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Arthritis Care Res (Hoboken). Author manuscript; available in PMC 2024 Feb 1.
Published in final edited form as:
Arthritis Care Res (Hoboken). 2023 Feb; 75(2): 231–239.
Published online 2022 Sep 10. doi:10.1002/acr.24762
PMCID: PMC8807743
NIHMSID: NIHMS1730052
PMID: 34338449
Bryant R. England, MD, PhD,1 Huifeng Yun, PhD,2 Lang Chen, PhD,2 Jared Vanderbleek, PharmD,2 Kaleb Michaud, PhD,1,3 Ted R. Mikuls, MD, MSPH,1 and Jeffrey R. Curtis, MD, MS, MPH2
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Abstract
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 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 (RAPID3 cohort) and 5,733 (CDAI cohort) patients with 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 Caucasian predominant, and on average received medications from 6–7 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 OR 0.95 [95% CI 0.91–0.98] RAPID3 cohort; OR 0.94 [95% CI 0.90–0.99] CDAI cohort). Those with the highest burden of multimorbidity had the lowest odds of achieving target RA disease activity (OR 0.54 [0.34–0.85] RAPID3 cohort; OR 0.65 [0.37–1.15] CDAI cohort).
Conclusions:
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.
INTRODUCTION
A treat-to-target management approach for rheumatoid arthritis (RA) is endorsed by both the American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) guidelines for the management of RA (1, 2). Despite these recommendations, adherence to a treat-to-target management approach outside of clinical trial settings is suboptimal (3, 4). Several factors have been identified that contribute to the challenge of adhering to a treat-to-target strategy in real-world settings, such as older age, current and prior therapies, patient preferences, and discordance between provider assessment of RA disease activity and RA disease activity scores (4–6).
Real-world patients with RA are frequently multimorbid, meaning they are afflicted with more than one chronic condition in addition to their arthritis. While a growing public health problem among the general population, the rates of multimorbidity and its progression are disproportionately accelerated in RA compared to those without RA (7). Long-term complications that can result from multimorbidity include premature mortality, physical disability, and decreased quality of life (8–10). Multimorbidity may also impact RA management. In an international, cross-sectional study, greater multimorbidity was associated with lower odds of receiving treatment with biologic disease-modifying anti-rheumatic drugs (DMARDs) (11). In a separate single-center cohort of patients with RA initiating any DMARD, greater multimorbidity was associated with lower odds of achieving remission or low disease activity (12). In another single-center cohort, RA patients with multimorbidity initiated biologic DMARDs approximately one year later on average than those without multimorbidity and were less likely to achieve remission (13).
Together, these studies have demonstrated the potential for multimorbidity to influence treatment selection and response in RA. However, it remains incompletely understood how multimorbidity affects the adherence to and success of the treat-to-target management approach, particularly among large, diverse, real-world RA populations. Therefore, the objectives of this study were to determine whether multimorbidity is associated with the likelihood of initiating a new therapy in the context of inadequately controlled RA and, among those initiating a new therapy, to achieve target disease activity thresholds among a national real-world patient cohort. We hypothesized that greater multimorbidity would be associated with lower odds of making RA treatment changes and achieving treatment targets.
PATIENTS & METHODS
Study Design & Participants
We conducted a retrospective cohort study using the Rheumatology Informatics System for Effectiveness (RISE) registry. The RISE registry is a rheumatology-specific Qualified Clinical Data Registry developed and maintained by the ACR (14). It represents the largest electronic health record-enabled rheumatology registry in the U.S. with more than 1,000 rheumatology providers and 2.4 million patients. RISE data represents routine clinical care and is automatically extracted from all rheumatology patients’ records at participating practices. As a result, RISE provides a diverse patient population while simultaneously reducing the potential for selection bias. Data for this study were collected from January 2016 through July 2018, and all data were analyzed by a RISE data analytic center in a de-identified fashion. The institutional review board at the University of Alabama at Birmingham approved this study.
Within the RISE registry, we identified patients >18 years of age, with ≥2 encounters for RA based on International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes (M05.X and M06.X [excluding M06.1 and M06.4]). We selected patients with persistently active RA, operationalized as those who had two consecutive visits in moderate or high disease activity, as defined by either the Clinical Disease Activity Index (CDAI) (15) or Routine Assessment of Patient Index Data 3 (RAPID3) (16), and did not initiate a new therapy between those two visits (Figure 1, ,A).A). For analyses assessing the achievement of target disease activity thresholds, we additionally required the initiation of a new DMARD within the 90 days following the second visit and ≥1 follow-up visit in the next 365 days with collection of the same RA disease activity measure (Figure 1, ,B).B). The requirement of a new initiation within 90 days was included to maximize capture of new DMARD initiations while preserving a sufficient observation period for follow-up post-treatment disease activity measurements.
Figure 1.
Overview of study design.
Depiction of study design for the overall cohort (A) assessing new treatment initiation and the sub-cohort (B) assessing achieving low disease activity or remission among patients who initiated a new therapy.
Abbreviations: RADAM, rheumatoid arthritis disease activity measure
Multimorbidity Assessment
We assessed multimorbidity through the RxRisk, a validated medication-based measure of chronic diseases that consists of 46 categories of chronic conditions (17–19). Initially developed as the Chronic Disease Score in 1992 (20), it has been periodically updated as new medications have become available and renamed the RxRisk score (19). RxRisk scores are predictive of healthcare costs (21, 22) and mortality (17, 18) as well as associated with DMARD use in a RA cohort (23). We excluded the following RxRisk categories that included medications frequently used in the treatment of RA, reflective of RA status, or that might be related to misclassification from RA: inflammation/pain, steroid-responsive disease, pain, transplant, gout, and psoriasis. Medication data from all prior visits in RISE were utilized to construct the RxRisk score anchored at the ‘index date’, corresponding to the second consecutive visit in moderate to high disease activity (Figure 1). All available pre-index date data was used since resolution of chronic conditions is infrequent and discontinuation of medications could not be reliably determined in this dataset.
Medications in RISE data were recorded according to a variety of drug coding systems including National Drug Codes, Generic Product Identifier codes, RxNorm codes, and free text, and were normalized to Drug Concept Unique Identifiers using the RxNorm application program interface (24). These were subsequently mapped to updated RxRisk categories through manual review by members of the research team with clinical expertise in rheumatology, pharmacology, and epidemiology (HY, JC, JV). Of 8,697 unique drug concept ingredients, 110 (1.3%) mapped to more than one RxRisk category. A RxRisk category was considered present if there was documentation (e.g. medication reconciliation, medication prescription) of a medication from that RxRisk category during at least one prior visit. Medications were used to classify multimorbidity rather than relying on ICD-9/10 diagnoses (and measures based on diagnosis codes including the multimorbidity index (9)), since diagnostic codes for non-rheumatologic conditions have poor sensitivity in a data source like RISE that consists of only diagnoses, procedures, and other data recorded by rheumatologists (25). Medication-based identification of chronic diseases has been demonstrated to address this gap, improving the sensitivity for capturing prevalent chronic conditions in the RISE registry (25).
Study Outcomes
The two separate study outcomes included the initiation of a new DMARD (overall cohort, A) and achieving low disease activity or remission (i.e. CDAI ≤10 in CDAI cohort, RAPID3 ≤6 in RAPID3 cohort) among the sub-cohort (B). A new DMARD initiation was considered to have occurred if a DMARD not previously documented for that patient in the RISE registry was recorded at the index date or within the following 365 days. In the overall cohort, the primary outcome was met by any new DMARD initiation while the secondary outcome was specific classes of DMARDs (conventional synthetic [cs] DMARDs, tumor necrosis factor inhibitors [TNFi], non-TNFi bDMARDs, and targeted synthetic [ts] DMARDs).
In the sub-cohort, achieving low disease activity or remission was assessed throughout the following 365 days using the same RA disease activity measure that fulfilled eligibility criteria and established thresholds for low disease activity and remission (26). Because patients may have multiple visits and RA disease activity scores during the one-year follow-up window, we selected the first RA disease activity score for each patient in the primary analysis and the last RA disease activity score for each patient in the sensitivity analysis. To prevent selection bias related to treatment continuation, patients were not required to be on the newly initiated DMARD at the time of follow-up RA disease activity measurement.
Covariates
Covariates were measured as of the index date and selected a priori for their potential to act as a confounder by being associated with multimorbidity and study outcomes. Covariates in both analyses included demographics (age, sex, race, geographic region), insurance status, number of visits in the RISE registry, RA autoantibody seropositivity, number of prior DMARDs (by DMARD class), and oral glucocorticoid use in the prior 365 days. Since obesity was considered an aspect of multimorbidity, we did not include body mass index. Additional covariates included in the assessments of achieving target disease activity thresholds were disease activity category at the index date (moderate or high disease activity by RAPID3 or CDAI) and the DMARD class being initiated (cDMARD, TNFi, non-TNFi bDMARD, or tsDMARD). Missing RA autoantibody status was considered a separate category, and results were unchanged when RA autoantibody status was removed from models (data not shown).
Statistical Analysis
Descriptive statistics of the overall cohorts and treatment initiation sub-cohorts were stratified by RA disease activity measure. Patients with valid CDAI and RAPID3 measures could contribute to both cohorts (i.e. cohorts were not mutually exclusive). Multivariable logistic regression models were used to assess the association of multimorbidity with treatment initiation and achieving target disease activity thresholds, adjusting for the aforementioned covariates. Multivariable linear regression models assessed the association of multimorbidity with change in RA disease activity measures, since these dependent variables were approximately normally distributed. The RxRisk was modeled as both a continuous (per 1-unit change in RxRisk score) and categorical variable (0–2 [referent], 3–6, 7–9, and ≥10 RxRisk categories; chosen to approximate quartiles). All analyses were completed using SAS v9.4.
RESULTS
Within the RISE registry, we identified 15,626 and 5,733 patients with active RA for the RAPID3 and CDAI cohorts (A). There were 2023 (10.5%) patients represented in both cohorts. Of the total patients, 1,558 (RAPID3) and 834 (CDAI) initiated new treatments within 90 days of their 2nd visit, and thus were eligible for sub-cohort analyses of achieving target RA disease activity thresholds (B). Patient characteristics for each study cohort (RAPID3 and CDAI), including the treatment sub-cohorts, are shown in Table 1. Patients were middle-aged, female and Caucasian predominant. Patients generally resided in the Midwest or South Regions of the U.S. and most often had Medicare or private insurance. On average, patients received medications from approximately 6–7 RxRisk categories. The most frequent RxRisk categories included in the multimorbidity burden assessment were hypertension (57.6–63.8%) and congestive heart failure (53.1–59.7%) (Supplemental Table 1). Characteristics of the patients in the RAPID3 and CDAI cohorts were alike, and patients in the treatment initiation sub-cohorts were generally similar to the overall cohorts, with the exception of having a younger age and greater use of both cDMARDs and oral glucocorticoids.
Table 1.
Baseline patient characteristics by study cohort
| Overall cohort (A) | Initiation sub-cohorts (B)* | |||
|---|---|---|---|---|
| RAPID3 (n=15,626) | CDAI (n=5,733) | RAPID3 (n=1,558) | CDAI (n=834) | |
| Demographics | ||||
| Age, years | 63.6 (13.1) | 62.6 (12.7) | 59.3 (13.5) | 58.9 (12.5) |
| Female sex, % | 80.4 | 82.2 | 83.7 | 82.4 |
| Race, % | ||||
| White | 68.5 | 62.8 | 66.0 | 57.7 |
| Black | 11.3 | 7.8 | 11.6 | 8.9 |
| Other/Missing/Unknown | 20.2 | 29.5 | 22.5 | 33.5 |
| Region | ||||
| Midwest | 33.5 | 39.4 | 33.0 | 33.0 |
| Northeast | 6.7 | 2.2 | 6.4 | 4.3 |
| South | 50.8 | 45.9 | 52.6 | 49.2 |
| West | 6.2 | 11.9 | 6.4 | 12.9 |
| Insurance status | ||||
| Medicare | 40.8 | 42.0 | 29.4 | 31.4 |
| Medicaid | 3.6 | 5.2 | 3.3 | 4.9 |
| Private | 30.4 | 35.2 | 33.6 | 39.6 |
| Other/None/Unknown | 25.1 | 17.7 | 33.7 | 24.1 |
| Multimorbidity | ||||
| Mean # RxRisk categories | 6.9 (3.9) | 6.7 (4.0) | 6.4 (3.9) | 6.2 (4.1) |
| ≥5 RxRisk categories, % | 71.0 | 68.0 | 65.5 | 62.8 |
| # RxRisk categories, % | ||||
| 0–2 | 15.5 | 18.1 | 19.3 | 23.1 |
| 3–6 | 31.0 | 30.5 | 33.3 | 31.1 |
| 7–9 | 27.5 | 26.5 | 25.3 | 22.7 |
| ≥10 | 26.0 | 24.9 | 22.1 | 23.1 |
| RA Seropositivity | ||||
| Positive | 18.3 | 15.6 | 22.1 | 18.6 |
| Negative | 17.9 | 12.9 | 22.1 | 14.3 |
| Missing | 63.8 | 71.5 | 55.8 | 67.1 |
| Baseline Medications** | ||||
| cDMARDs, % | 41.2 | 46.3 | 52.3 | 52.9 |
| bDMARDs, % | ||||
| TNFi | 28.1 | 29.4 | 33.1 | 31.9 |
| Non-TNFi | 19.4 | 22.3 | 18.9 | 21.9 |
| tsDMARDs, % | 4.6 | 7.2 | 5.4 | 7.2 |
| Oral steroids, % | 39.3 | 41.5 | 54.7 | 55.8 |
| DMARDs Newly Initiated*** | ||||
| Any new DMARD, % | 23.4 | 31.1 | 100 | 100 |
| cDMARDs, % | 7.5 | 9.4 | 35.2 | 31.5 |
| TNFi, % | 9.9 | 12.7 | 45.1 | 45.6 |
| Non-TNFi bDMARD, % | 6.9 | 9.8 | 32.2 | 34.5 |
| tsDMARDs, % | 3.1 | 6.8 | 18.2 | 22.7 |
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Values mean (SD) unless otherwise indicated
*Patients initiating a new DMARD while in moderate or high disease activity with ≥1 visit in the following 365 days (see Figure 1)
**Baseline medications were not mutually exclusive
***DMARD prescribed in the following 365 days (overall cohort, A) or 90 days (initiation sub-cohorts, B) that has not been previously prescribed. These are not mutually exclusive with 5.7% (RAPID3) and 8.2% (CDAI) of overall cohorts and 8.7% (RAPID3) and 8.9% (CDAI) of treatment initiation sub-cohorts initiating more than 1 DMARD.
Initiation of New Treatments in Active RA
New treatments were initiated in 23.4% and 31.1% of active RA patients in the RAPID3 and CDAI cohorts, respectively (Table 1). TNFi were the most common newly initiated therapy (9.9% and 12.7% of RAPID3 and CDAI cohorts). A greater multimorbidity burden was not associated with the initiation of a new therapy during follow-up (per 1-unit RxRisk OR 1.00 [95% CI 0.99–1.01] in RAPID3 cohort; OR 1.00 [95% CI 0.99–1.02] in CDAI cohort; Supplemental Table 2). Similarly, as a categorical variable, a greater burden of multimorbidity was not significantly associated with the odds of DMARD initiation (Figure 2). Results were consistent when multimorbidity burden, RxRisk as either a continuous or categorical measure, was evaluated as a predictor of the initiation of cDMARDs, TNFi, non-TNFi bDMARDs, and tsDMARDs separately (Table 2 and Supplemental Table 2).
Figure 2.
Association of multimorbidity with DMARD initiation in active rheumatoid arthritis.
Forest plot illustrating the association of multimorbidity categories by RxRisk with the initiation of a new disease-modifying anti-rheumatic drug in active rheumatoid arthritis.
Abbreviations: bDMARDs, biologic disease-modifying anti-rheumatic drugs; CI, confidence interval; csDMARDs, conventional-synthetic disease-modifying anti-rheumatic drugs; OR, odds ratio
Table 2.
Association of multimorbidity burden with initiation of specific disease-modifying anti-rheumatic drug therapies*
| cDMARDs initiation | TNFi initiation | Non-TNFi bDMARD initiation | tsDMARD initiation | |
|---|---|---|---|---|
| RAPID3 cohort (n=15,626) | ||||
| RxRisk score | ||||
| 0–2 | Ref | Ref | Ref | Ref |
| 3–6 | 1.04 (0.87, 1.25) | 1.02 (0.87, 1.20) | 0.93 (0.77, 1.14) | 1.11 (0.86, 1.43) |
| 7–9 | 1.03 (0.84, 1.25) | 1.06 (0.89, 1.26) | 0.94 (0.76, 1.15) | 0.97 (0.74, 1.29) |
| ≥10 | 1.03 (0.84, 1.28) | 1.04 (0.86, 1.26) | 0.84 (0.68, 1.05) | 0.99 (0.74, 1.33) |
| CDAI cohort (n=5,733) | ||||
| RxRisk score | ||||
| 0–2 | Ref | Ref | Ref | Ref |
| 3–6 | 1.01 (0.78, 1.31) | 1.06 (0.85, 1.34) | 0.96 (0.74, 1.25) | 1.03 (0.75, 1.42) |
| 7–9 | 1.13 (0.85, 1.49) | 0.91 (0.71, 1.18) | 0.88 (0.66, 1.18) | 0.92 (0.65, 1.32) |
| ≥10 | 1.06 (0.78, 1.44) | 1.13 (0.87, 1.48) | 1.10 (0.82, 1.48) | 0.84 (0.57, 1.23) |
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Values odds ratio (95% confidence interval). Frequency of specific DMARD initiations provided in Table 1.
Models adjusted for age, sex, race, U.S. region, insurance status, seropositivity, number of visits, number of prior csDMARDs, number of prior bDMARDs, number of prior tsDMARDs, and oral steroids
*Initiation of new DMARDs assessed during 365 days of follow-up
Abbreviations: CDAI, Clinical Disease Activity Index; cDMARDs, conventional DMARD; bDMARD, biologic DMARD; RAPID3, Routine Assessment of Patient Index Data 3; TNFi, tumor necrosis factor inhibitor; tsDMARD, targeted synthetic DMARD
Achieving Target RA Disease Activity Thresholds
Mean (SD) time between treatment initiation and follow-up disease activity measurement was 146 (53) and 144 (53) days in the RAPID3 and CDAI cohorts, respectively. In the RAPID3 and CDAI cohorts, 23.5% and 23.4% of patients initiating a new therapy achieved low disease activity or remission, respectively, at the first follow-up measurement. A greater multimorbidity burden was independently associated with lower odds of achieving target disease activity thresholds (per 1-unit RxRisk OR 0.95 [95% CI 0.91–0.98] in the RAPID3 cohort; OR 0.94 [95% CI 0.90–0.99] in the CDAI cohort; Supplemental Table 2). Those with the highest burden of multimorbidity (≥10 RxRisk categories, i.e. the highest quartile) had decreased odds of achieving low disease activity or remission compared to those with the lowest burden of multimorbidity (OR 0.54 [95% CI 0.34–0.85] in the RAPID3 cohort; OR 0.65 [95% CI 0.37–1.15] in the CDAI cohort; Figure 3). Sensitivity analyses using the last RA disease activity measure during the follow-up period were consistent with the primary analyses (Supplemental Table 3).
Figure 3.
Association of multimorbidity with achieving target disease activity thresholds after initiating a new treatment in active rheumatoid arthritis.
Forest plot illustrating the association of multimorbidity categories by RxRisk with the achievement of low disease activity or remission among rheumatoid arthritis patients initiating a new disease-modifying anti-rheumatic drug.
Abbreviations: bDMARDs, biologic disease-modifying anti-rheumatic drugs; CI, confidence interval; csDMARDs, conventional synthetic disease-modifying anti-rheumatic drugs; OR, odds ratio; TNFi, tumor necrosis factor inhibitor; tsDMARDs, targeted-synthetic disease-modifying anti-rheumatic drugs
Greater multimorbidity burden was also independently associated with higher disease activity scores at follow-up (change in RA disease activity score per 1-unit RxRisk β 0.10 [95% CI 0.01–0.18) in the RAPID3 cohort; β 0.25 [95% CI 0.05–0.45] in the CDAI cohort). Absolute mean change in RAPID3 for those with a RxRisk of 0–2 was −2.8 (95% CI −3.7, −1.8) and for CDAI was −6.4 (95% CI −8.6, −4.3). Improvement in RA disease activity was significantly attenuated for patients with the highest burden of multimorbidity (≥10 RxRisk categories) (β 1.40 (95% CI 0.38–2.42) in the RAPID3 cohort; β 2.83 (95% CI 0.46–5.20) in the CDAI cohort; Table 3).
Table 3.
Association of multimorbidity with change in rheumatoid arthritis disease activity measures after treatment initiation.
| Δ RAPID3 (n=1,558) | Δ CDAI (n=834) | |
|---|---|---|
| RxRisk Score | ||
| 0–2 | Ref* | Ref* |
| 3–6 | 0.90 (0.05, 1.75) | 0.86 (−1.16, 2.88) |
| 7–9 | 0.77 (−0.17, 1.70) | 1.81 (−0.41, 4.04) |
| ≥10 | 1.40 (0.38, 2.42) | 2.83 (0.46, 5.20) |
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Values Beta (95% confidence interval). Higher (i.e. more positive) values represent less improvement in disease activity at follow-up.
*Mean change in RAPID3 for RxRisk 0–2 was −2.8 (95% CI −3.7, −1.8) and mean change in CDAI for RxRisk 0–2 was −6.4 (95% CI −8.6, −4.3).
Models adjusted for age, sex, race, U.S. region, insurance status, seropositivity, number of visits, oral steroids, number of prior csDMARDs, number of prior bDMARDs, number of prior tsDMARDs, baseline disease activity category, treatment being initiated (csDMARD, TNFi, non-TNFi bDMARD, tsDMARD)
Abbreviations: CDAI, Clinical Disease Activity Index; RAPID3, Routine Assessment of Patient Index Data 3
DISCUSSION
In this real-world cohort of patients with active RA, multimorbidity was not associated with the likelihood of initiating a new DMARD. In contrast, multimorbidity was independently associated with a lower likelihood of achieving target disease activity thresholds (low disease activity or remission) among patients initiating a new therapy. Thus, our findings illustrate that multimorbidity is an important patient characteristic to consider when evaluating the potential implementation of a treat-to-target management strategy for RA in real-world settings, and in considering future quality measures that assess the proportion of a rheumatologists’ RA patients that achieve the treat-to-target goals of low disease activity or remission. Recognizing the rising prevalence of multimorbidity, these findings highlight the need for management strategies targeted at multimorbid patients with RA as well as comparative effectiveness and safety studies in multimorbid RA populations. Risk-adjustment of quality measures that hold rheumatologists accountable for target disease activity measures in RA should also take multimorbidity into account.
Although international guidelines for the management of RA endorse a treat-to-target strategy (1, 2), it has been recognized that in real-world settings, adherence to this approach may be suboptimal. Indeed, we observed that the minority of patients with persistently active RA in this diverse real-world cohort initiated a new therapy during follow-up, as previously reported from the RISE registry (4). While some patient and provider factors associated with treat-to-target adherence have been previously identified (3–6, 27), our study is among the first to comprehensively assess multimorbidity as a determinant of treatment escalation and achieving treatment targets, core principles of the treat-to-target management strategy. Using the validated RxRisk to optimally capture chronic conditions in the RISE registry (25), we did not find multimorbidity burden to be associated with the likelihood of initiating new therapies in active RA. While other studies have suggested multimorbidity is associated with less frequent, or delayed, bDMARD use (11, 13), multimorbidity burden did not affect the likelihood of initiating specific DMARDs, including TNFi, non-TNFi bDMARDs, or tsDMARDs in this study. There are several study characteristics that may explain these findings. Most notably, our study was uniquely designed as a cohort study of active RA patients who would be candidates for treatment escalation according to a treat-to-target management approach while other studies were cross-sectional and included patients with lower disease activity (11, 13). Other differences between studies included the patient populations examined (U.S. vs. international), number of sites (multi-center vs. single-center), method of multimorbidity assessment (RxRisk (17, 18) vs. Multimorbidity Index (9) vs. select chronic conditions), and method of identifying chronic conditions (medications vs. questionnaire vs. medical record documentation). Notably, more patients initiated a DMARD in the CDAI cohort (31.1%) compared to the RAPID3 cohort (23.4%), which may reflect providers weighting of formal joint counts in their decision to change treatment.
While multimorbidity was not associated with treatment initiation, it was significantly associated with treatment response. Those with the highest burden of multimorbidity had the lowest odds of achieving low disease activity or remission after initiating a new DMARD. While this finding did not reach statistical significance in the CDAI cohort due to a smaller sample size, our conclusions are further supported by the finding of significantly less improvement in RA disease activity (RAPID3 and CDAI) during follow-up in those with the greatest burden of multimorbidity. Although the mean improvement in RAPID3 and CDAI scores were 2.8 and 6.4 units, respectively, for those with an RxRisk of 0–2, scores were on average 1.4 (RAPID3) and 2.8 (CDAI) units higher in those with an RxRisk ≥10. These findings are consistent with those from previous reports including a cohort of RA patients with varying levels of baseline RA disease activity who initiated a new DMARD (12) as well as a separate cohort of patients initiating biologic DMARDs (13). The consistent observation of poorer treatment response among multimorbid patients with RA across studies with different patient populations and variable multimorbidity assessments supports the conclusion that multimorbidity represents a key patient construct that influences treatment response and the ability to reach target RA disease activity thresholds as part of a treat-to-target management strategy. Moreover, these findings emphasize the need to investigate the effectiveness and safety of RA therapies specifically in multimorbid patients with RA. Recognizing the poorer response to therapies observed in this study and that multimorbid patients are at higher risk for adverse effects such as infection (28), it is expected the benefit-to-risk ratio for DMARDs may differ according to the extent of patients’ multimorbidity.
With the rising prevalence of chronic diseases and multimorbidity worldwide, the development of novel strategies to enhance the management of multimorbid patients with RA is crucial. In the general population, interventions targeted at multimorbidity have included case management, multidisciplinary care, and patient-centered care models (29, 30). Unfortunately, the efficacy of such interventions on patient outcomes, such as quality of life, have been variable. Specifically in rheumatology, a nurse-led program improved screening and management of select chronic diseases in patients with stable RA (31, 32). While RA disease activity and functional status were not impacted over 6 months, fewer patients randomized to this program intensified DMARD therapy compared to those randomized to a self-assessment program (32). Recently, an expert panel provided recommendations for improving the quality of care for patients with RA and associated comorbidities (33). This included the need for comorbidity assessments early in the RA disease course and comorbidity management strategies (e.g. enabling self-management) in established RA. However, the onset of multimorbidity appears early in the natural history of RA (7), so such strategies likely need implemented immediately upon diagnosis and the initiation of DMARD treatment. Developing and delivering interventions for multimorbid patients with RA remains a priority area for future study.
There is currently no reference standard for assessing multimorbidity, and the distinction between multimorbidity and comorbidity remains ambiguous when investigating a population with a chronic disease, such as RA. Thus, it remains important to validate these findings in similarly diverse cohorts using alternate methods of multimorbidity assessment (e.g. diagnostic code-based algorithms), develop novel, comprehensive measures of multimorbidity, and ultimately establish consensus on the chronic conditions to be included in multimorbidity measures. Novel measures of multimorbidity may focus on unique patterns of multimorbidity. For example, a somatization phenotype consisting of depression, anxiety, fibromyalgia, and neuropathic pain was associated with poorer response to certolizumab pegol in a large clinical trial (34). In that same study, in which RA patients were randomized to assessment and management by the RAPID3 vs. the CDAI, disease activity assessment in the RAPID3 arm was less responsive (35) and the somatization phenotype demonstrated less discrimination with this measure (34), suggesting that multimorbidity may impact the choice and performance of disease activity measures, perhaps related to an impact on patient global assessment values (36). The findings from our study will serve as a valuable and clinically relevant benchmark when evaluating novel multimorbidity measures in future efforts.
There are limitations to this study. We assessed multimorbidity using a medication-based index of chronic conditions (RxRisk) rather than traditional medical diagnoses and multimorbidity measures based on medical diagnoses. This provides less granularity on specific diagnoses and may result in misclassification of specific chronic conditions, but even in the context of potential misclassification, represents a validated tool for broadly assessing comorbidity or multimorbidity (17–22). This assessment remains distinct from polypharmacy because multimorbidity burden was defined by the number of different medication categories, rather than the number of medications. There may be some misclassification of multimorbidity related to a medication contributing to multiple RxRisk categories, but only 1.3% of unique ingredients were mapped to more than one RxRisk category. Our analyses focused on evaluating multimorbidity broadly, but there may be specific chronic conditions or patterns of multimorbidity that differentially affect treatment initiation and achieving treatment targets. Due to left censoring of the data prior to the study period, the timing and number of prior DMARD initiations is unknown and could have contributed to the infrequency of new DMARD initiation. The reasons for not initiating new DMARDs in active RA were not available for this study, and we were unable to evaluate DMARD or biologic dose escalations. The timing of follow-up disease activity measurement after new treatment initiation was not standardized, reflecting a real-world setting, but our findings were consistent using both the first and last RA disease activity measure during the follow-up window. Component scores of composite RA disease activity measures were not available for these analyses, which will require future studies. While we were able to adjust for many potential confounders, data was not available to account for health behaviors such as smoking or alcohol use, and BMI was not included since this is reflective of multimorbidity and may result in overadjustment. Finally, medication discontinuation could not be determined in this dataset.
In conclusion, we used a large, real-world registry of diverse patients with RA to evaluate whether multimorbidity influenced new treatment initiation and treatment response. A greater burden of multimorbidity was not independently associated with the initiation of new DMARDs in active RA but was significantly associated with lower odds of achieving target disease activity thresholds. Poorer response to treatment escalation related to multimorbidity emphasizes the need for comparative effectiveness studies and novel management strategies in multimorbid patients with RA to improve long-term patient outcomes.
SIGNIFICANCE & INNOVATIONS
The prevalence of multimorbidity (multiple chronic conditions) is rising in patients with rheumatoid arthritis (RA) and can negatively impact long-term outcomes.
It is poorly understood how multimorbidity affects adherence to and the success of a treat-to-target management strategy in diverse real-world settings.
Using the national Rheumatology Informatics System for Effectiveness (RISE) registry, we found a greater burden of multimorbidity to be associated with lower odds of achieving target RA disease activity thresholds among patients with active RA initiating a new DMARD.
Supplementary Material
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Funding:
This work was supported by the Rheumatology Research Foundation Scientist Development Award (BRE), Great Plains IDeA-CTR Scholars Award (BRE), and the NIH (P30AR072583, JRC). Dr. Mikuls is supported by the NIH/NIGMS (U54GM115458) and NIAAA (R25AA020818). Drs. Yun and Curtis are supported by NIH/NIAMS (P30AR072583). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Disclosures:
HY, LC, JV none. BRE has received honoraria from Boehringer-Ingelheim. KM has received research funding from Pfizer. TRM has received research funding from Bristol Myers Squibb and Horizon Therapeutics and served as a consultant for Pfizer, Gilead, Sanofi and Horizon Therapeutics. JRC has received research funding from Abbvie, Amgen, Bristol Myers Squibb, Corevitas, Janssen, Lilly, Novartis, Myriad, Pfizer, Sanofi, Setpoint, Schipher, UCB and served as a consultant for Abbvie, Amgen, Bristol Myers Squibb, Corevitas, Janssen, Lilly, Novartis, Myriad, Pfizer, Sanofi, Setpoint, Schipher, UCB.
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