Updating and Validating the Rheumatic Disease Comorbidity Index to ICD-10-CM

Background/Objective: Comorbidities can contribute to increased risk for mortality and disability in individuals with rheumatoid arthritis (RA)1,2. The Rheumatic Disease Comorbidity Index (RDCI) assesses 11 comorbidities and produces a weighted score (0-9) that accurately predicts several health outcomes3. The RDCI was developed with self-report data and later validated with ICD-9-CM codes collected from administrative data3,4. On October 1, 2015, the U.S. transitioned to ICD-10-CM, resulting in a nearly five-fold increase in the number of codes available to classify conditions5. Our objective was to update the RDCI by translating it into ICD-10-CM. Methods: We defined an ICD-9-CM cohort and an ICD-10-CM cohort using patient data from the Veterans Affairs Rheumatoid Arthritis Registry (VARA). ICD-10-CM codes were generated by converting ICD-9-CM codes using tools that provide suggested crosswalks, and the codes were reviewed by a physician to assess clinical relevance. Comorbidities were collected from national VA administrative data over a two-year period in both cohorts (ICD-9-CM: October 1, 2013 to September 30, 2015; ICD-10-CM: January 1, 2016 to December 31, 2017). Comorbidity frequencies were compared using Cohen’s Kappa, and RDCI scores were compared using Intraclass Correlation Coefficients (ICC). Results: Both the ICD-9-CM cohort (n=1,082) and ICD-10-CM cohort (n=1,446) were predominantly male (ICD-9-CM: 89%; ICD-10-CM: 87%), Caucasian (ICD-9-CM: 76%; ICD-10-CM: 73%), and middle to old-aged (ICD-9-CM: 67.3 ± 10.2 years; ICD-10-CM: 68.2 ± 10.0 years). Prevalence of comorbidities were similar between coding systems, with absolute differences less than 4% (range: 0.28 to 3.91). Myocardial infarction, hypertension, diabetes mellitus, depression, stroke, other cardiovascular, lung disease, and cancer had moderate agreement or higher (range κ: 0.47 to 0.84), while fracture and ulcer/stomach problem had slight and fair agreement, respectively (κ = 0.13; κ = 0.27)6,7. The RDCI scores were 2.95 ± 1.73 (mean ± SD) for the ICD-9-CM cohort and 2.93 ± 1.75 for the ICD-10-CM cohort. RDCI scores had moderate agreement (ICC: 0.71; 95% CI: 0.68-0.74)8 among individuals who were observed during both the ICD-9-CM and ICD-10-CM eras. Conclusion: We have mapped the RDCI from ICD-9-CM to ICD-10-CM codes, generating comparable RDCI scores in a large RA registry. Individual comorbidity agreement varied, with more chronic conditions such as diabetes and hypertension having higher agreement and more acute conditions such as fractures and ulcer/stomach problems having lower agreement. The updated RDCI can be used in clinical outcomes research with ICD-10-CM era patient data.https://digitalcommons.unmc.edu/surp2021/1043/thumbnail.jp

Lowering Expectations: Glucocorticoid Tapering Among Veterans With Rheumatoid Arthritis Achieving Low Disease Activity on Stable Biologic Therapy

Objective In the Steroid EliMination In Rheumatoid Arthritis (SEMIRA) trial, 65% of patients with rheumatoid arthritis (RA) in low disease activity (LDA) on stable biologic therapy successfully tapered glucocorticoids. We aimed to evaluate real‐world rates of glucocorticoid tapering among similar patients in the Veterans Affairs Rheumatoid Arthritis registry. Methods Within a multicenter, prospective RA cohort, we used registry data and linked pharmacy claims from 2003 to 2021 to identify chronic prednisone users achieving LDA after initiating a new biologic or targeted synthetic disease‐modifying antirheumatic drug (b/tsDMARD). We defined the index date as first LDA occurring 60 to 180 days after b/tsDMARD initiation. The primary outcome of successful tapering, assessed at day 180 after LDA, required a 30‐day averaged prednisone dose both less than or equal to 5mg/day and at least 50% lower than at the index date. The secondary outcome was discontinuation, defined as a prednisone dose of 0 mg/day at days 180 through 210. We used univariate statistics to compare patient characteristics by fulfillment of the primary outcome. Results We evaluated 100 b/tsDMARD courses among 95 patients. Fifty‐four courses resulted in successful tapering; 33 resulted in discontinuation. Positive rheumatoid factor, higher erythrocyte sedimentation rate, more background DMARDs, shorter time from b/tsDMARD initiation to LDA, and higher glucocorticoid dose 30 days before LDA were associated with greater likelihood of successful tapering. Conclusion In a real‐world RA cohort of chronic glucocorticoid users in LDA, half successfully tapered and a third discontinued prednisone within 6 months of initiating a new b/tsDMARD. Claims‐based algorithms of glucocorticoid tapering and discontinuation may be useful to evaluate predictors of tapering in administrative data sets

Malondialdehyde-Acetaldehyde Adducts and Antibody Responses in Rheumatoid Arthritis-Associated Interstitial Lung Disease

To compare serum anti-malondialdehyde-acetaldehyde (anti-MAA) antibody levels and MAA expression in lung tissue from patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD) to those found in controls. Anti-MAA antibody (IgA, IgM, IgG) concentrations were measured in patients with RA-ILD and compared to those of RA patients with chronic obstructive pulmonary disease (COPD) and RA patients without lung disease. Associations between anti-MAA antibody with RA-ILD were assessed using multivariable logistic regression. Lung tissue from patients with RA-ILD, other ILD, or emphysema, and from controls (n = 3 per group) were stained for MAA, citrulline, macrophages (CD68), T cells (CD3), B cells (CD19/CD27), and extracellular matrix proteins (type II collagen, fibronectin, vimentin). Tissue expression and colocalization with MAA were quantified and compared. Among 1,823 RA patients, 90 had prevalent RA-ILD. Serum IgA and IgM anti-MAA antibody concentrations were higher in RA-ILD than in RA with COPD or RA alone (P = 0.005). After adjustment for covariates, the highest quartiles of IgA anti-MAA antibody concentration (odds ratio 2.09 [95% confidence interval 1.11-3.90]) and IgM (odds ratio 2.23 [95% confidence interval 1.19-4.15]) were significantly associated with the presence of RA-ILD. MAA expression in RA-ILD lung tissue was greater than in tissue from all other groups (P < 0.001), and it colocalized with citrulline (r = 0.79), CD19+ B cells (r = 0.78), and extracellular matrix proteins (type II collagen [r = 0.72] and vimentin [r = 0.77]) to the greatest degree in RA-ILD. Serum IgA and IgM anti-MAA antibody is associated with ILD among RA patients. MAA is highly expressed in RA-ILD lung tissue, where it colocalizes with other RA autoantigens, autoreactive B cells, and extracellular matrix proteins, highlighting its potential role in the pathogenesis of RA-ILD