A somatization comorbidity phenotype impacts response to therapy in rheumatoid arthritis: post-hoc results from the certolizumab pegol phase 4 PREDICT trial

The Multimorbidity Web in rheumatoid arthritis

Multimorbidity, the presence of multiple chronic conditions, is highly prevalent in people with RA. An essential characteristic of multimorbidity is the interrelatedness of the different conditions that may develop in a multimorbid person. Recent studies have begun to identify and describe the Multimorbidity Web by elucidating unique multimorbidity patterns in people with RA. The primary multimorbidity patterns in this web are cardiopulmonary, cardiometabolic, and mental health and chronic pain multimorbidity. Once caught in the Multimorbidity Web, the consequences can be devastating, with reduced quality of life, physical function, survival, and treatment responses observed in multimorbid RA persons. The development of effective management and preventive approaches for multimorbidity in people with RA is in its infancy. Determining how best to assess, intervene, and prevent multimorbidity in RA is crucial to optimize long-term outcomes in people with RA.

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.

Identification of Multimorbidity Patterns in Rheumatoid Arthritis Through Machine Learning

OBJECTIVE

Recognizing that the interrelationships between chronic conditions that complicate rheumatoid arthritis (RA) are poorly understood, we aimed to identify patterns of multimorbidity and to define their prevalence in RA through machine learning.

METHODS

We constructed RA and age- and sex-matched (1:1) non-RA cohorts within a large commercial insurance database (MarketScan) and the Veterans Health Administration (VHA). Chronic conditions (n = 44) were identified from diagnosis codes from outpatient and inpatient encounters. Exploratory factor analysis was performed separately in both databases, stratified by RA diagnosis and sex, to identify multimorbidity patterns. The association of RA with different multimorbidity patterns was determined using conditional logistic regression.

RESULTS

We studied 226,850 patients in MarketScan (76% female) and 120,780 patients in the VHA (89% male). The primary multimorbidity patterns identified were characterized by the presence of cardiopulmonary, cardiometabolic, and mental health and chronic pain disorders. Multimorbidity patterns were similar between RA and non-RA patients, female and male patients, and patients in MarketScan and the VHA. RA patients had higher odds of each multimorbidity pattern (odds ratios [ORs] 1.17-2.96), with mental health and chronic pain disorders being the multimorbidity pattern most strongly associated with RA (ORs 2.07-2.96).

CONCLUSION

Cardiopulmonary, cardiometabolic, and mental health and chronic pain disorders represent predominant multimorbidity patterns, each of which is overrepresented in RA. The identification of multimorbidity patterns occurring more frequently in RA is an important first step in progressing toward a holistic approach to RA management and warrants assessment of their clinical and predictive utility.

Machine Learning Applied to Patient-Reported Outcomes to Classify Physician-Derived Measures of Rheumatoid Arthritis Disease Activity

OBJECTIVE

Patient-reported outcome (PRO) data have assumed increasing importance in the care of patients with rheumatoid arthritis (RA), yet physician-derived disease activity measures, such as Clinical Disease Activity Index (CDAI), remain the most accepted metrics to assess disease activity. The possibility that newer longitudinal PRO data might be used as a proxy for the CDAI has not been evaluated.

METHODS

Using data from a large pragmatic trial, we evaluated patients with RA initiating golimumab intravenous or infliximab. The classification target was low disease activity (LDA) (CDAI ≤10) at the first visit between months 3 and 12. Data were randomly partitioned into training (80%) and test (20%) data sets. Multiple machine learning (ML) methods (eg, random forests, gradient boosting, support vector machines) were used to classify CDAI disease activity category, conduct feature selection, and assess feature importance. Model performance evaluated cross-validated error, comparing different ML approaches using both training and test data.

RESULTS

A total of 494 patients were analyzed, and 36.4% achieved LDA. The most important classification features included several Patient-Reported Outcomes Measurement Information System measures (social participation, pain interference, pain intensity, and physical function), patient global, and baseline CDAI. Among all ML methods, random forests performed best. Overall model accuracy and positive predictive values for all ML methods were approximately 80%.

CONCLUSION

ML methods coupled with longitudinal PRO data appear useful and can achieve reasonable accuracy in classifying LDA among patients starting a new biologic. This approach has promise for real-world evidence generation in the common circumstance when physician-derived disease activity data are not available yet PRO measures are.

What Factors Influence Treatment Effectiveness in Rheumatoid Arthritis: An Evidence-Based Approach to Multidimensional Measurement of Treatment Effectiveness

Purpose: The aim of the study was to examine the effects of socio-demographic characteristics, disease-related characteristics and health care use related-characteristics on the treatment effectiveness of rheumatoid arthritis patients, both separately and together. Methods: The sample of the study consisted of 440 rheumatoid arthritis patients for 99% confidence level, and this sample was reached based on the convenience sampling method. Patients who received at least one anti-TNF therapy were included in the study. Treatment effectiveness levels of rheumatoid arthritis patients were measured with a questionnaire. In the analysis of the study, four different regression models were established. In the first model, socio-demographic characteristics; in the second model, disease characteristics; in the third model, health care use characteristics: in the fourth model, the effect of all these variables on treatment effectiveness was examined. Results: In the study, smoking status, age (socio-demographic characteristics), drug regimen complexity, comorbidity status, education about the disease, disease duration (disease characteristics), and a number of admissions (health care use characteristics), were found to have a significant effect on treatment effectiveness. Conclusion: In the study, the factors affecting the treatment effectiveness were determined. Therefore, it is important to consider these factors revealed in this study in order to increase the treatment effectiveness in patients with rheumatoid arthritis.

Efficacy of tofacitinib in patients with rheumatoid arthritis stratified by baseline body mass index: an analysis of pooled data from phase 3 studies

Objective

Tofacitinib is an oral Janus kinase for the treatment of rheumatoid arthritis (RA). This post hoc analysis assessed whether baseline body mass index (BMI) impacts tofacitinib efficacy in patients with RA.

Methods

Pooled data from six phase 3 studies in patients receiving tofacitinib 5 mg (N=1589) or 10 mg (N=1611) twice daily or placebo (advancing to active treatment at months 3 or 6; N=680), ±conventional synthetic disease-modifying antirheumatic drugs, were stratified by baseline BMI (<25, 25 to <30, ≥30 kg/m²). Endpoints (through to month 6) were assessed descriptively: American College of Rheumatology 20/50/70 response rates; changes from baseline (∆) in Disease Activity Score in 28 joints, erythrocyte sedimentation rate (DAS28-4(ESR)), DAS28-4(C-reactive protein), Clinical Disease Activity Index (CDAI), Health Assessment Questionnaire-Disability Index (HAQ-DI) and pain; and proportions of patients achieving DAS28-4(ESR) ≥1.2 and HAQ-DI ≥0.22 decreases from baseline, low disease activity (DAS28-4(ESR) ≤3.2 or CDAI ≤10) and radiographic non-progression (Δmodified Total Sharp Score ≤0.5; months 12 and 24). Estimates were adjusted using multivariable models for selected outcomes. Univariate/multivariable regression analyses determined predictors of month 6 outcomes.

Results

Of 3880 patients included, 1690 (43.6%), 1173 (30.2%) and 1017 (26.2%) had baseline BMI <25, 25 to <30 and ≥30 kg/m², respectively. Tofacitinib showed greater efficacy improvements versus placebo in each BMI category. Differences in efficacy outcomes (adjusted and unadjusted) were generally not clinically meaningful across BMI categories within treatment groups. In regression analyses, BMI was not consistently associated with selected outcomes.

Conclusions

Baseline BMI did not consistently affect tofacitinib response suggesting that tofacitinib is an effective oral treatment option for adults with moderate to severe RA regardless of baseline BMI, including patients with BMI ≥30 kg/m².

Trial registration numbers

NCT00814307, NCT01039688; NCT00960440; NCT00847613; NCT00856544; NCT00853385.

Update Fibromyalgiesyndrom

In der neuen Klassifikation der Krankheiten (ICD-11) der Weltgesundheitsorganisation wurde das FMS – ohne Absprache mit rheumatologischen Fachgesellschaften – aus dem Kapitel „Erkrankungen des muskuloskelettalen System und des Bindegewebes“ entfernt und in ein neu geschaffenes Kapitel „Chronischer Schmerz“ aufgenommen. Pathologische Befunde an den kleinen Nervenfasern bei einer Untergruppe von Patienten belegen nicht, dass das FMS eine Neuropathie der kleinen Nervenfasern ist. Die Überprüfung der sogenannten Tender Points zur Diagnose des FMS ist nicht mehr erforderlich. Es wurden mehrere neue diagnostische Kriterien entwickelt, welche zur symptombasierten Diagnose neben chronischen Schmerzen in mehreren Körperregionen nicht-erholsamen Schlaf und Müdigkeit (körperlich und / oder geistig) erfordern. Die nach klinischen Kriterien durchführbare Schweregradeinteilung (leicht, mittel, schwer) des FMS ist wichtig für eine abgestufte Versorgung. In Abhängigkeit von psychologischen Befunden stehen verschiedene evidenzbasierte psychotherapeutische Verfahren zur Verfügung, welche bei schweren Formen des FMS eingesetzt werden sollen. Ein komorbides (sekundäres) FMS ist bei entzündlich-rheumatischen Erkrankungen häufig und führen zu falsch hohen Aktvitätsscores. Cannabisbasierte Arzneimittel sind eine Therapieoption für eine Untergruppe von FMS-Patienten.

Exposure-Response Relationship of Certolizumab Pegol and Achievement of Low Disease Activity and Remission in Patients With Rheumatoid Arthritis

Anti-tumor necrosis factor (anti-TNF) drugs are often prescribed for the treatment of rheumatoid arthritis (RA) and other immune-mediated inflammatory diseases. Although this treatment has been shown to be effective in many patients, up to 40% of patients do not achieve disease control. Drug concentration in plasma may be a factor affecting the observed variability in therapeutic response. In this study, we aimed to identify the plasma concentrations of the anti-TNF certolizumab pegol (CZP), associated with improvement in disease activity in patients with RA. Data were pooled from three randomized, controlled clinical trials with a combined total of 1,935 patients analyzed. Clinical outcomes of low disease activity (LDA) and remission were defined as Disease Activity Score in 28 joints with C-reactive protein (DAS28(CRP)) ≤ 2.7 and < 2.3, respectively. Quartile analysis results indicated that there may be an exposure-response relationship between CZP concentration and LDA/remission outcomes at weeks 12 and 24; the association was strongest for LDA (P < 0.05). Receiver operating characteristic (ROC) analysis showed that CZP concentrations ≥ 28.0 μg/ml at week 12, and ≥ 17.6 μg/ml at week 24, were associated with a greater likelihood of achieving LDA/remission outcomes. Although confirmatory studies are warranted to define the optimal CZP therapeutic range at weeks 12 and 24, these data indicate that CZP concentrations may be associated with improvement of disease activity.

Fibromyalgia interferes with disease activity and biological therapy response in inflammatory rheumatic diseases

Fibromyalgia is one of the numerous comorbidities that may accompany inflammatory rheumatic diseases. Concomitant fibromyalgia in inflammatory rheumatic conditions can interfere with symptomatology, disease activity and overall management plan. The aim of the present narrative review article was to discuss the current evidence on (i) the prevalence/frequency of comorbid fibromyalgia in inflammatory rheumatic conditions, (ii) the role of fibromyalgia on disease activity, (iii) the impact of concomitant fibromyalgia on biological disease-modifying antirheumatic treatment outcomes and (iv) potential effectiveness of biological disease-modifying antirheumatic drugs on fibromyalgia-related symptoms among patients with inflammatory rheumatic diseases. A literature search was conducted through PubMed/MEDLINE Cochrane and Web of Science databases by using relevant keywords and their combinations. Studies representing different geographical areas of the world revealed that frequency rates of fibromyalgia are higher in inflammatory rheumatic diseases than those in the general population. Comorbid fibromyalgia interferes not only with the disease activity scores but also with the treatment outcomes and management plan. Further evidence is warranted in order to determine the potential benefits of biological disease-modifying antirheumatic drugs on fibromyalgia-related symptoms in patients with inflammatory rheumatic diseases.

Uptake and Clinical Utility of Multibiomarker Disease Activity Testing in the United States

OBJECTIVE

The clinical utility of the multibiomarker disease activity (MBDA) test for rheumatoid arthritis (RA) management in routine care in the United States has not been thoroughly studied.

METHODS

Using 2011-2015 Medicare data, we linked each patient with RA to their MBDA test result. Initiation of a biologic or Janus kinase (JAK) inhibitor in the 6 months following MBDA testing was described. Multivariable adjustment evaluated the likelihood of adding or switching biologic/JAK inhibitor, controlling for potential confounders. For patients with high MBDA scores who added a new RA therapy and were subsequently retested, lack of improvement in the MBDA score was evaluated as a predictor of future RA medication failure, defined by the necessity to change RA medications again.

RESULTS

Among 60,596 RA patients with MBDA testing, the proportion adding or switching biologics/JAK inhibitor among those not already taking a biologic/JAK inhibitor was 9.0% (low MBDA), 11.8% (moderate MBDA), and 19.7% (high MBDA, p < 0.0001). Similarly, among those already taking biologics/JAK inhibitor, the proportions were 5.2%, 8.3%, and 13.5% (p < 0.0001). After multivariable adjustment, referent to those with low disease MBDA scores, the likelihood of switching was 1.51-fold greater (95% CI 1.35-1.69) for patients with moderate MBDA scores, and 2.62 (2.26-3.05) for patients with high MBDA scores. Among those with high MBDA scores who subsequently added a biologic/JAK inhibitor and were retested, lack of improvement in the MBDA score category was associated with likelihood of future RA treatment failure (OR 1.61, 95% CI 1.27-2.03).

CONCLUSION

The MBDA score was associated with both biologic and JAK inhibitor medication addition/switching and subsequent treatment outcomes.

Comorbid fibromyalgia: A qualitative review of prevalence and importance

Fibromyalgia (FM) may be an unrecognized cause of suffering for persons with an array of medical conditions. This is especially true for illness that is characterized by pain of any nature. Once believed to be a unique diagnosis, FM is recently reported to occur concomitantly with various rheumatic diseases, and importantly adversely impacts global health status. However, there is increasing report of FM associated with other diseases that are not defined by chronic pain. This qualitative review examines the evidence for comorbid FM in illness, and where available the effect of FM on the primary disease. Other than for musculoskeletal disorders, the published literature reporting an association of FM with illness is limited with scanty reports for some neurological, gastrointestinal, mental health and other overlapping pain conditions. Comorbid FM adversely affects both health status and outcome for rheumatic diseases, but with limited study in other diseases. When unrecognized, comorbid FM may be mistaken as poor control of the primary disease, leading to incorrect treatment decisions. FM may be a neglected condition that pervades many conditions and may contribute to the burden of illness. Physicians should be alert to the possibility of comorbid FM, and symptoms of FM should be specifically addressed.

SIGNIFICANCE

Comorbid fibromyalgia (FM) in other medical conditions is largely unrecognized. When reported as accompanying rheumatic diseases, FM adversely affects global health status. With limited reports of comorbid FM with other conditions, neglect to diagnose comorbid FM may misdirect treatments.