Myasoedova, Elena

Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.

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.

ObjectiveTreat‐to‐target (T2T) and shared decision‐making are valued features of current guidelines for rheumatoid arthritis (RA) management. Although T2T has demonstrated value for improving RA outcomes, implementation remains inconsistent and lacks standardization and procedures for including patient input. We sought to better understand the impact of shared decisions on T2T and how treatment goal discussions between patients and providers impact RA treatment improvement and satisfaction.MethodsAn anonymous, web‐based questionnaire was presented to United States residents aged 18 years or older with a self‐reported diagnosis of RA by a medical professional with 28 questions regarding socio‐demographics, RA disease activity (DA), diagnosis, treatments, outcomes, and goals. Analyses included descriptive statistics with χ2 and rank sum tests for comparisons.ResultsThe questionnaire was completed by 907 people (mean age of 58 years; mean 11 years since diagnosis; 90% female). The majority (571; 63%) did not discuss RA treatment goals with providers. Patients engaging in treatment goal discussions with their providers were three times more likely to be satisfied with their treatment plans. Patients discussing treatment goals with their providers were more likely to have improved DA levels and 68% more likely to reach remission.ConclusionA majority of patients with RA report having no treatment goal discussion with their providers; however, these discussions are associated with greater DA improvement and treatment satisfaction. Further research should seek understanding of how shared treatment goal discussions relate to successful RA management and explore the development of practical tools to implement them in regular clinic practice as part of a T2T regimen.

Aim The objective was to examine the prevalence of atherosclerotic cardiovascular disease (ASCVD) and its risk factors among patients with RA with diabetes mellitus (RA-DM) and patients with RA without diabetes mellitus (RAwoDM), and to evaluate lipid and blood pressure (BP) goal attainment in RA-DM and RAwoDM in primary and secondary prevention. Methods The cohort was derived from the Survey of Cardiovascular Disease Risk Factors in Patients with Rheumatoid Arthritis from 53 centres/19 countries/3 continents during 2014–2019. We evaluated the prevalence of cardiovascular disease (CVD) among RA-DM and RAwoDM. The study population was divided into those with and without ASCVD, and within these groups we compared risk factors and CVD preventive treatment between RA-DM and RAwoDM. Results The study population comprised of 10 543 patients with RA, of whom 1381 (13%) had DM. ASCVD was present in 26.7% in RA-DM compared with 11.6% RAwoDM (p<0.001). The proportion of patients with a diagnosis of hypertension, hyperlipidaemia and use of lipid-lowering or antihypertensive agents was higher among RA-DM than RAwoDM (p<0.001 for all). The majority of patients with ASCVD did not reach the lipid goal of low-density lipoprotein cholesterol <1.8 mmol/L. The lipid goal attainment was statistically and clinically significantly higher in RA-DM compared with RAwoDM both for patients with and without ASCVD. The systolic BP target of <140 mm Hg was reached by the majority of patients, and there were no statistically nor clinically significant differences in attainment of BP targets between RA-DM and RAwoDM. Conclusion CVD preventive medication use and prevalence of ASCVD were higher in RA-DM than in RAwoDM, and lipid goals were also more frequently obtained in RA-DM. Lessons may be learnt from CVD prevention programmes in DM to clinically benefit patients with RA .

Abstract Aims To assess differences in estimated cardiovascular disease (CVD) risk among rheumatoid arthritis (RA) patients from different world regions and to evaluate the management and goal attainment of lipids and blood pressure (BP). Methods and results The survey of CVD risk factors in patients with RA was conducted in 14 503 patients from 19 countries during 2014–19. The treatment goal for BP was &lt;140/90 mmHg. CVD risk prediction and lipid goals were according to the 2016 European guidelines. Overall, 21% had a very high estimated risk of CVD, ranging from 5% in Mexico, 15% in Asia, 19% in Northern Europe, to 31% in Central and Eastern Europe and 30% in North America. Of the 52% with indication for lipid-lowering treatment (LLT), 44% were using LLT. The lipid goal attainment was 45% and 18% in the high and very high risk groups, respectively. Use of statins in monotherapy was 24%, while 1% used statins in combination with other LLT. Sixty-two per cent had hypertension and approximately half of these patients were at BP goal. The majority of the patients used antihypertensive treatment in monotherapy (24%), while 10% and 5% as a two- or three-drug combination. Conclusion We revealed considerable geographical differences in estimated CVD risk and preventive treatment. Low goal attainment for LLT was observed, and only half the patients obtained BP goal. Despite a high focus on the increased CVD risk in RA patients over the last decade, there is still substantial potential for improvement in CVD preventive measures.

Objective To investigate the effect of therapies on radiographic progression in patients with axial spondyloarthritis (SpA). Methods A comprehensive database search for studies assessing radiographic progression in axial SpA (particular treatment versus no treatment of interest) was performed. Study‐specific standardized mean differences in treatment outcomes at 2 and ≥4 years were estimated and combined using random‐effects models. Results Twenty‐four studies in patients with axial SpA were identified, of which 18 involved tumor necrosis factor inhibitors ( TNF i), 8 involved nonsteroidal antiinflammatory drugs ( NSAID s), and 1 involved secukinumab. Spinal radiographic progression, as measured by the modified Stoke Ankylosing Spondylitis Spine Score ( mSASSS ), was not significantly different between TNF i‐treated and biologics‐naive patients at 2 years ( mSASSS difference −0.73 [95% confidence interval (95% CI ) −1.52, 0.12], I 2 = 28%) and ≥4 years ( mSASSS difference −2.03 [95% CI −4.63, 0.72], I 2 = 63%). Sensitivity analyses restricted to studies with a low risk of bias showed a significant difference in spinal radiographic progression between TNF i‐treated and biologics‐naive patients at ≥4 years ( mSASSS difference −2.17 [95% CI −4.19, −0.15]). No significant difference in spinal radiographic progression was observed between NSAID ‐treated and control patients ( mSASSS difference −0.30 [95% CI −2.62, 1.31], I 2 = 71%) or between secukinumab‐treated and biologics‐naive patients ( mSASSS difference −0.34 [95% CI −0.85, 0.17]). With regard to treatment differences in patients with nonradiographic axial SpA or in patients with radiographic progression measured using the sacroiliac joint score, an insufficient number of studies were available for analysis. Conclusion Although no significant protective effect of TNF i treatment on spinal radiographic progression was seen over the course of 2 years or ≥4 years in patients with axial SpA, our analysis restricted to studies with a low risk of bias showed a protective effect of TNF i after ≥4 years. Therefore, long‐term TNF i exposure might confer beneficial effects on spinal radiographic progression in axial SpA. No difference in radiographic progression at 2 years was seen in either the NSAID or secukinumab treatment groups compared to their controls. Future studies should explore the effects of biologic treatment on radiographic progression, as well as the effects of long‐term biologics exposure, in patients with early axial SpA or those with nonradiographic axial SpA.