Myasoedova, Elena

Objectives The objective is to examine utilisation of cardiovascular preventive services in patients with rheumatoid arthritis (RA), compared with a non-RA population, and to examine cardiovascular disease (CVD) screening rates among RA patients without diabetes mellitus (DM), hypertension or hyperlipidaemia to non-RA patients with one of these diagnoses. Methods All ≥18-year-old patients with an RA diagnosis living in one of eight Minnesota counties on 1 January 2015 were included and matched (1:1) by sex, age and county to non-RA comparators. Rates of screening for CVD risk factors, including DM (ie, glucose), hypertension (ie, blood pressure) and hyperlipidaemia (ie, lipids), were compared between groups using Cox models. Results The study included 1614 patients with RA and 1599 non-RA comparators. DM screening was more common among patients with RA (HR: 1.10, 95% CI: 1.01 to 1.19), as was hypertension screening (HR: 1.37, 95% CI: 1.24 to 1.52). Hyperlipidaemia screening in RA was similar to comparators (HR: 0.99, 95% CI: 0.89 to 1.10). Conversely, patients with RA and no CVD risk factors had a lower probability of undergoing diabetes (HR: 0.67, 95% CI: 0.57 to 0.78) and hyperlipidaemia screening (HR: 0.65, 95% CI: 0.54 to 0.79) than non-RA patients with only one CVD risk factor diagnosis. Hypertension screening was similar between both groups. Conclusions RA patients undergo CVD preventive screening at rates at least comparable to the general population. However, patients with RA as their sole CVD risk factor were less likely to undergo screenings, despite an equivalent-to-higher risk as the traditional CVD risk factors. These findings demonstrate opportunities for improvement of RA patient care.

Abstract Objectives Active RA has been associated with an increased risk of both cardiovascular and peripheral vascular disease. We aimed to compare cerebrovascular changes in patients with and without RA, both with and without a neuropathologic diagnosis of neurodegenerative disease. Methods Patients with RA (n = 32) who died and underwent autopsy between 1994 and 2021 were matched to non-RA controls (n = 32) on age, sex and level of neurodegenerative proteinopathy. Routine neuropathologic examination was performed at the time of autopsy. Cerebrovascular disease severity was evaluated using modified Kalaria and Strozyk scales. Clinical dementia diagnoses were manually collected from patients’ medical records. Results Prior to death, 15 (47%) RA patients and 14 (44%) controls were diagnosed with dementia; 9 patients in each group (60% and 64%, respectively) had Alzheimer’s disease. The prevalence of cerebral amyloid angiopathy, microinfarcts, infarcts or strokes was found to be similar between groups. Patients with RA were more likely to have more severe vascular changes in the basal ganglia by Kalaria scale (P = 0.04), but not in other brain areas. There were no significant differences in the presence of large infarcts, lacunar infarcts or leukoencephalopathy by Strozyk scale. Among patients with RA and no clinical diagnosis of dementia, the majority had mild–moderate cerebrovascular abnormalities, and a subset of patients had Alzheimer’s disease neuropathologic changes. Conclusion In this small series of autopsies, patients with and without RA had largely similar cerebrovascular pathology when controlling for neurodegenerative proteinopathies, although patients with RA exhibited more pronounced cerebrovascular disease in the basal ganglia.

ObjectiveThe focus of this study was to assess changes in the cumulative incidence of extra‐articular manifestations of rheumatoid arthritis (ExRAs) and associated mortality risk.MethodsThis study evaluated trends in occurrence of ExRAs using a population‐based inception cohort that included all adult patients with incident rheumatoid arthritis (RA) from 1985 through 2014 meeting the 1987 American College of Rheumatology criteria. Patients were divided into two cohorts based on the incidence date of RA, 1985 to 1999 and 2000 to 2014. The occurrence of ExRAs was determined by manual chart review, and the 10‐year cumulative incidence was estimated for each ExRA in both cohorts. Cox proportional hazard models were used to determine associations between specific demographic and RA disease characteristics and ExRAs and between ExRAs and mortality.ResultsThere were 907 patients included, 296 in the 1985 to 1999 cohort and 611 in the 2000 to 2014 cohort. The 10‐year cumulative incidence of any ExRA decreased significantly between the earlier and later cohorts (45.1% vs 31.6%, P < 0.001). This was largely driven by significant declines in subcutaneous rheumatoid nodules (30.9% vs 15.8%, P < 0.001) and nonsevere ExRAs (41.4% vs 28.8%, P = 0.001). Identified risk factors for the development of any ExRAs include rheumatoid factor positivity (hazard ratio [HR] 2.02, 95% confidence interval [CI] 1.43–2.86) and current smoking (HR 1.61, 95% CI 1.10–2.34). Mortality was increased in patients with either nonsevere (HR 1.83, 95% CI 1.18–2.85) or severe ExRAs (HR 3.05, 95% CI 1.44–6.49).ConclusionsThe incidence of ExRAs has decreased over time. Mortality remains increased in patients with ExRAs.

Abstract Objectives Multimorbidity is burdensome for people with RA. We investigated differences in multimorbidity and comorbidities by sex and age in the RA population. Methods This cross-sectional analysis used national administrative claims (OptumLabs® Data Warehouse) from people with RA and non-RA comparators (matched on age, sex, race, census region, index year and length of baseline insurance coverage) from 2010–2019. RA was determined using a validated algorithm. Multimorbidity was defined as ≥2 (MM2+) or ≥5 (MM5+) comorbidities from a validated set of 44 chronic conditions. We used logistic regression to assess associations between characteristics and multimorbidity. Results The sample included 154 391 RA patients and 154 391 non-RA comparators. For people aged 18–50 years, RA women (vs RA men) had 7.5 and 4.4 (vs 3.2 and 0.9 in non-RA women vs non-RA men) percentage point increases for MM2+ and MM5+, respectively. For people aged 51+ years, RA women (vs RA men) had 2.1 and 2.5 (vs 1.2 and 0.3 in non-RA women vs non-RA men) percentage point increases for MM2+ and MM5+, respectively. Interactions revealed that differences in multimorbidity between women and men were exacerbated by RA (vs non-RA) (P &lt; 0.05), with more pronounced effects in people aged 18–50. Men had more cardiovascular-related conditions, whereas RA women had more psychological, neurological and general musculoskeletal conditions. Other comorbidities varied by sex and age. Conclusion Multimorbidity disproportionately impacts women with RA. Research, clinical and policy agendas for rheumatic diseases should acknowledge and support the variation in care needs by sex and gender across the lifespan.

ObjectiveTo identify differences in multimorbidity and individual comorbidities among individuals with rheumatoid arthritis (RA), separated by race and ethnicity.MethodsThis case–control study within OptumLabs Data Warehouse from 2010 to 2019 matched RA cases (defined by 2 codes plus prescription of an RA drug) to non‐RA controls 1:1 on age, sex, race and ethnicity, region, index date of RA, and insurance coverage duration. We defined multimorbidity as the presence of ≥2 or ≥5 validated comorbidities. Logistic regression models calculated adjusted odds of multimorbidity with 95% confidence intervals (95% CIs) within each race and ethnicity.ResultsWe identified 154,391 RA cases and 154,391 controls (mean age 59.6, 76% female). Black enrollees had the most multimorbidity ≥2/≥5 (73.1%, 34.3%); Asian enrollees had the least (52.4%, 17.3%). Adjusted odds of multimorbidity ≥2 and ≥5 in RA cases versus controls was 2.19 (95% CI 2.16–2.23) and 2.06 (95% CI 2.02–2.09), respectively. This increase was similar across race and ethnicity. However, we observed elevated occurrence of certain comorbidities by race and ethnicity versus controls (P < 0.001), including renal disease in White enrollees (4.7% versus 3.2%) and valvular heart disease in Black and White enrollees (3.2% and 2.8% versus 2.6% and 2.2%).ConclusionMultimorbidity is a problem for all RA patients. Targeted identification of certain comorbidities by race and ethnicity may be a helpful approach to mitigate multimorbidity.

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.

ObjectiveTo identify the incidence, risk factors, and outcomes of rheumatoid arthritis–associated interstitial lung disease (RA‐ILD) and to assess time trends in the incidence and mortality in RA‐ILD.MethodsWe included adult residents of Olmsted County, Minnesota with incident RA between 1999 and 2014. Subjects were followed until death, emigration, or April 30, 2019. ILD was defined as the presence of a radiologist‐defined pattern consistent with ILD on chest computed tomography (CT). When chest CT was absent, the combination of chest radiograph abnormalities compatible with ILD and restrictive pattern on pulmonary function testing was considered consistent with ILD. Potential risk factors included age, sex, smoking, obesity, seropositivity, extraarticular manifestations (EAMs), and medications. For survival analysis, we matched RA‐ILD patients to RA–non‐ILD comparators. The frequency and mortality from clinician‐diagnosed RA‐ILD from 1999 to 2014 was compared against a cohort from 1955 to 1994.ResultsDuring the 1999–2014 time period, 645 individuals (70% women) had incident RA, were a median age of 55.3 years, and 53% never smoked. Twenty‐two patients had ILD before RA, and 51 (67% women) developed ILD during follow‐up. The 20‐year cumulative incidence of RA‐ILD was 15.3%. Ever‐smoking (hazard ratio [HR] 1.92), age at RA onset (HR 1.89 per 10‐year increase), and severe EAMs (HR 2.29) were associated with incident RA‐ILD. The RA‐ILD cases had higher mortality than their matched RA comparators (HR 2.42). Incidence of RA‐ILD was non‐significantly lower from 1999 to 2014 than from 1955 to1994, but mortality was improved.ConclusionsRA‐ILD occurs in nearly 1 in 6 patients with RA within 20 years and is associated with shorter survival. Lack of significant change in RA‐ILD incidence over 6 decades deserves further investigation.

Objective To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). Methods Demographic, clinical, and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n = 336) and validation cohort (n = 307) were used. The genomic data comprised 160 single‐nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European Alliance of Associations for Rheumatology (EULAR) response criteria at the 3‐month follow‐up. Supervised ML methods were trained with 5 repeats and 10‐fold cross‐validation using the training cohort. Prediction performance was validated in the independent validation cohort. Results Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score in 28 joints (DAS28) scores and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 ( P = 0.05) in the training cohort and achieved a prediction accuracy of 76% ( P = 0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 scores were among the top predictors of methotrexate response. Conclusion Pharmacogenomic biomarkers combined with baseline DAS28 scores can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies.