereas an encounter with a nephrologist was associated with more ED visits. Finally, cost of care was increased in lupus cohorts in the year before diagnosis and the year subsequently, and was highest in LN.
Conclusion The steadily increasing frequency of laboratory tests, emergency department visits, and cost in the year before diagnosis demonstrates the complexity of lupus diagnosis and management. At diagnosis and thereafter, there are major differences between the evaluation and management of lupus patients observed in the general care community reflected within claims databases than those set forth by professional society guidelines.
References
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Weckerle CE, Franek BS, Kelly JA, Kumabe M, Mikolaitis RA, Green SL, et al. Network analysis of associations between serum interferon-α activity, autoantibodies, and clinical features in systemic lupus erythematosus. Arthritis Rheum 2011;63:1044–1053.
Acknowledgments We thank Mark Delsesto, Ryan Rumantir, David Kauffman, and Shailendra Singh for their assistance with cohort generation/design and organization of the database. We thank C. Neil Lyons for providing the data to convert the cost to 2022 dollars.
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