Real-World Effectiveness of Pegloticase Associated with Use of Concomitant Immunomodulatory Therapy.

OBJECTIVE: The objective was to ascertain pegloticase persistence and adverse events associated with concomitant immunomodulatory drug use in patients with gout. METHODS: We conducted a retrospective analysis of gout patients using the ACR's Rheumatology Informatics System for Effectiveness (RISE) registry from 1/2016 through 6/2020. The first pegloticase infusion defined the index date. Based on concomitant immunomodulatory drug use, we identified 3 exposure groups: 1) Immunomodulatory drug initiators - patients initiating an immunomodulatory prescription ±60 days from index date; 2) Immunomodulatory drug prevalent users - patients receiving their first immunomodulatory drug prescription >60 days before the index date with at least one prescription within ±60 days of index date; and 3) Immunomodulatory non-users - patients receiving pegloticase without concomitant IMM drugs. We calculated the proportion of patients achieving a serum urate (SU) ≤6mg/dL and with lab abnormalities (WBC <3.4; platelets <135,000; HCT <30; ALT or AST ≥1.5X ULN) within 180 days after the index date. Cox regression analyzed time to pegloticase discontinuation controlling for potential confounders. RESULTS: We identified 700 pegloticase users (91 immunomodulatory drug initiators, 33 immunomodulatory drug prevalent users, and 576 non-users) with median follow-up of 14 months. Immunomodulatory drug users were less likely to discontinue pegloticase. The adjusted hazard ratio of pegloticase discontinuation associated with concomitant immunomodulatory drug initiation was 0.52 (95% CI: 0.37,0.75) and 0.69 (95% CI: 0.42,1.16) for prevalent users. Lab abnormalities were uncommon (<5%) and were not higher with concomitant immunomodulatory use. CONCLUSION: Consistent with clinical trials, results from this large observational registry suggest that concomitant immunomodulatory drug use improves pegloticase persistence.

Classifying Multimorbidity Using Drug Concepts via the Rx-Risk Comorbidity Index: Methods and Comparative Cross-Sectional Study.

OBJECTIVE: The study objective was to update a method to identify comorbid conditions using only medication information in circumstances in which diagnosis codes may be undercaptured, such as in single-specialty electronic health records (EHRs), and to compare the distribution of comorbidities across Rx-Risk versus other traditional comorbidity indices. METHODS: Using First Databank, RxNorm, and its web-based clients, RxNav and RxClass, we mapped Drug Concept Unique Identifiers (RxCUIs), National Drug Codes (NDCs), and Anatomical Therapeutic Chemical (ATC) codes to Rx-Risk, a medication-focused comorbidity index. In established rheumatoid arthritis (RA) and osteoarthritis (OA) cohorts within the Rheumatology Informatics System for Effectiveness registry, we then compared Rx-Risk with other comorbidity indices, including the Charlson Comorbidity Index, Rheumatic Disease Comorbidity Index (RDCI), and Elixhauser. RESULTS: We identified 965 unique ingredient RxCUIs representing the 46 Rx-Risk comorbidity categories. After excluding dosage form and ingredient related RxCUIs, 80,911 unique associated RxCUIs were mapped to the index. Additionally, 187,024 unique NDCs and 354 ATC codes were obtained and mapped to the index categories. When compared to traditional comorbidity indices in the RA cohort, the median score for Rx-Risk (median 6.00 [25th percentile 2, 75th percentile 9]) was much greater than for Charlson (median 0 [25th percentile 0, 75th percentile 0]), RDCI (median 0 [25th percentile 0, 75th percentile 0]), and Elixhauser (median 1 [25th percentile 1, 75th percentile 1]). Analyses of the OA cohort yielded similar results. For patients with a Charlson score of 0 (85% of total), both the RDCI and Elixhauser were close to 1, but the Rx-Risk score ranged from 0 to 16 or more. CONCLUSION: The misclassification and under-ascertainment of comorbidities in single-specialty EHRs can largely be overcome by using a medication-focused comorbidity index.

Identifying inpatient mortality in MarketScan claims data using machine learning.

PURPOSE: Inpatient mortality is an important variable in epidemiology studies using claims data. In 2016, MarketScan data began obscuring specific hospital discharge status types for patient privacy, including inpatient deaths, by setting the values to missing. We used a machine learning approach to correctly identify hospitalizations that resulted in inpatient death using data prior to 2016. METHODS: All hospitalizations from 2011 to 2015 with discharge status of missing, died, or one of the other subsequently obscured values were identified and divided into a training set and two test sets. Predictor variables included age, sex, elapsed time from hospital discharge until last observed claim and until healthcare plan disenrollment, and absence of any discharge diagnoses. Four machine learning methods were used to train statistical models and assess sensitivity and positive predictive value (PPV) for inpatient mortality. RESULTS: Overall 1 307 917 hospitalizations were included. All four machine learning approaches performed well in all datasets. Random forest performed best with 88% PPV and 93% sensitivity for the training set and both test sets. The two factors with the highest relative importance for identifying inpatient mortality were having no observed claims for the patient on days 2-91 following hospital discharge and patient disenrollment from the healthcare plan within 60 days following hospital discharge. CONCLUSION: We successfully developed machine learning algorithms to identify inpatient mortality. This approach can be applied to obscured data to accurately identify inpatient mortality among hospitalizations with missing discharge status.

Real-World Sarilumab Use and Rule Testing to Predict Treatment Response in Patients with Rheumatoid Arthritis: Findings from the RISE Registry.

INTRODUCTION: Clinical trial findings may not be generalizable to routine practice. This study evaluated sarilumab effectiveness in patients with rheumatoid arthritis (RA) and tested the real-world applicability of a response prediction rule, derived from trial data using machine learning (based on C-reactive protein [CRP] > 12.3 mg/l and seropositivity [anticyclic citrullinated peptide antibodies, ACPA +]). METHODS: Sarilumab initiators from the ACR-RISE Registry, with ≥ 1 prescription on/after its FDA approval (2017-2020), were divided into three cohorts based on progressively restrictive criteria: Cohort A (had active disease), Cohort B (met eligibility criteria of a phase 3 trial in RA patients with inadequate response/intolerance to tumor necrosis factor inhibitors [TNFi]), and Cohort C (characteristics matched to the phase 3 trial baseline). Mean changes in Clinical Disease Activity Index (CDAI) and Routine Assessment of Patient Index Data 3 (RAPID3) were evaluated at 6 and 12 months. In a separate cohort, predictive rule was tested based on CRP levels and seropositive status (ACPA and/or rheumatoid factor); patients were categorized into rule-positive (seropositive with CRP > 12.3 mg/l) and rule-negative groups to compare the odds of achieving CDAI low disease activity (LDA)/remission and minimal clinically important difference (MCID) over 24 weeks. RESULTS: Among sarilumab initiators (N = 2949), treatment effectiveness was noted across cohorts, with greater improvement noted for Cohort C at 6 and 12 months. Among the predictive rule cohort (N = 205), rule-positive (vs. rule-negative) patients were more likely to reach LDA (odds ratio: 1.5 [0.7, 3.2]) and MCID (1.1 [0.5, 2.4]). Sensitivity analyses (CRP > 5 mg/l) showed better response to sarilumab in rule-positive patients. CONCLUSIONS: In real-world setting, sarilumab demonstrated treatment effectiveness, with greater improvements in the most selective population, mirroring phase 3 TNFi-refractory and rule-positive RA patients. Seropositivity appeared a stronger driver for treatment response than CRP, although optimization of the rule in routine practice requires further data.

Rheumatoid arthritis (RA) is a condition that may cause joint damage, if untreated. Sarilumab is an advanced medication, approved for treating moderate-to-severe RA in patients not responding to initial standard medicines. Clinical trials have shown that sarilumab improves RA symptoms; however, some people do not respond. This is a common problem in RA treatment. Physicians measure proteins in people's blood (called biomarkers; e.g., anticyclic citrullinated peptide antibodies [ACPA], C-reactive protein [CRP], and rheumatoid factor [RF]) to predict a medicine's response. A previous study showed that people with positive blood tests for ACPA and CRP (> 12.3 mg/l) responded well to sarilumab; this study was based on machine learning (a branch of science using computers) and identified factors that could be linked to treatment benefits. The present study analyzed routine data of 2949 people from the ACR-RISE Registry and showed an improvement in RA symptoms after 6 and 12 months of sarilumab, with a greater improvement noted in patients previously treated with other medicines. Biomarkers were tested in 205 people to check whether they could predict treatment response in day-to-day life. People were called rule-positive if they tested positive for RF and/or ACPA with CRP > 12.3 mg/l, and otherwise rule-negative. After 24 weeks of treatment, rule-positive people had a greater chance of disease improvement than rule-negative people. These results showed the benefits of sarilumab in RA in routine care and suggested the usefulness of machine learning in identifying biomarkers that physicians can use to make treatment decisions.

Geographic Variation in Disease Burden and Mismatch in Care of Patients With Rheumatoid Arthritis in the United States.

OBJECTIVE: Our objective was to evaluate the factors associated with regional variation of rheumatoid arthritis (RA) disease burden in the US. METHODS: In a retrospective cohort analysis of Rheumatology Informatics System for Effectiveness (RISE) registry data, seropositivity, RA disease activity (Clinical Disease Activity Index [CDAI], Routine Assessment of Patient Index Data-version 3 [RAPID3]), socioeconomic status (SES), geographic region, health insurance type, and comorbidity burden were recorded. An Area Deprivation Index score of more than 80 defined low SES. Median travel distance to practice sites' zip codes was calculated. Linear regression was used to analyze associations between RA disease activity and comorbidity adjusting for age, sex, geographic region, race, and insurance type. RESULTS: Enrollment data for 184,722 patients with RA from 182 RISE sites were analyzed. Disease activity was higher in African American patients, in those from Southern regions, and in those with Medicaid or Medicare coverage. Greater comorbidity was prevalent in patients in the South and those with Medicare or Medicaid coverage. There was moderate correlation between comorbidity and disease activity (Pearson coefficient: RAPID3 0.28, CDAI 0.15). High-deprivation areas were mainly in the South. Less than 10% of all participating practices cared for more than 50% of all Medicaid recipients. Patients living more than 200 miles away from specialist care were located mainly in Southern and Western regions. CONCLUSION: A disproportionately large portion of socially deprived, high comorbidity, and Medicaid-covered patients with RA were cared for by a minority of rheumatology practices. Studies are needed in high-deprivation areas to establish more equitable distribution of specialty care for patients with RA.

Rheumatoid Arthritis Disease Activity and Hospitalized Infection in a Large US Registry.

OBJECTIVES: The association between disease activity and infection risk among patients with rheumatoid arthritis (RA) is not clear, and it is challenging to determine because of confounding due to the effects of RA treatments and comorbidities. METHODS: Using patients with RA in the CorEvitas registry with Medicare coverage in 2006-2019, we identified eligible patients who had at least 1 visit with moderate disease activity based on the Clinical Disease Activity Index (CDAI; CDAI >10 and ≤22). Follow-up started at the subsequent CorEvitas visit. Hospitalized infection during follow-up was assessed in linked Medicare data. We calculated the incidence rate of hospitalized infection for patients in remission, and low and moderate disease activity, and estimated the effect of time-varying CDAI on hospitalized infection by controlling for baseline and time-dependent confounders using marginal structural models (MSMs). RESULTS: A total of 3,254 patients with RA were eligible for analysis, among which 529 hospitalized infections were identified during follow-up. The crude incidence of hospitalized infection was 3.8 per 100 person-years for patients in remission, 6.6 for low disease activity, and 8.0 for moderate disease activity. Using MSMs and compared with being in remission, the hazard ratio of hospitalized infection associated with low disease activity was 1.60 (95% confidence interval [95% CI] 1.13-2.28) and with moderate disease activity was 1.83 (95% CI 1.30-2.64). CONCLUSION: The risk of hospitalized infection was higher for patients with RA in low or moderate disease activity than for those in remission after accounting for the interplay of disease activity, RA treatments, treatment switching, and other potential confounders.

Influence of Multimorbidity on New Treatment Initiation and Achieving Target Disease Activity Thresholds in Active Rheumatoid Arthritis: A Cohort Study Using the Rheumatology Informatics System for Effectiveness Registry.

OBJECTIVE: To determine whether multimorbidity is associated with treatment changes and achieving target disease activity thresholds in patients with active rheumatoid arthritis (RA). METHODS: We conducted a retrospective cohort study of adults with active RA within the Rheumatology Informatics System for Effectiveness (RISE) registry. Multimorbidity was measured using RxRisk, a medication-based index of chronic disease. We used multivariable logistic regression models to assess the associations of multimorbidity with the odds of initiating a new disease-modifying antirheumatic drug (DMARD) in active RA, and among those initiating a new DMARD, the odds of achieving low disease activity or remission. RESULTS: We identified 15,626 patients using the Routine Assessment of Patient Index Data 3 (RAPID3) cohort and 5,733 patients using the Clinical Disease Activity Index (CDAI) cohort. All patients had active RA, of which 1,558 (RAPID3) and 834 (CDAI) initiated a new DMARD and had follow-up disease activity measures. Patients were middle aged, female, and predominantly White, and on average received medications from 6 to 7 RxRisk categories. Multimorbidity was not associated with new DMARD initiation in active RA. However, a greater burden of multimorbidity was associated with lower odds of achieving treatment targets (per 1-unit RxRisk: RAPID3 cohort odds ratio [OR] 0.95 [95% confidence interval (95% CI) 0.91, 0.98]; CDAI cohort OR 0.94 [95% CI 0.90, 0.99]). Those with the highest burden of multimorbidity had the lowest odds of achieving target RA disease activity (RAPID3 cohort OR 0.54 [95% CI 0.34, 0.85]; CDAI cohort OR 0.65 [95% CI 0.37, 1.15]). CONCLUSION: These findings from a large, real-world registry illustrate the potential impact of multimorbidity on treatment response and indicate that a more holistic management approach targeting multimorbidity may be needed to optimize RA disease control in these patients.

Adherence patterns in naïve and prevalent use of infliximab and its biosimilar.

INTRODUCTION: Although short-term clinical trials have demonstrated that switching from infliximab (INF) bio-originator to its biosimilar is safe with no significant loss of efficacy, there are limited real-world data comparing their patterns of use and adherence. METHODS: Using 2015-2018 IBM Marketscan data, we established 4 cohorts of patients with at least one administration or pharmacy claim for INF bio-originator or biosimilar in 2017, including INF naïve biosimilar users, INF prevalent biosimilar users, INF naïve bio-originator users, and INF prevalent bio-originator users, defined according to their prior use of INF from 2015 to their first INF administration in 2017. The proportion of days covered (PDC) was calculated for patients with at least 6, 12, or 18 months of follow-up time. Factors associated with optimal adherence (PDC > 80%) were evaluated using log-binomial models. RESULTS: We identified 96 INF naïve biosimilar users, 223 INF prevalent biosimilar users, 2,149 INF naïve bio-originator users, and 10,970 INF prevalent bio-originator users. At the end of 18 months of follow-up, 64% of INF prevalent bio-originators, 48% of INF naïve biosimilars, 41% of INF naïve bio-originators, and 36% of INF prevalent biosimilars had optimal adherence. Depression, previous hospitalization, and greater use of prior biologics were negatively associated with adherence, whereas IBD diagnoses (referent to RA) and age 55-64 (referent to < 35) were positively associated with high adherence. CONCLUSION: INF prevalent users had higher adherence in our analyses than INF naïve users. However, further studies with larger sample size are needed to evaluate INF biosimilar users' adherence.

Incidence of dementia in patients with rheumatoid arthritis and association with disease modifying anti-rheumatic drugs - Analysis of a national claims database.

OBJECTIVE: To evaluate the risk of incident dementia associated with the use of biologics or targeted synthetic DMARDs (b/tsDMARD) compared to conventional synthetic (cs) DMARDS only in patients with rheumatoid arthritis (RA). METHODS: We analyzed claims data from the Center for Medicare & Medicare Services (CMS) from 2006-2017. Patients with RA were identified as adults ≥40 years old and two RA diagnoses by a rheumatologist > 7 and < 365 days apart. Patients with a prior diagnosis of dementia were excluded. Use of cs/b/tsDMARDs was the exposure of interest. Person-time was classified as either: 1) b/tsDMARD exposed, which included tumor necrosis factor alpha inhibitors (TNFi)-bDMARDs, non-TNFi-bDMARDs or tsDMARDs with or without csDMARDs; 2) csDMARD-exposed: any csDMARD without b/tsDMARD. Patients could contribute time to different exposure groups if they changed medications. Incident dementia was defined as: 1 inpatient OR 2 outpatients ICD-9-CM or ICD-10 claims for dementia, OR prescription of a dementia-specific medication (rivastigmine, galantamine, memantine, donepezil, tacrine). Age-adjusted incident rates (IR) were calculated, and univariate and multivariate Cox proportional hazard models were used to calculate Hazard Ratios (HR) and 95% confidence intervals (CI). RESULTS: We identified 141,326 eligible RA patients; 80% female and 75.3% white, median age 67 years and mean (SD) exposure time of 1.1 (1.5) years. There were 233,271 initiations of c/b/tsDMARDS and 3,794 cases of incident dementia during follow up. The crude IR of dementia was 2.0 (95% CI 1.9-2.1) per 100 person-years for patients on csDMARDs and 1.3 (95% CI 1.2-1.4) for patients on any b/tsDMARD. Patients on b/tsDMARDs had an adjusted 19% lower risk for dementia than patients on csDMARDs [HR 0.81 (95% CI 0.76-0.87)]. Subgroup analysis found comparable risk reductions between TNFi, non-TNFi, and tsDMARDs. on the risk of dementia. CONCLUSIONS AND RELEVANCE: The incidence of dementia in patients with RA was lower in patients receiving b/tsDMARDs when compared to patients on csDMARD only. No differences were observed between different classes of b/tsDMARDs, suggesting that decreased risk is possibly explained by the overall decrease in inflammation rather than a specific mechanism of action of these drugs.

ISPE-Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID-19: Opportunities and Trade-Offs.

As the scientific research community along with healthcare professionals and decision makers around the world fight tirelessly against the coronavirus disease 2019 (COVID-19) pandemic, the need for comparative effectiveness research (CER) on preventive and therapeutic interventions for COVID-19 is immense. Randomized controlled trials markedly under-represent the frail and complex patients seen in routine care, and they do not typically have data on long-term treatment effects. The increasing availability of electronic health records (EHRs) for clinical research offers the opportunity to generate timely real-world evidence reflective of routine care for optimal management of COVID-19. However, there are many potential threats to the validity of CER based on EHR data that are not originally generated for research purposes. To ensure unbiased and robust results, we need high-quality healthcare databases, rigorous study designs, and proper implementation of appropriate statistical methods. We aimed to describe opportunities and challenges in EHR-based CER for COVID-19-related questions and to introduce best practices in pharmacoepidemiology to minimize potential biases. We structured our discussion into the following topics: (1) study population identification based on exposure status; (2) ascertainment of outcomes; (3) common biases and potential solutions; and (iv) data operational challenges specific to COVID-19 CER using EHRs. We provide structured guidance for the proper conduct and appraisal of drug and vaccine effectiveness and safety research using EHR data for the pandemic. This paper is endorsed by the International Society for Pharmacoepidemiology (ISPE).

Inpatient Diagnosis of Delirium and Encephalopathy: Coding Trends in 2011-2018.

BACKGROUND: Ten medical societies have called for scientific literature to integrate research on delirium and encephalopathy, while physicians continually debate how to accurately document diagnoses of acute confusional states. OBJECTIVE: To promote this integration, we evaluated trends in diagnoses of delirium and encephalopathy among hospitalized adults and physician specialties, incorporating transitions to the Diagnostic and Statistical Manual of Mental Disorders-5 and the International Classification of Disease, tenth edition. METHODS: Using the 2011-2018 IBM MarketScan datasets, we identified delirium/encephalopathy patients aged ≥18 years using International Classification of Disease 9/10 codes among hospitalized patients. We identified physician specialties associated with the hospitalization and comorbidities within one year before the diagnosis of delirium or encephalopathy. Log-binomial models were used to evaluate diagnostic trends, adjusting for age, gender, insurance, and comorbidities. RESULTS: We identified 10,509 hospitalized patients with a diagnosis of delirium and 94,438 with encephalopathy between 2011 and 2018. Although the number of patients with either diagnosis increased over time, the use of delirium diagnosis was less than it was for encephalopathy compared with 2011 after adjusting for covariates (adjusted risk ratio 0.45; 95% confidence interval 0.43 to 0.48). During the 8 years, neurologists and internists increased their use of both diagnoses, whereas only psychiatrists increased their use of delirium. Family practice physicians and nurse practitioners presented no significant change in either diagnosis for this timeframe. CONCLUSIONS: Our results suggest that refined diagnostic codes and criteria may alter trends among clinicians in diagnosing delirium and/or encephalopathy. Additional diagnostic clarity may be necessary to support refined diagnoses among family practice physicians and nurse practitioners.

Patient-Reported Nausea and Fatigue Related to Methotrexate: A Prospective, Self-Controlled Study in the ArthritisPower® Registry.

INTRODUCTION: The magnitude and frequency of temporally related methotrexate (MTX)-associated side effects in rheumatoid arthritis (RA) or psoriatic arthritis (PsA) patients are difficult to quantify using traditional research methods. As proof of concept designed in part to implement digital data collection for remote patient monitoring, we conducted a study implementing self-controlled case series analytic methods to understand MTX-related symptoms in RA or PsA. METHODS: In study phase 1, adults with RA or PsA from the ArthritisPower® Registry (past or current oral MTX users) participated in a cross-sectional survey. In phase 2, current MTX users participated in a longitudinal study and completed the Patient-Reported Outcomes Measurement Information System (PROMIS®) 1-day nausea/vomiting and fatigue measure. Within-person change in PROMIS scores between risk (6-36 h post-dose) and control (96-144 h post-dose) windows were compared using mixed models. RESULTS: The baseline survey was completed by 671 participants (mean age: 54 years, 88% female, 92% white, 79% with RA). Among current MTX users (353/671 [53%]), most reported MTX-associated side effects (216/353 [61%]), most frequently fatigue (161/353 [46%]). Among phase 2 participants with (n = 39) and without (n = 84) baseline nausea, mean increase in PROMIS nausea was 5.1 units (P < 0.0001) and 0.7 units (P = 0.135), respectively; among those with (n = 51) and without (n = 72) baseline fatigue, mean increase in PROMIS fatigue was 3.9 units (P = 0.0003) and 0.4 units (P = 0.554), respectively. CONCLUSIONS: Digital remote patient monitoring presents an opportunity to detect and address medication tolerability in real time. Using a novel study design to control for between-person confounding, the magnitude of nausea and fatigue experienced by participants with RA and PsA temporally related to weekly MTX use was substantial.

The Annual Diagnostic Prevalence of Ankylosing Spondylitis and Axial Spondyloarthritis in the United States Using Medicare and MarketScan Databases.

OBJECTIVE: The objective of this study was to investigate the diagnostic prevalence of ankylosing spondylitis (AS) and axial spondyloarthritis (axSpA) in the United States and examine treatment patterns for these diseases. METHODS: This retrospective observational cohort study drew from 2006-2014 data in the US Medicare Fee-for-Service and IBM MarketScan databases. AS and axSpA diagnoses were identified through International Classification of Diseases, Ninth Revision [ICD-9] codes. Diagnostic prevalence (per 10,000 patients) was calculated as patients with AS and axSpA with full insurance coverage in each calendar year divided by the total patients with full insurance coverage in the same year. Two diagnosis definitions were used: definition 1 (D1), one or more relevant ICD-9 codes from hospital claims or two or more relevant ICD-9 codes from outpatient claims; definition 2 (D2), one or more codes from hospital/outpatient claims. Primary analyses assessed annual AS and axSpA prevalence (D1); sensitivity analyses assessed annual (D2) and 2-year prevalence. Patterns in prevalence and treatment use were analyzed descriptively; no statistical tests were performed. RESULTS: An increase in AS prevalence (per 10,000 patients) was seen from 2006 to 2014 in primary analyses (Medicare: 2.12-3.60; MarketScan: 0.85-1.42) and sensitivity analyses. A similar trend occurred for axSpA (Medicare: 4.39-6.52; MarketScan: 1.33-2.21). For Medicare, the proportion of patients with AS (D1) using tumor necrosis factor α inhibitors (TNFis), conventional synthetic antirheumatic drugs (csARDs), nonsteroidal antiinflammatory drugs (NSAIDs), opioids, and glucocorticoids remained relatively stable; for MarketScan, TNFi-treated patients increased (51.7% to 65.7%) and NSAID-treated patients decreased (63.5% to 55.7%). CONCLUSION: AS and axSpA prevalence may have increased in the United States between 2006 and 2014. Reasons are unknown, but this may be due to increased disease awareness, among other factors.

Effects of the SARS-CoV-2 global pandemic on U.S. rheumatology outpatient care delivery and use of telemedicine: an analysis of data from the RISE registry.

The SARS-CoV-2 global pandemic resulted in major disruptions to medical care. We aimed to understand changes in outpatient care delivery and use of telemedicine in U.S. rheumatology practices during this period. Rheumatology Informatics System Effectiveness (RISE) is a national, EHR-enabled registry that passively collects data on all patients seen by participating practices. Included practices were required to have been participating in RISE from January 2019 through August 2020 (N = 213). We compared total visit counts and telemedicine visits during March-August 2020 to March-August 2019 and stratified by locations in states with shelter-in-place (SIP) orders. We assessed characteristics of patients within each practice, including primary rheumatic diagnosis and disease activity scores, where available. We included 213 practices with 945,160 patients. Overall, we found visit counts decreased by 10.9% (from 1,302,455 to 1,161,051) between March and August 2020 compared to 2019; this drop was most dramatic during the month of April (- 22.3%). Telemedicine visits increased from 0% to a mean of 12.1%. Practices in SIP states had more dramatic decreases in visits, (11.5% vs. 5.3%). We found no major differences in primary diagnoses or disease activity across the two periods. We detected a meaningful decrease in rheumatology visits in March-August 2020 during the SARS-CoV-2 global pandemic compared to the year prior with a concomitant increase in the use of telemedicine. Future work should address possible adverse consequences to patient outcomes due to decreased contact with clinicians.

Meaningful Change Thresholds for Patient-Reported Outcomes Measurement Information System (PROMIS) Fatigue and Pain Interference Scores in Patients With Rheumatoid Arthritis.

OBJECTIVE: We estimated meaningful change thresholds (MCTs) for Patient-Reported Outcomes Measurement Information System (PROMIS) Fatigue and Pain Interference in rheumatoid arthritis (RA). METHODS: The responsiveness of several patient-reported outcomes (PROs) was assessed among 521 patients with RA in the Arthritis, Rheumatism, and Aging Medical Information Systems (ARAMIS) cohort. PROMIS Fatigue (7-item) and Pain Interference (6-item) short form instruments were administered at baseline, 6 months, and 12 months. Self-reported retrospective changes over the previous 6 months (a lot better/worse, a little better/worse, stayed the same) were obtained at 6 and 12 months' follow-up. We estimated MCTs using the mean change in PROMIS scores for patients who rated their change "a little better" or "a little worse." RESULTS: Baseline fatigue and pain interference scores were near normal (median 54 and 56, respectively). At 6 months, 7.9% of patients reported their fatigue was a little better compared to baseline (mean change [SD]: -2.6 [4.8]) and 22.8% a little worse (1.7 [5.6]). Pain was a little better for 11.5% of patients (-1.9 [6.1]) and a little worse for 24.2% of patients (0.6 [5.7]). At 12 months, results were similar. Thus, the MCT range was 1-2 points for both fatigue and pain interference. Correlations between change scores and retrospective ratings were low (0.13-0.29), indicating possible underestimation of MCT. CONCLUSION: The group-level MCT for PROMIS Fatigue and Pain Interference is roughly 2-3 points and corresponds to a small effect size, which is consistent with earlier work demonstrating an MCT of 2 points for PROMIS Physical Functioning.

Association of Anti-Tumor Necrosis Factor Therapy With Mortality Among Veterans With Inflammatory Bowel Disease.

Importance: Inflammatory bowel disease (IBD) is commonly treated with corticosteroids and anti-tumor necrosis factor (TNF) drugs; however, medications have well-described adverse effects. Prior work suggests that anti-TNF therapy may reduce all-cause mortality compared with prolonged corticosteroid use among Medicare and Medicaid beneficiaries with IBD. Objective: To examine the association between use of anti-TNF or corticosteroids and all-cause mortality in a national cohort of veterans with IBD. Design, Setting, and Participants: This cohort study used a well-established Veteran's Health Administration cohort of 2997 patients with IBD treated with prolonged corticosteroids (≥3000-mg prednisone equivalent and/or ≥600 mg of budesonide within a 12-month period) and/or new anti-TNF therapy from January 1, 2006, to October 1, 2015. Data were analyzed between July 1, 2019, and December 31, 2020. Exposures: Use of corticosteroids or anti-TNF. Main Outcomes and Measures: The primary end point was all-cause mortality as defined by the Veterans Health Administration vital status file. Marginal structural modeling was used to compare associations between anti-TNF therapy or corticosteroid use and all-cause mortality. Results: A total of 2997 patients (2725 men [90.9%]; mean [SD] age, 50.0 [17.4] years) were included in the final analysis, 1734 (57.9%) with Crohn disease (CD) and 1263 (42.1%) with ulcerative colitis (UC). All-cause mortality was 8.5% (n = 256) over a mean (SD) of 3.9 (2.3) years' follow-up. At cohort entry, 1836 patients were new anti-TNF therapy users, and 1161 were prolonged corticosteroid users. Anti-TNF therapy use was associated with a lower likelihood of mortality for CD (odds ratio [OR], 0.54; 95% CI, 0.31-0.93) but not for UC (OR, 0.33; 95% CI, 0.10-1.10). In a sensitivity analysis adjusting prolonged corticosteroid users to include patients receiving corticosteroids within 90 to 270 days after initiation of anti-TNF therapy, the OR for UC was statistically significant, at 0.33 (95% CI, 0.13-0.84), and the OR for CD was 0.55 (95% CI, 0.33-0.92). Conclusions and Relevance: This study suggests that anti-TNF therapy may be associated with reduced mortality compared with long-term corticosteroid use among veterans with CD, and potentially among those with UC.

ANCA-associated Vasculitis Management in the United States: Data From the Rheumatology Informatics System for Effectiveness (RISE) Registry.

OBJECTIVE: The management of antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) has evolved substantially over the last 2 decades. We sought to characterize AAV treatment patterns in the United States. METHODS: We identified patients with AAV in the Rheumatology Informatics System for Effectiveness (RISE) registry who had at least 2 rheumatology clinician visits between January 1, 2015, and December 31, 2017. Demographics, medications, laboratory test results, and billing codes were extracted from the medical record. Demographic and prescription trends were assessed overall and across US regions. RESULTS: We identified 1462 patients with AAV, 259 (18%) with new or relapsing AAV. The majority were classified as having granulomatosis with polyangiitis (75%). The mean age was 59.8 years and 59% were female. The majority of patients were in the South (45%) followed by the Mid-West (32%), West (12%), and Northeast (8%). Patients had a median of 3 visits and follow-up of 579 days. The most commonly prescribed medications during the study period were glucocorticoids (86%) followed by rituximab (45%), methotrexate (33%), azathioprine (32%), and mycophenolate mofetil (18%); cyclophosphamide (CYC) was rarely used (7%). At the most recent visits in RISE, 47% of patients were on glucocorticoids. Prescription trends were similar across regions. CONCLUSION: To our knowledge, this is the first study to evaluate the demographics and management of AAV by rheumatologists outside of major referral centers. Management strategies vary widely, but CYC is rarely used. These observations can be used to inform future research priorities. Additional studies are needed to characterize AAV severity in RISE as well as patient and provider treatment preferences.

Benefits of Methotrexate Use on Cardiovascular Disease Risk Among Rheumatoid Arthritis Patients Initiating Biologic Disease-modifying Antirheumatic Drugs.

OBJECTIVE: Methotrexate (MTX) has been associated with reduced risk for cardiovascular disease (CVD) events among patients with rheumatoid arthritis (RA) not exposed to biologic disease-modifying antirheumatic drugs (bDMARDs). The effect of concomitant MTX on CVD risk among RA patients initiating bDMARDs remains unknown. METHODS: A retrospective cohort study was conducted to assess the effect of MTX on CVD risk using 2006-2015 Medicare claims data for patients with RA initiating bDMARD. The main exposure was current use of MTX, updated in a time-varying fashion. The primary outcome was a composite of incident myocardial infarction (MI), stroke, and fatal CVD. Secondary outcomes were each event that comprised the primary outcome. Incidence rates (IR) and 95% CI were calculated using Poisson regression. Associations between MTX and risk of CVD were assessed using Cox regression. RESULTS: A total of 88,255 bDMARD initiations and 1861 CVD events were included in this study. Mean age was 64.6 (12.3) years, 84.0% were female, and 68.2% were non-Hispanic White. The crude IRs (95% CI) for CVD were 17.9 (16.9-18.8) and 12.1 (11.1-13.2) per 1000 patient-years among MTX unexposed and exposed, respectively. The multivariable adjusted HR (95% CI) for CVD events associated with MTX was 0.76 (0.68-0.85). Multivariable adjusted HRs were 0.78 (0.66-0.91), 0.74 (0.62-0.88), 0.77 (0.68-0.86), and 0.82 (0.73-0.93) for MI, stroke, MI or stroke, and a composite CVD outcome, respectively. Results were robust in sensitivity and subgroup analyses. CONCLUSION: Among patients with RA receiving biologics, concomitant MTX use was associated with a 24% lower risk for CVD events.