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188 Validation of an administrative algorithm to assess SLE disease severity
  1. Cameron B Speyer1,
  2. Daniel Li1,
  3. Hongshu Guan1,
  4. Kazuki Yoshida1,
  5. April Jorge2,
  6. Candace H Feldman1 and
  7. Karen H Costenbader1
  1. 1Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital
  2. 2Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital


Background SLE severity is heterogeneous: some patients have mild disease with rashes and arthritis, while others have severe multi-organ system involvement. It is challenging to study SLE in administrative databases given this heterogeneity. Garris et al developed an administrative claims-based SLE severity algorithm derived from elements of the SLEDAI, SLAM and BILAG instruments (Garris, J Med Econ 2013). It employs ICD-9, CPT and NDC claims over a 1 year period and classifies patients as having mild, moderate or severe disease. We sought to validate this administrative algorithm in comparison to SLEDAI scores at clinical visits.

Methods We identified 100 SLE patients followed at the Brigham and Womens Hospital (BWH) Lupus Center (2008–2010) with SLEDAI-2K (Gladman, J Rheumatol 2002) data at each visit over a 1 year period per person. We also obtained ICD-9, CPT and NDC codes for the Garris algorithm items (e.g. codes for glucocorticoids, ICD-9 codes for pericarditis) for the same year per subject. We compared Garris SLE severity to the highest SLEDAI-2K in that year. We defined the SLEDAI-2K categories of mild <3, moderate 3–6, and severe >6 as in the literature (Polachek, Arthritis Care Res 2017). We compared classification in binary categories of mild vs. moderate/severe and mild/moderate vs. severe. For each, we calculated sensitivity, specificity, and C-statistics.

Results We analyzed 377 SLEDAI-2K assessments on 100 subjects (mean 3.77 [SD 2.63]) in the BWH Lupus Cohort. For the Garris vs. highest SLEDAI-2K model, 56 of 100 subjects were classified similarly by Garris and highest SLEDAI-2K (23/36 mild, 22/34 moderate, and 11/36 severe by SLEDAI-2K). The performance characteristics compared to the highest SLEDAI-2K of the year were: C-statistics were 0.755 for mild/moderate vs. severe SLE severity and 0.740 for mild vs. moderate/severe (table). Sensitivity of the Garris algorithm compared to the highest SLEDAI-2K were 63.9% for mild vs. moderate/severe and 94.3% for mild/moderate vs. severe. Specificity was 82.8% for mild vs. moderate/severe, but 36.7% for mild/moderate vs. severe.

Abstract 188 Table 1

Garris administrative algorithm for SLE severity vs. highest SLEDAI-2K

Conclusions The Garris algorithm, developed for use in administrative datasets, has acceptable performance for classifying SLE severity when compared to the gold standard of highest SLEDAI-2K assessment in 1 year in a Lupus Center. It may be used to classify patients in administrative datasets according to their SLE severity over 1 year.

Funding Source(s): Dr. Costenbaders research is supported by NIAMS R01 AR057327 and K24 AR066109.

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