Search

Abstract Background Treatments for rheumatoid arthritis (RA) are associated with complex changes in lipids and lipoproteins that may impact cardiovascular (CV) risk. The objective of this study was to examine lipid and lipoprotein changes associated with two common RA treatment strategies, triple therapy or tumor necrosis factor inhibitor (TNFi), and association with CV risk. Methods In this secondary data analysis of the TARGET trial, methotrexate (MTX) inadequate responders with RA were randomized to either add sulfasalazine and hydroxychloroquine (triple therapy), or TNFi for 24-weeks. The primary trial outcome was the change in arterial inflammation measured in the carotid arteries or aorta by FDG-PET/CT at baseline and 24-weeks; this change was described as the target-to-background ratio (TBR) in the most diseased segment (MDS). Routine lipids and advanced lipoproteins were measured at baseline and 24-weeks; subjects on statin therapy at baseline were excluded. Comparisons between baseline and follow-up lipid measurements were performed within and across treatment arms, as well as change in lipids and change in MDS-TBR. Results We studied 122 participants, 61 in each treatment arm, with median age 57 years, 76% female, and 1.5 year median RA disease duration. When comparing treatment arms, triple therapy had on average a larger reduction in triglycerides (15.9 mg/dL, p = 0.01), total cholesterol to HDL-C ratio (0.29, p-value = 0.01), and LDL particle number (111.2, p = 0.02) compared to TNFi. TNFi had on average a larger increase in HDL particle number (1.6umol/L, p = 0.006). We observed no correlation between change in lipid measurements and change in MDS-TBR within and across treatment arms. Conclusions Both treatment strategies were associated with improved lipid profiles via changes in different lipids and lipoproteins. These effects had no correlation with change in CV risk as measured by vascular inflammation by FDG-PET/CT. Trial registration ClinicalTrials.gov ID NCT02374021.

Objective Developing and evaluating new treatment guidelines for rheumatoid arthritis (RA) based on observational data requires a quantitative understanding of patterns in current treatment practice with biologic and targeted synthetic disease‐modifying antirheumatic drugs (b/tsDMARDs). Methods We used data from the CorEvitas RA registry to study patients starting their first b/tsDMARD therapy, defined as the first line of therapy, between 2012 and the end of 2021. We identified treatment patterns as unique sequences of therapy changes following and including the first‐line therapy. Therapy cycling was defined as switching back to a treatment from a previously used therapeutic class. Results A total of 6015 b/tsDMARD‐naïve patients (77% female) were included in the analysis. Their median age was 58 years, and their median disease duration was 3 years. In 2012–2014, 80% of the patients started a tumor necrosis factor inhibitor (TNFi) as their first b/tsDMARD. However, the use of TNFi decreased in favor of Janus kinase inhibitors since 2015. Although the number of treatment patterns was large, therapy cycling was relatively common. For example, 601 patterns were observed among 1133 patients who changed therapy at least four times, of whom 85.3% experienced therapy cycling. Furthermore, the duration of each of the first three lines of therapy decreased over the past decade. For example, the median duration of the first‐line therapy was 153 days in 2018–2021 compared to 208 days in 2015–2017 (P

Background Coronary microvascular dysfunction as measured by myocardial flow reserve (MFR) is associated with increased cardiovascular risk in rheumatoid arthritis (RA). The objective of this study was to determine the association between reducing inflammation with MFR and other measures of cardiovascular risk. Methods and Results Patients with RA with active disease about to initiate a tumor necrosis factor inhibitor were enrolled (NCT02714881). All subjects underwent a cardiac perfusion positron emission tomography scan to quantify MFR at baseline before tumor necrosis factor inhibitor initiation, and after tumor necrosis factor inhibitor initiation at 24 weeks. MFR

Background Cardiovascular disease remains an important comorbidity in patients with rheumatoid arthritis (RA), but traditional models do not accurately predict cardiovascular risk in patients with RA. The addition of biomarkers could improve prediction. Methods and Results The TARGET (Treatments Against RA and Effect on FDG PET/CT) trial assessed whether different treatment strategies in RA differentially impact cardiovascular risk as measured by the change in arterial inflammation on arterial target to background ratio on fluorodeoxyglucose positron emission tomography/computed tomography scans conducted 24 weeks apart. A group of 24 candidate biomarkers supported by prior literature was assessed at baseline and 24 weeks later. Longitudinal analyses examined the association between baseline biomarker values, measured in plasma EDTA, and the change in arterial inflammation target to background ratio. Model fit was assessed for the candidate biomarkers only, clinical variables only, and models combining both. One hundred nine patients with median (interquartile range) age 58 years (53–65 years), RA duration 1.4 years (0.5–6.6 years), and 82% women had biomarkers assessed at baseline and follow‐up. Because the main trial analyses demonstrated significant target to background ratio decreases with both treatment strategies but no difference across treatment groups, we analyzed all patients together. Baseline values of serum amyloid A, C‐reactive protein, soluble tumor necrosis factor receptor 1, adiponectin, YKL‐40, and osteoprotegerin were associated with significant change in target to background ratio. When selected candidate biomarkers were added to the clinical variables, the adjusted R2 improved from 0.20 to 0.33 (likelihood ratio P=0.0005). Conclusions A candidate biomarker approach identified several promising biomarkers that associate with baseline and treatment‐associated changes in arterial inflammation in patients with RA. These will now be tested in an external validation cohort.

Abstract Background Comorbid conditions are very common in rheumatoid arthritis (RA) and several prior studies have clustered them using machine learning (ML). We applied various ML algorithms to compare the clusters of comorbidities derived and to assess the value of the clusters for predicting future clinical outcomes. Methods A large US-based RA registry, CorEvitas, was used to identify patients for the analysis. We assessed the presence of 24 comorbidities, and ML was used to derive clusters of patients with given comorbidities. K-mode, K-mean, regression-based, and hierarchical clustering were used. To assess the value of these clusters, we compared clusters across different ML algorithms in clinical outcome models predicting clinical disease activity index (CDAI) and health assessment questionnaire (HAQ-DI). We used data from the first 3 years of the 6-year study period to derive clusters and assess time-averaged values for CDAI and HAQ-DI during the latter 3 years. Model fit was assessed via adjusted R 2 and root mean square error for a series of models that included clusters from ML clustering and each of the 24 comorbidities separately. Results 11,883 patients with RA were included who had longitudinal data over 6 years. At baseline, patients were on average 59 (SD 12) years of age, 77% were women, CDAI was 11.3 (SD 11.9, moderate disease activity), HAQ-DI was 0.32 (SD 0.42), and disease duration was 10.8 (SD 9.9) years. During the 6 years of follow-up, the percentage of patients with various comorbidities increased. Using five clusters produced by each of the ML algorithms, multivariable regression models with time-averaged CDAI as an outcome found that the ML-derived comorbidity clusters produced similarly strong models as models with each of the 24 separate comorbidities entered individually. The same patterns were observed for HAQ-DI. Conclusions Clustering comorbidities using ML algorithms is not computationally complex but often results in clusters that are difficult to interpret from a clinical standpoint. While ML clustering is useful for modeling multi-omics, using clusters to predict clinical outcomes produces models with a similar fit as those with individual comorbidities.

Objective Many patients with rheumatoid arthritis (RA) have difficulty finding clinicians to treat them because of workforce shortages. We developed an app to address this problem by improving care efficiency. The app collects patient‐reported outcomes (PROs) and can be used to inform visit timing, potentially reducing the volume of low‐value visits. We describe the development process, intervention design, and planned study for testing the app. Methods We employed user‐centered design, interviewing patients and clinicians, to develop the app. To improve visit efficiency, symptom tracking logic alerts clinicians to PRO trends: worsening PROs generate alerts suggesting an earlier visit, and stable or improving PROs generate notifications that scheduled visits could be delayed. An interrupted time‐series analysis with a nonrandomized control population will allow assessment of the impact of the app on visit frequency. Results Patient interviews identified several of the following needs for effective app and intervention design: the importance of a simple user interface facilitating rapid answering of PROs, the availability of condensed summary information with links to more in‐depth answers to common questions regarding RA, and the need for clinicians to discuss the PRO data during visits with patients. Clinician interviews identified the following user needs: PRO data must be easy to view and use during the clinical workflow, and there should be reduced interval visits when PROs are trending worse. Some clinicians believed visits could be delayed for patients with stable PROs, whereas others raised concerns. Conclusion PRO apps may improve care efficiency in rheumatology. Formal evaluation of an integrated PRO RA app is forthcoming.

Objective It is unknown how the relationship between disease activity in rheumatoid arthritis (RA) and cardiovascular (CV) events may change over time. We examined the potentially time‐varying association of RA disease activity to CV events. Methods We used the CorEvitas prevalent RA registry. The Clinical Disease Activity Index (CDAI) score category, averaged within each 6‐month window since enrollment, was the exposure, and the outcome was major adverse CV events (MACEs). We used marginal structural models to estimate the hazard ratio (HR), comparing each CDAI score category with remission, allowing for differential association over time. We predicted MACE‐free survival under several CDAI score scenarios. Results We found 44,816 eligible patients (77% female; mean age 58 years) with a crude event rate of 5.3/1000 person‐years (median follow‐up 3.4 years). The strongest association between higher CDAI score and MACEs was observed during the first 6 months of enrollment (HR for CDAI score low 2.29 [95% CI: 1.21‐4.36], moderate 2.81 [95% CI: 1.46‐5.43], and high 2.99 [95% CI: 1.48‐6.02]). These estimates gradually diminished; by year 5, the HRs were 1.00 (95% CI: 0.49‐2.05) for low, 1.18 (95% CI: 0.51‐2.71) for moderate, and 1.04 (95% CI: 0.45‐2.40) for high CDAI score. Predicted MACE‐free survival suggested a potential decrease in MACEs with a hypothetical earlier transition to remission. Conclusion The association of higher disease activity with CV events may be stronger earlier in the disease course of RA. Interventional studies may be warranted to precisely determine the effect of disease activity suppression on CV events in RA.

Abstract Background Observational data are increasingly being used to conduct external comparisons to clinical trials. In this study, we empirically examined whether different methodological approaches to longitudinal missing data affected study conclusions in this setting. Methods We used data from one clinical trial and one prospective observational study, both Norwegian multicenter studies including patients with recently diagnosed rheumatoid arthritis and implementing similar treatment strategies, but with different stringency. A binary disease remission status was defined at 6, 12, and 24 months in both studies. After identifying patterns of longitudinal missing outcome data, we evaluated the following five approaches to handle missingness: analyses of patients with complete follow-up data, multiple imputation (MI), inverse probability of censoring weighting (IPCW), and two combinations of MI and IPCW. Results We found a complex non-monotone missing data pattern in the observational study (N = 328), while missing data in the trial (N = 188) was monotone due to drop-out. In the observational study, only 39.0% of patients had complete outcome data, compared to 89.9% in the trial. All approaches to missing data indicated favorable outcomes of the treatment strategy in the trial and resulted in similar study conclusions. Variations in results across approaches were mainly due to variations in estimated outcomes for the observational data. Conclusions Five different approaches to handle longitudinal missing data resulted in similar conclusions in our example. However, the extent and complexity of missing observational data affected estimated comparative outcomes across approaches, highlighting the need for careful consideration of methods to account for missingness in this setting. Based on this empirical examination, we recommend using a prespecified advanced missing data approach to account for longitudinal missing data, and to conduct alternative approaches in sensitivity analyses.

Objective Vaccination against preventable infections is widely recommended for patients with systemic rheumatic disease. The coronavirus disease 2019 (COVID‐19) pandemic has highlighted variability in attitudes toward vaccination, particularly with the use of novel vaccine platforms. We studied attitudes toward vaccination against COVID‐19 and other preventable infections among patients with systemic rheumatic disease and compared these against the general population. Methods We surveyed patients treated at Brigham and Women’s Hospital for systemic rheumatic disease using a secure web‐based survey or paper survey in English or Spanish, from December 2020 to April 2021. We included survey questions used in the nationwide Harris Poll (October 2020 and February 2021), allowing the comparison of responses with those from the general population. Response frequencies were estimated and compared using descriptive statistics. Results Of 243 participants (25% response rate), the mean age was 56 years, 82% were women, and 33% were nonwhite. Rheumatoid arthritis (50%) and systemic lupus erythematosus (28%) were the most common diagnoses. Thirty percent had been hospitalized previously for any infection. Seventy‐six percent worried a lot or somewhat about contracting COVID‐19. Attitudes toward vaccination were very favorable, with 92% having received a flu shot in the past year and 84% desiring a COVID‐19 vaccine as soon as possible compared with 30% to 40% of Harris Poll respondents (P < 0.001). Physician recommendation to receive a vaccine and desire to avoid infection were the most common reasons for desiring vaccinations. Conclusion Vaccine acceptability, including toward COVID‐19 vaccines, was high among this population of patients with systemic rheumatic disease seen at an academic medical center cohort. Physician recommendation is a key factor for vaccine uptake.

Individuals with rheumatoid arthritis (RA) are at increased risk for atherosclerotic cardiovascular disease (ASCVD) events relative to the general population, potentially mediated by atherosclerotic plaques that are more inflamed and rupture prone. We sought to address whether RA immunomodulators reduce vascular inflammation, thereby reducing ASCVD risk, and whether such reduction depends on the type of immunomodulator. The TARGET (Treatments Against RA and Effect on 18‐Fluorodeoxyglucose [18F‐FDG] Positron Emission Tomography [PET]/Computed Tomography [CT]) trial (NCT02374021) will enroll 150 patients with RA with active disease and an inadequate response to methotrexate. Participants will be randomized to add either a tumor necrosis factor (TNF) inhibitor (etanercept or adalimumab) or sulfasalazine and hydroxychloroquine to their background methotrexate. Participants will undergo full‐body 18F‐FDG–labelled PET scanning at baseline and after 6 months. Efficacy and safety evaluations will occur every 6 weeks, with therapy modified in a treat‐to‐target approach. The primary outcome is the comparison of change in arterial inflammation in the wall of the aorta and carotid arteries between the randomized treatment groups, specifically, the change in the mean of the maximum target‐to‐background ratio of arterial 18F‐FDG uptake in the most diseased segment of either the aorta and carotid arteries. A secondary analysis will compare the effects of achieving low disease activity or remission with those of moderate to high disease activity on vascular inflammation. The TARGET trial will test, for the first time, whether RA treatments reduce arterial inflammation and whether such reduction differs according to treatment strategy with either TNF inhibitors or a combination of nonbiologic disease‐modifying antirheumatic drugs.

Objective Disagreement exists between rheumatology and primary care societies regarding gout management. This paper describes a formal process for gathering input from stakeholders in the planning of a trial to compare gout management strategies. Methods We recruited patients, nurses, physician assistants, primary care clinicians, and rheumatologists to participate in a modified Delphi panel (mDP) to provide input on design of a trial focused on optimal management for primary care patients with gout. The 16 panelists received a plain‐language briefing document that discussed the rationale for the trial, key clinical issues in gout, and aspects of trial design. The panelists also received information and considerations on nine voting questions (VQs), judged to be the key design questions. Cognitive interviews with panelists ensured that the VQs were understood by the range of panelists involved in the mDP. Panelists were asked to score all VQs from 1 (definitely no) to 9 (definitely yes). Two voting rounds were conducted—round 1 by email and round 2 by video conference. Results The VQs were modified through the cognitive interviews. The round 1 voting resulted in consensus on eight items, with consensus defined as median voting score in the same tercile (1‐3, 4‐6 or 7‐9). Re‐voting at the meeting (round 2) reached consensus on the remaining item. Conclusion An mDP with various stakeholders facilitated consensus on the design of a trial of different management strategies for chronic gout. This method may be useful for designing trials of clinical questions with substantial disagreement across stakeholders.

Abstract Background There are numerous non-biologic and biologic disease-modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice. Methods We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every 6 months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use. Results 7300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. Eighty-two percent of TCZm use began within 3 years of starting any bDMARD. Ninety-three percent of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with the use of TCZm included prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor. Conclusions Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data.

Objective Low‐dose methotrexate (LD‐MTX), a cornerstone in the treatment of rheumatoid arthritis, is associated with a moderately increased risk of anemia, leukopenia, and skin cancers, but the risks of myelosuppression and malignancy during LD‐MTX use remain incompletely described. We examined the risks of cytopenias and skin cancers among patients taking LD‐MTX versus placebo in a large randomized controlled trial (RCT). Methods We prespecified secondary analyses of a double‐blind, placebo‐controlled RCT that included adults with known cardiovascular disease and diabetes or metabolic syndrome in the United States and Canada. Subjects were randomly allocated to LD‐MTX (20 mg/week maximum) or placebo. All subjects received folic acid (1 mg daily for 6days/week). We assessed the frequency of blindly adjudicated hematologic and malignant adverse events (AEs). Results A total of 2391 subjects were randomized to LD‐MTX (mean dosage 14.9 mg/week), and 2395 were randomized to placebo. During follow‐up, in the LD‐MTX arm, simultaneous two‐line cytopenias (n = 92 [3.9%]) or pancytopenia (n = 13 [0.54%]) were infrequent. Pancytopenia developed as soon as 4 months and as late as 3.5 years after beginning LD‐MTX, though the latter subject had been recently diagnosed with multiple myeloma. Overall skin cancer risk was increased in users of LD‐MTX compared with users of placebo, which driven largely by a statistically significant increased risk of squamous cell skin cancer (hazard ratio [HR] 3.31; 95% confidence interval [CI] 1.63‐6.71). Melanoma was increased in LD‐MTX, but this was not statistically significant (HR 2.33; 95% CI 0.60‐9.01). Conclusions Among subjects using LD‐MTX, simultaneous two‐line cytopenias and pancytopenia were uncommon. We found more cases of skin cancer, particularly squamous cell carcinomas, in the LD‐MTX arm than the placebo arm.

Objective We previously derived and validated a risk score for major nonsteroidal anti‐inflammatory drug (NSAID) toxicity over 1 year among NSAID users in a randomized controlled trial. This work was extended to examine the risk score's performance in an external population using real‐world data. Methods Patients enrolled in the Corrona Rheumatoid Arthritis (RA) Registry were included if they initiated use of an NSAID. We defined the original risk factors previously identified in the risk score for major NSAID toxicity: age; male sex; history of cardiovascular disease, hypertension, and diabetes; tobacco use; statin use; elevated serum creatinine and hematocrit values; and RA. Additionally, we defined the occurrence of major toxicity, including major adverse cardiovascular events, acute kidney injury, significant gastrointestinal events, and mortality. The original risk factors were assessed in Cox regression examining discrimination and calibration. Low (less than 1%), intermediate (1%‐4%), and high (more than 4%) risk categories for 1‐year risk were applied to the population. Results A total of 5231 patients from Corrona who had a new NSAID exposure period were included. The original risk score model showed good discrimination (C‐index 0.70). Not all of the original variables were statistically significant in real‐world data. Using the original risk score weights, 1363 (26.1%) patients had predicted risk of less than 1%, 3571 (68.3%) had predicted risk of 1% to 4%, and 297 (5.7%) had predicted risk of more than 4%. Conclusion The original NSAID major toxicity risk score demonstrated good model fit characteristics in this external real‐world cohort. These results suggest that such a risk score is valid in typical practice and could be considered for clinical care.

Objective Most patients with rheumatoid arthritis (RA) strive to consolidate their treatment from methotrexate combinations. The objective of this analysis was to identify patients with RA most likely to achieve remission with tocilizumab (TCZ) monotherapy by developing and validating a prediction model and associated remission score. Methods We identified four TCZ monotherapy randomized controlled trials in RA and chose two for derivation and two for internal validation. Remission was defined as a Clinical Disease Activity Index score less than 2.8 at 24 weeks post randomization. We used logistic regression to assess the association between each predictor and remission. After selecting variables and assessing model performance in the derivation data set, we assessed model performance in the validation data set. The cohorts were combined to calculate a remission prediction score. Results The variables selected included younger age, male sex, lower baseline Clinical Disease Activity Index score, shorter RA disease duration, region of the world (Europe and South America [increased odds of remission] versus Asia and North America), no previous exposure to disease‐modifying antirheumatic drugs and/or methotrexate, lower baseline Health Assessment Questionnaire Disability Index score, and baseline hematocrit. The area under the receiver operating characteristic curve was 0.739 in the derivation data set and 0.756 in the validation data set. Patients were categorized into three remission prediction categories based on the remission prediction score: 40% in the low (less than 10% probability of remission), 45% in the intermediate (10%‐25% probability), and 15% in the moderate remission prediction category (greater than 25% probability). Conclusion We used easily accessible factors to develop a remission prediction score to predict RA remission at 24 weeks after initializing TCZ monotherapy. These results may provide guidance to clinicians tailoring treatment options based on clinical characteristics.

Objective Previous attempts to estimate rheumatoid arthritis (RA) disease activity using claims data only did not yield high performance. We aimed to assess whether supplementing claims data with readily available electronic medical record (EMR) data might result in improvement. Methods We used a subset of the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) that had linked Medicare claims. The disease activity score in 28 joints with C‐reactive protein (DAS28‐CRP) was considered the gold standard of measure. Variables in the linked Medicare claims, as well as EMR recorded in the preceding one‐year period were used as potential explanatory variables. We constructed three models: “Claims‐Only,” “Claims + Medications,” and “Claims + Medications + Labs (laboratory data from EMR). We selected variables via adaptive LASSO. Model performance was measured with adjusted R2 for continuous DAS28‐CRP and C‐statistics for binary category classification (high/moderate vs low disease activity/remission). Results We identified 300 patients with laboratory data and linked Medicare claims. The mean age was 68 years and 80% were female. The mean (SD) DAS28‐CRP was 3.6 (1.6) and 51% had high or moderate DAS28‐CRP. For the continuous estimation, the adjusted R2 was 0.02 for Claims‐Only, 0.09 for Claims + Medications, and 0.18 for Claims + Medications + Labs. The C‐statistics for discriminating the binary categories were 0.61 for Claims‐Only, 0.68 for Claims + Medications, and 0.76 for Claims + Medications + Labs. Conclusion Adding EMR‐derived variables to claims‐derived variables resulted in modest improvement. Even with EMR variables, we were unable to estimate continuous DAS28‐CRP satisfactorily. However, in claims‐EMR models, we were able to discriminate between binary categories of disease activity with reasonable accuracy.

Abstract Background The impact of smoking on TNF inhibition (TNFi) therapy is unclear. We examined the effect of smoking on all-cause and cause-specific TNFi discontinuation in axial spondyloarthritis (axSpA). Methods We used longitudinal data from the British Society for Rheumatology Biologics Register for Ankylosing Spondylitis (BSRBR-AS). Patients fulfilling the ASAS criteria for axSpA, who started their first TNFi, were eligible for analysis. Inverse-probability weights were used to balance differences in baseline disease severity and other confounders. We used marginal structural Cox proportional hazard models to estimate hazard ratios (HR) for TNFi discontinuation according to smoking status. In analyses of cause-specific discontinuation, competing risk events were considered as censoring, using inverse-probability weights. Results A total of 758 participants were included in the analysis (66% male, mean age 45 years), providing 954 patient-years of follow-up. TNFi was discontinued in 174 (23%) patients, among whom 26% stopped due to infections, 20% due to other adverse events and 44% due to inefficacy or other reasons. Thirty-four percent were current smokers and 30% ex-smokers. Compared to never smokers, current smokers’ risk of TNFi discontinuation was HR 0.79 (95%CI 0.53 to 1.20) and ex-smokers HR 0.68 (95%CI 0.45 to 1.04). Our data did not show evidence that current smoking influenced discontinuation due to infections (HR 0.79, 95%CI 0.40 to 1.54), other adverse events (HR 0.86, 95%CI 0.41 to 1.78) or inefficacy/other causes (HR 1.44, 95%CI 0.86 to 2.41). Conclusion Baseline smoking status did not impact TNFi discontinuation in this UK cohort of axSpA participants.

Abstract Background Patients with metabolic syndrome (MetS) are at increased risk of asymptomatic hyperuricemia (i.e., elevated serum uric acid (SUA) level without gout) and cardiovascular disease. We conducted a cross-sectional study to examine associations between SUA levels and coronary flow reserve and urate deposits in carotid arteries in patients with asymptomatic hyperuricemia and MetS. Methods Adults aged ≥40 years with MetS and SUA levels ≥6.5 mg/dl, but no gout, were eligible. Using a stress myocardial perfusion positron emission tomography (PET), we assessed myocardial blood flow (MBF) at rest and stress and calculated coronary flow reserve (CFR). CFR

Abstract Background The relationship between inflammation and blood pressure (BP) has been studied mainly in the general population. In this study, we examined the association between inflammation and BP across a broader range of inflammation observed in rheumatoid arthritis (RA) and non-RA outpatients. Methods We studied subjects from a tertiary care outpatient population with C-reactive protein (CRP) and BP measured on the same date in 2009–2010; RA outpatients were identified using a validated algorithm. General population data were obtained from the National Health and Nutrition Examination Survey (NHANES) as comparison. To study the cross-sectional association between CRP and BP in the three groups, we constructed a generalized additive model. Longitudinal association between CRP and BP was examined using a repeated-measures linear mixed-effects model in RA outpatients with significant change in inflammation at two consecutive time points. Results We studied 24,325 subjects from the outpatient population, of whom 1811 had RA, and 5561 were from NHANES. In RA outpatients, we observed a positive relationship between CRP and systolic BP (SBP) at CRP

Abstract Background Patient registry data serves an increasing role in drug safety and comparative effectiveness research, but registry databases often do not contain confounder information measured at the same time that treatments begin. This study evaluated a set of approaches for estimating confounder values at treatment initiation using actual data from a rheumatoid arthritis (RA) registry to examine the risk of infection associated with different biologic DMARDs (bDMARDs). Methods We examined the risk of infection associated with starting a TNF inhibitor (TNFi) versus any of the other non-TNFi bDMARDs. Different confounder assessment approaches were tested. All approaches were tested in Cox proportional hazard regression models that used a propensity score (PS). The confounder of interest was the disease activity score (DAS28-CRP). The confounder assessment approaches utilized different temporal relationships between the DAS28-CRP measurement and the start of the treatments of interest. Results We included 219 subjects with RA with 269 initiations of either a TNFi or a different bDMARD or both. Among this group, 305 infections were reported and confirmed through chart review. The hazard ratio (HR) for the risk of infection associated with use of a non-TNFi bDMARD ranged from 1.17 to 3.03 using 13 different approaches; only the approach with the highest HR produced results significantly different than one, but this approach included the fewest subjects and infections. Conclusions The relative risk of infection for TNFi and other non-TNFi bDMARDs was similar using various approaches regarding which DAS28-CRP score should be used as the baseline measure in adjusted analyses.

Abstract Background The collection of routine clinical data in the setting of research registries can serve an important role in understanding real world care. However, relatively little is known about the patient experience in registries, motivating us to survey patients enrolled in two chronic disease registries. Methods We conducted similar surveys in two disease-based registries based at one academic medical center in the US. One group of patients with rheumatoid arthritis (RA) had been enrolled in a registry, and we focused on retention factors. In a second group of patients with inflammatory bowel disease (IBD) recently enrolled or considering enrollment, we examined factors that would influence their enrollment and willingness to answer frequent questionnaires and give biospecimens. The surveys were analyzed using descriptive statistics and the two cohorts were compared using nonparametric and chi-square tests. Results We received 150 (50%) completed surveys from RA and 169 (63%) from IBD patients. Mean age of subjects was 62 years in RA and 43 in IBD with more women respondents with RA (83%) than IBD (62%). The two groups described very similar factors as the top three motivations for participation: desire to help others, desire to improve care of own disease, and ease of volunteering. Preferred methods of surveying included mail, e-mail, but telephone was not favored; age was an important correlate of this preference. Respondents preferred surveys either every 1–3 months (28.7% RA and 55.0% IBD) or every 4–6 months (50.7% RA and 29.0% IBD). They differed in the preference for payment for answering surveys with 68.0% with RA answering that no payment was necessary but only 36.1% with IBD felt similarly. Conclusions Patients engaged in clinical registries demonstrate a high level of commitment to improve care and many report a willingness to answer questions relatively frequently.

Objective: To develop an innovative and effective educational intervention to inform patients about the need for osteoporosis treatment and to determine factors associated with its online uptake. Methods: Postmenopausal women with a prior fracture and not currently using osteoporosis therapy were eligible to be included in the Activating Patients at Risk for OsteoPOroSis (APROPOS). Four nominal groups with a total of 18 racially/ethnically diverse women identified osteoporosis treatment barriers. We used the Information, Motivation, Behavior Skills conceptual model to develop a direct-to-patient intervention to mitigate potentially modifiable barriers to osteoporosis therapy. The intervention included videos tailored by participants’ race/ethnicity and their survey responses: ranked barriers to osteoporosis treatment, deduced barriers to treatment, readiness to behavior change, and osteoporosis treatment history. Videos consisted of “storytelling” narratives, based on osteoporosis patient experiences and portrayed by actresses of patient-identified race/ethnicity. We also delivered personalized brief phone calls followed by an interactive voice-response phone messages aimed to promote uptake of the videos. Results: To address the factors associated with online intervention uptake, we focused on participants assigned to the intervention arm (n = 1342). These participants were 92.9% Caucasian, with a mean (SD) age 74.9 (8.0) years and the majority (77.7%) had some college education. Preference for natural treatments was the barrier ranked #1 by most (n = 130; 27%), while concern about osteonecrosis of the jaw was the most frequently reported barrier (at any level; n = 322; 67%). Overall, 28.1% (n = 377) of participants in the intervention group accessed the videos online. After adjusting for relevant covariates, the participants who provided an email address had 6.07 (95% CI 4.53–8.14) higher adjusted odds of accessing their online videos compared to those who did not. Conclusion: We developed and implemented a novel tailored multi-modal intervention to improve initiation of osteoporosis therapy. An email address provided on the survey was the most important factor independently associated with accessing the intervention online. The design and uptake of this intervention may have implications for future studies in osteoporosis or other chronic diseases.

Following publication of the original article [1], it has come to our attention that we did not appropriately convert the units for CRP from the National Health and Nutrition Examination Survey (NHANES) from mg/dL to mg/L. In this study, NHANES was considered the general population cohort for this study; interestingly, the correction resulted in changes to a broader range of CRP values in NHANES from 0.01 to 18.0 mg/dL to 0.10 to 180.0 mg/L.

BackgroundTopical NSAIDs have less systemic absorption than oral NSAIDs. We examined the risk of cardiovascular events associated with nonselective topical NSAIDs versus oral NSAIDs among patients with rheumatoid arthritis in Taiwan. Methods and ResultsWe conducted a retrospective cohort study that included patients with incident rheumatoid arthritis who were newly starting therapy with nonselective topical NSAIDs or oral NSAIDs. We used the Taiwan National Health Insurance Research Database (NHIRD). The first date patients received either type of NSAID was defined as the index date. NSAID exposures continued until there was a treatment gap of >30 days. The main outcome was composite cardiovascular events, including myocardial infarction, unstable angina, heart failure, stroke, or revascularization. Follow‐up was censored at treatment discontinuation, switch or addition of other NSAID category, cardiovascular outcome, death, or the end of the study. Propensity score weighted Cox regression models were used to compare the risk of cardiovascular events between topical NSAIDs and oral NSAIDs. There were 10 758 and 78 056 treatment episodes for topical and oral NSAIDs identified. After weighting by propensity score, the cohorts were well balanced over all covariates. The crude cardiovascular event rate was 1.87 per 100 person‐years for topical NSAIDs and 2.14 per 100 person‐years for oral NSAIDs. Results of propensity score weighted Cox regression found the topical NSAID group had 36% lower risk for cardiovascular events compared with the oral NSAID group (hazard ratio, 0.64; 95% confidence interval, 0.43–0.95). ConclusionsWe found topical NSAID users experienced a reduced risk of cardiovascular events compared with oral NSAID users. If future studies with a larger sample size and longer follow‐up confirm these results, NSAID prescribing might change accordingly.

Background Antihypertensive medication use may vary by race and ethnicity. Longitudinal antihypertensive medication use patterns are not well described in women. Methods and Results Participants from the Study of Women's Health Across the Nation (SWAN), a prospective cohort of women (n=3302, aged 42–52), who reported a diagnosis of hypertension or antihypertensive medication use at any annual visit were included. Antihypertensive medications were grouped by class and examined by race/ethnicity adjusting for potential confounders in logistic regression models. A total of 1707 (51.7%) women, mean age 50.6 years, reported hypertension or used antihypertensive medications at baseline or during follow‐up (mean 9.1 years). Compared with whites, blacks were almost 3 times as likely to receive a calcium channel blocker (odds ratio, 2.92; 95% CI, 2.24–3.82) and twice as likely to receive a thiazide diuretic (odds ratio, 2.38; 95% CI, 1.93–2.94). Blacks also had a higher probability of reporting use of ≥2 antihypertensive medications (odds ratio, 1.95; 95% CI, 1.55–2.45) compared with whites. Use of angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers and thiazide diuretics increased over time for all racial/ethnic groups. Contrary to our hypothesis, rates of β‐blocker usage did not decrease over time. Conclusions Among this large cohort of multiethnic midlife women, use of antihypertensive medications increased over time, with angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers becoming the most commonly used antihypertensive medication, even for blacks. Thiazide diuretic utilization increased over time for all race/ethnic groups as did use of calcium channel blockers among blacks; both patterns are in line with guideline recommendations for the management of hypertension.