Coffey, Caitrín M.

Objective To identify clusters of comorbidities in patients with rheumatoid arthritis (RA) using 4 methods and to compare to patients without RA. Methods In this retrospective, population‐based study, residents of 8 Minnesota counties with prevalent RA as of January 1, 2015 were identified. Age‐, sex‐, and county‐matched non‐RA comparators were selected from the same underlying population. Diagnostic codes were retrieved for 5 years before January 1, 2015. Using 2 codes ≥30 days apart, 44 previously defined morbidities and 11 nonoverlapping chronic disease categories based on Clinical Classifications Software were defined. Unsupervised machine learning methods of interest included hierarchical clustering, factor analysis, K‐means clustering, and network analysis. Results Two groups of 1,643 patients with and without RA (72% female; mean age 63.1 years in both groups) were studied. Clustering of comorbidities revealed strong associations among mental/behavioral comorbidities and among cardiovascular risk factors and diseases. The clusters were associated with age and sex. Differences between the 4 clustering methods were driven by comorbidities that are rare and those that were weakly associated with other comorbidities. Common comorbidities tended to group together consistently across approaches. The instability of clusters when using different random seeds or bootstrap sampling impugns the usefulness and reliability of these methods. Clusters of common comorbidities between RA and non‐RA cohorts were similar. Conclusion Despite the higher comorbidity burden in patients with RA compared to the general population, clustering comorbidities did not identify substantial differences in comorbidity patterns between the RA and non‐RA cohorts. The instability of clustering methods suggests caution when interpreting clustering using 1 method.

Objective To comprehensively assess multimorbidity burden in patients with rheumatoid arthritis (RA) in order to unify the multimorbidity definition for RA research and clinical practice. Methods In this population-based study, residents of eight Minnesota counties with prevalent RA on 1 January 2015 were identified. Age, sex and county-matched non-RA comparators were selected from the same population. Diagnostic codes were retrieved for 5 years before 1 January 2015. Using two codes ≥30 days apart, 44 previously defined morbidities and 78 non-overlapping chronic disease categories based on Clinical Classification Software were defined. Prevalence of each morbidity in the RA versus non-RA cohorts was compared using false discovery rate to adjust for multiple comparisons. Morbidities more common in RA than non-RA and those with prevalence ≥5% were retained. Results 1643 patients with RA and 1643 non-RA subjects (72% women; mean age 63.1 years) were studied. Using the 44 morbidities, multimorbidity (defined as 2+ morbidities) was present in 1411 (86%) of RA and 1164 (71%) of non-RA subjects (p<0.001) with 5+ morbidities present in 907 (55%) of RA and 619 (38%) of non-RA (p<0.001). Patients with RA had significantly higher prevalence of 24 of the 44 morbidities compared with non-RA, especially interstitial lung disease, fibromyalgia, osteoarthritis and osteoporosis. Among the additional 78 categories, 7 were significantly higher in RA than non-RA, including organic sleep disorders, vitamin D deficiency and foot ulcers. Conclusion Patients with RA have a higher prevalence of multimorbidity compared with non-RA subjects. These results confirm the list of 44 morbidities and add several other morbidities of interest in RA.

Background/Purpose: To update the epidemiology of systemic sclerosis and evaluate the performance of the American College of Rheumatology/European League Against Rheumatism 2013 versus 1980 American Rheumatism Association classification criteria in a US population-based cohort. Methods: An inception cohort of patients with incident systemic sclerosis from 1 January 1980 through 31 December 2018, in Olmsted County, Minnesota, was identified based on comprehensive individual medical record review. Incidence and prevalence rates were age- and sex-adjusted to the 2010 US white population. Survival rates were compared with expected rates in the general population. Fulfillment of 1980 and 2013 classification criteria was ascertained. Results: A total of 85 incident cases of systemic sclerosis (91% female, mean age = 55.4 ± 16 years) and 49 prevalent cases on 1 January 2015 were identified. The overall age- and sex-adjusted annual incidence was 25 (95% confidence interval = 20–31) per million population, with no change in incidence over time. The age- and sex-adjusted prevalence was 436 (95% confidence interval = 313–558) per 1,000,000 population. 77 (91%) patients fulfilled the 2013 classification criteria, and 38 (45%) fulfilled the 1980 criteria. Mortality among patients with systemic sclerosis was significantly higher in comparison to the general population, with a standardized mortality ratio of 2.48 (95% confidence interval = 1.76–3.39) and no evidence of improvement over time. Conclusion: Systemic sclerosis developed in 25 persons/million/year with no change over the 39-year study period. The 2013 classification criteria perform significantly better than the 1980 criteria but failed to classify 9% of patients. Systemic sclerosis portends a 2.5-fold higher risk of mortality than the general population, with no evidence of improved survival over time.