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Abstract
Objectives To investigate the impact of disease activity and treatment with disease-modifying antirheumatic drugs (DMARDs) on all-cause mortality in patients with rheumatoid arthritis and prevalent interstitial lung disease (RA-ILD).
Methods Patients with RA-ILD were selected from the biologics register Rheumatoid Arthritis: Observation of Biologic Therapy (RABBIT). Using time-varying Cox regression, the association between clinical measures and mortality was investigated. The impact of DMARDs was analysed by (1) Cox regression considering cumulative exposure (ie, treatment months divided by total months) and (2) time-varying Cox regression as main approach (treatment exposures at monthly level).
Results Out of 15 566 participants, 381 were identified as RA-ILD cases with 1258 person-years of observation and 2.6 years median length of follow-up. Ninety-seven patients (25.5%) died and 34 (35.1%) of these were not receiving DMARD therapy at the time of death. Higher inflammatory biomarkers but not swollen and tender joint count were significantly associated with mortality. Compared with tumour necrosis factor inhibitors (TNFi), non-TNFi biologic DMARDs (bDMARDs) exhibited adjusted HRs (aHRs) for mortality below 1, lacking statistical significance. This finding was stable in various sensitivity analyses. Joint aHR for non-TNFi biologics and JAKi versus TNFi was 0.56 (95% CI 0.33 to 0.97). Receiving no DMARD treatment was associated with a twofold higher mortality risk compared with receiving any DMARD treatment, aHR 2.03 (95% CI 1.23 to 3.35).
Conclusions Inflammatory biomarkers and absence of DMARD treatment were associated with increased risk of mortality in patients with RA-ILD. Non-TNFi bDMARDs may confer enhanced therapeutic benefits in patients with RA-ILD.
- arthritis, rheumatoid
- biological therapy
- inflammation
- risk factors
- tumor necrosis factor inhibitors
Data availability statement
No data are available.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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WHAT IS ALREADY KNOWN ON THIS TOPIC
High disease activity appears to increase the risk of death in patients with rheumatoid arthritis (RA) and interstitial lung disease (ILD); however, previous studies are either based on small sample sizes or selected populations such as male patients, and available results are contradictory.
Recent reviews state the low and limited evidence about disease-modifying antirheumatic drugs (DMARDs) treatment of patients with RA-ILD.
Some studies suggest that DMARD treatments such as abatacept and rituximab could be superior to therapy with tumour necrosis factor inhibitors (TNFi), but most of them examine the improvement or stabilisation of RA-ILD and not mortality.
In clinical practice, DMARDs may not be used adequately due to the lack of evidence on treatment effects in RA-ILD.
WHAT THIS STUDY ADDS
We found that disease activity driven by increased systemic inflammation rather than active joints increases the risk of mortality in patients with RA-ILD.
Using tightly monitored register data, we analysed the largest RA-ILD cohort to date including all available DMARDs for the first time simultaneously.
Treatment with interleukin-6 inhibitors, T-cell co-stimulation modulators and B-cell-targeted therapy showed a signal indicating a potential reduction in mortality compared with TNFi.
Patients without DMARD treatment had an increased risk of death.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
To decrease all-cause mortality in patients with RA-ILD, reducing systemic inflammation through appropriate DMARD treatment is of utmost importance.
Non-TNFi biologic DMARDs may offer additional benefits regarding mortality in patients with RA-ILD.
The signals we observed are consistent with current guidelines that favour increased use of these treatments; however, further data and analyses are needed to support the signals described.
Introduction
Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects between 0.5% and 1% of the global population.1 2 RA is characterised by inflammation and synovial proliferation in joints, leading to joint destruction, stiffness and pain.3 In addition to the joint involvement, RA is associated with several extra-articular manifestations, with lung diseases being the most common.4 Interstitial lung disease (ILD) is a significant comorbidity in RA, with reported prevalence between 5%,5 10%6 and up to 60%.4 Known risk factors for RA-ILD comprise male sex, older age, smoking, longer RA duration, elevated rheumatoid factor (RF) and/or cyclic citrullinated peptide antibodies and RA disease activity.5 7–9 Sparks et al reported a twofold increased risk of ILD in patients with moderate/high disease activity versus those with remission/low disease activity.10 ILD can occur at any stage of RA and is associated with substantially increased morbidity and mortality.5 7 8 11
Disease activity is an important predictor of all-cause mortality in patients with RA.12 Dixon et al found in their study about the influence of tumour necrosis factor inhibitors (TNFi) on mortality in patients with RA-ILD that the composite Disease Activity Score in 28 joints (DAS28) was a predictor of mortality in these patients, although disease activity was not the focus of the study.13 In contrast, in the study by Koduri et al, DAS28 failed to be included in the multivariable analysis as a predictor of mortality and erythrocyte sedimentation rate (ESR) was after the adjustment no longer significant.14 The most recent results by Brooks et al reported higher disease activity to be associated with mortality for patients with RA-ILD.15 However, patients in this study consisted mainly of males (93%), which may affect the generalisability of the results as the prevalence of RA is 2–3 times higher in women than in men.16
Disease‐modifying antirheumatic drugs (DMARDs) are the cornerstone of treatment for RA and have shown beneficial effects on joint and systemic inflammation.17 To date, the knowledge regarding the effectiveness and safety of DMARDs in patients with RA-ILD is limited and conflicting.9 18–23 Only few studies examined the association between RA treatment and mortality in patients with RA-ILD.13 24–30 Moreover, optimising RA treatment strategy is complicated by the possible pulmonary toxicity of some DMARDs.18 Recommendations are given in reviews and by national guidelines such as those from the British Society for Rheumatology, the Spanish Society of Rheumatology or the Taiwan Society of Rheumatology, which advise the use of abatacept (ABA) and rituximab (RTX) as first choice but raise concern towards TNFi.19 22 31 However, recent review addresses the limited evidence on which recommendations are based.31 Furthermore, the American College of Rheumatology (ACR) recently released guideline recommends RTX and tocilizumab (TOC) as treatment for RA-ILD.32
It is known that a high comorbidity burden increases the risk of not receiving any DMARDs,33 but there is a lack of information on treatment patterns in patients with RA-ILD, including the impact of non-treatment with DMARDs on mortality. Rodriguez-Rodriguez et al34 showed that in the overall RA population treatment with DMARDs decreases mortality with an adjusted hazard ratio (aHR) of 0.52 (95% CI 0.32 to 0.86) for biologic DMARDs (bDMARDs) and/or conventional synthetic DMARDs (csDMARDs) compared with receiving no therapy.
Further research is needed to understand the complex contribution of disease activity and treatment to survival in patients with RA-ILD. Particularly, to our knowledge, there are no prospective studies investigating the effects of disease activity and treatment, including the use or non-use of DMARDs, on all-cause mortality, adequately accounting for confounders.
Methods
Data source
The German biologics register RABBIT is an ongoing, prospective longitudinally followed cohort of patients with RA according to the ACR 1987 criteria and with age at disease onset >15 years. Patients are included in RABBIT with a new start of a bDMARD, a targeted synthetic DMARD (tsDMARD) or with a csDMARD after at least one prior DMARD therapy. At the time of enrolment, at months 3 and 6, and then every 6 months during the observation period, information is collected from patients and rheumatologists on demographics, clinical status, DMARD treatment regimen including exact start and stop dates, concomitant therapies with glucocorticoids (GC) and adverse events.35 Beyond this, patients document the functional status using the ‘Hannover Functional Status Questionnaire’ (Funktionsfragebogen Hannover (FFbH)). The intended minimal observation period in RABBIT is 5 years, and patients are requested to extend their participation for a further 5 years thereafter. In this context, termination of the study after the fifth year is not considered a dropout.
Patient population
Patients with RA enrolled between January 2007 and June 2021 with an ILD reported by the rheumatologist were selected. Patients were included in the analysis only if they had a chance to receive a second questionnaire during the observation period (online supplemental figure 1). Observation started at the time of ILD reporting (=baseline) and ended at death, dropout or end of follow-up, whichever occurred first (=end of observation).
Supplemental material
Definition of outcome
All-cause mortality was evaluated. Rheumatologists either reported the death of a patient directly on the questionnaire or patients who missed two study visits were followed up according to routine procedures: first, contacting the rheumatologist, second, questioning relatives and third, contacting the local registration office. The physician-reported causes of death were summarised as follows: death due to pneumonia, respiratory disease such as lung fibrosis, malignancy, cardiovascular events, infections including sepsis and unknown causes.
Definition of exposure
Primary exposure was RA disease activity, measured by DAS28, using DAS28-ESR and DAS28-CRP. DAS28-ESR was categorised into remission/low (<3.2), moderate (3.2–5.1) and high (>5.1), and DAS28-CRP into remission/low (<2.9), moderate (2.9–4.6) and high (>4.6).36 Additionally, systemic inflammation measured by ESR/C reactive protein (CRP) and disease activity in the joints defined as swollen and tender joint count (0–28) were analysed separately. Elevated systemic inflammation was defined as ESR >21 mm/hour and CRP ≥5 mg/L, and high systemic inflammation as ESR >50 mm/hour and CRP >30 mg/L. A value of ≥4 was considered as increased number of swollen/tender joints.
Secondary exposure was related to DMARD treatment and was categorised into seven groups: csDMARD (azathioprine, cyclophosphamide, gold, hydroxychloroquine, leflunomide, methotrexate, sulfasalazine), TNFi (adalimumab, certolizumab, etanercept, golimumab, infliximab), interleukin-6 inhibitors (IL6i) (TOC, sarilumab), T-cell co-stimulation modulator (T-cell) (ABA), B-cell-targeted therapy (B-cell) (RTX), Janus kinase inhibitors (JAKi) (baricitinib, filgotinib, tofacitinib, upadacitinib) and no DMARD treatment.
Confounders
A Directed Acyclic Graph (DAG) was created to select necessary confounders for adjustment at baseline. The graph was drawn using DAGitty, a browser-based environment37 (online supplemental figure 2).
For the exposure ‘disease activity’, the minimal sufficient adjustment set for the total effect on mortality contained the following baseline variables (online supplemental figure 2, upper part): age, sex, disease duration of RA, obesity (yes, missing category vs no), smoking (ever vs never), RF and/or anticitrullinated protein antibodies (ACPA) (yes vs no), count of comorbidities (chronic obstructive pulmonary disease, coronary heart disease, heart failure, cerebrovascular disease, chronic liver disease, chronic renal disease, diabetes, malignancy, osteoporosis, depression), history of b/tsDMARD use (yes vs no), current DMARD treatment (seven categories) and GC treatment during the last 6 months (>5 mg/day vs ≤5 mg/day).
For the exposure ‘treatment’, the variable ‘current DMARD treatment’ of the above adjustment set was replaced by ESR, and the variables smoking and obesity were removed from the set, as explained in online supplemental figure 2, lower part.
Statistical analyses
Patient characteristics are presented using descriptive statistics (numbers, percentages, mean, median, SD) and are reported with missing values (online supplemental tables 2 and 3).
Analyses for exposure (1): To visualise the development of disease activity and systemic inflammation, means and categorised values were mapped up to 5 years starting from baseline as well as 5 years before the end of observation. Associations of DAS28-ESR, DAS28-CRP, ESR, CRP and joint counts with mortality were evaluated in separate models using multivariable time-varying Cox regression. The effect of these variables was analysed including them either continuously or categorised, both as time-updated values on monthly level data. To adjust for confounding, variables identified by the DAG were included (online supplemental figure 2, upper part). Due to skewness of CRP, log-transformed values were incorporated into the models.
Analyses for exposure (2): Previous therapies ever received and therapies received within the 6 months prior to ILD are shown in online supplemental tables 4 and 5. To examine the pattern of therapy prescription, Sankey diagrams38 were created for the first 5 years of observation, starting at baseline. Several approaches were used to analyse the impact of treatment on mortality:
Model 1: Cox regression considering cumulative exposure. The cumulative exposure was calculated for each treatment group and per patient as the proportion of observation time exposed to a certain treatment group (range: 0–1) (for further information on the cumulative method, see Meissner et al39). A risk window was applied, considering a patient exposed to b/tsDMARDs for an additional 6 months (for B-cell 12 months instead of 6) after treatment discontinuation (for further information on the risk window method, see Listing et al12).
Model 2 (Main model): like model 1. Instead of calculating cumulative exposure, in this model a Cox regression considering time-varying treatment exposure on a monthly basis was applied.
For these models, therapies were coded hierarchically so that b/tsDMARDs were superimposed on the csDMARD/no DMARD treatment group, for example, if csDMARDs were administered concomitantly with b/tsDMARD, the patient was deemed exposed to b/tsDMARDs in the corresponding month. If a 6-month risk window overlapped with the time at risk in the cs/no DMARD group, the patient was considered exposed to the b/tsDMARD in the respective month. Furthermore, a joint estimator for IL6i, T-cell, B-cell and JAKi versus TNFi was issued and for all models, TNFi were chosen as reference category. In a supplementary analysis (online supplemental table 14), the main model was run with three different reference categories instead of TNFi, which were selected according to the size of the treatment group (csDMARD, B-cell and T-cell). We acknowledge that concurrent exposure to different drugs may occur for some intervals due to the risk window method and prevailing clinical practice.
Additionally, the following multiple regression models were fitted considering the effect of no DMARD treatment versus any DMARD treatment as sole treatment categories:
Model 3A: like model 1.
Model 3B: like model 1 but with a 6-month induction time period in addition to the 6-month risk window for all treatment groups, that is, if death occurred within the first 6 months of a treatment episode (b/ts/csDMARD or no DMARD), this death was not attributed to the started episode. If, on the other hand, death occurred within 6 months after the end of a treatment episode (b/ts/csDMARD or no DMARD), the death was still attributed to the preceding episode. This procedure allowed us to study long-term risks of the absence of drug administration as short ‘no DMARDs episodes’ with <6 months exposure time before the end of the observation are not counted. Further information on the method can be found in online supplemental figure 4.
Model 4A: like model 2.
Model 4B (Main model): like model 3B. Instead of calculating cumulative exposure, in this model a Cox regression considering time-varying treatment exposure on a monthly basis was applied.
Unadjusted estimates of regression models are shown in online supplemental tables 6, 8 and 9. Following the recommendations of epidemiological literature on adjusting for confounding (see to avoid collider stratification bias40), we adjusted all models for baseline confounders, and models with time-varying covariates were conducted as a sensitivity analysis (online supplemental tables 6, 10 and 11). Further to account for the effect of prior therapies as a confounder between therapies at ILD diagnosis and mortality, we performed two sensitivity analyses using the model 2: In the first, we considered therapies ever received and in the second, prescribed therapies within the 6 months prior to diagnosis (online supplemental table 12). In addition, due to the small number of deaths attributable to the lungs, we were only able to consider lung-specific mortality as a sensitivity analysis (online supplemental table 13).
To account for attrition bias, all regression models were weighted with inverse probability of censoring weights (IPCW) for selective dropout, including the variables sex, age, DAS28-ESR and FFbH. Schoenfeld residuals were calculated to check the proportional hazard assumption of the Cox regression models. If the residuals change over time, a violation of the proportional hazard assumption is indicated. This was tested by means of the Pearson’s correlation coefficient between residuals and time to event, including 95% CI.
For regression analyses, missing values were imputed 10 times using multiple imputation and the fully conditional specification method. P values in regression models <0.05 were considered statistically significant. Data analyses were performed with SAS V.9.4.
Patient and public involvement
Results were presented and discussed in the presence of patient research partners of the Targeted Risk Management in Musculoskeletal Diseases network.
Results
Out of 15 566 cohort participants, a comorbid ILD was reported for 381 patients with RA (online supplemental figure 1). The total observed time for 381 patients with ILD was 1258 person-years, and the median length of follow-up was 2.6 years (IQR 1.2–4.7). Fifty-four patients (14.2%) dropped out of the study with a median length until dropout of 2.2 years (IQR 0.6–4.0). At their last study visit, these patients had higher DAS-ESR scores (4.4 vs 3.6) and worse FFbH values (56.7 vs 60.3) than patients remaining in the study. After the fifth year, 84 patients (22.0% of the initial RA-ILD cohort) were still under observation.
A total of 97 patients with ILD (25.5%) died, and precise information on causes of death was obtained in 86.6% (n=84). The leading causes of death were pneumonia, malignancies and cardiovascular diseases. Every second death was related to the lungs, especially to pneumonia, respiratory diseases and lung cancer (online supplemental table 1). Most of the deaths occurred within 5 years after ILD reporting (82/97 (84.5%)).
Exposure 1: disease activity
Based on DAS28-ESR, 21.4% of patients were in low, 36.1% in moderate and 42.5% in high disease activity at baseline (table 1). For 40 patients, the information on DAS28-ESR was missing. Patients with high disease activity were more often women, had worse physical function and poorer patient self-assessment, received more often GC doses >5 mg/day and received fewer prior treatments with b/tsDMARDs despite a comparable disease duration of RA and mean number of comorbidities.
Characteristics at baseline (=first reporting of ILD) stratified by DAS28-ESR categories
Patients who died during follow-up had higher DAS28 and systemic inflammation in the 5 years before death (figure 1). ESR and CRP increased within the last 2 years before death. Looking at the trajectory of disease activity starting from ILD reporting, deceased patients had higher systemic inflammation levels throughout the entire follow-up than surviving patients (online supplemental figure 3).
Mean and categorised DAS28-ESR, DAS28-CRP, ESR and CRP within 5 years prior to the end of observation (death, end of follow-up or dropout) given for patients with ILD who either deceased or survived within the observation time. Upper plots: unimputed and untransformed values as means with 95% CI; lower plots: percentages of patients with DAS28-ESR >5.1, DAS28-CRP >4.6, ESR >50 mm/hour, CRP >30 mg/L; the time is represented in months; # Death, end of follow-up or dropout. CRP, C reactive protein; DAS28, Disease Activity Score in 28 joints; ESR, erythrocyte sedimentation rate; ILD, interstitial lung disease.
An elevation of DAS28 total scores was significantly associated with mortality in patients with RA-ILD (DAS28-ESR aHR 1.17 (95% CI 1.00 to 1.36) per point increase; DAS28-CRP aHR 1.20 (95% CI 1.02 to 1.43) per point increase; table 2), and the effect was especially pronounced in patients with high disease activity (DAS28-ESR >5.1 aHR 2.10 (95% CI 1.16 to 3.81)).
Adjusted HRs of disease activity measures with mortality in RA-ILD
Similarly, higher systemic inflammation levels were associated with death (ESR >50 mm/hour aHR 2.06 (95% CI 1.28 to 3.33) and CRP >30 mg/L aHR 3.26 (95% CI 1.87 to 5.66)). Neither swollen nor tender joint counts alone were found to be associated with mortality. Unadjusted HRs are provided in online supplemental table 6. Results of the sensitivity analysis, which considered time-varying covariates, showed similar results (online supplemental table 6). Across all disease activity exposures investigated, higher age (aHR 1.04 (95% CI 1.01 to 1.06)–aHR 1.04 (95% CI 1.01 to 1.07) per year) and higher number of comorbidities (aHR 1.26 (95% CI 1.08 to 1.47)–aHR 1.31 (95% CI 1.12 to 1.53) per additional comorbidity) were significant predictors of all-cause mortality (online supplemental table 7).
Exposure 2: DMARD treatment
Patient characteristics stratified by seven treatment groups at baseline are shown in table 3. Patients were on average 63.7 (IL6i) to 67.1 (T-cell) years old, and the duration of RA varied between 9.5 (IL6i) and 14.2 (JAKi) years. Patients exposed to JAKi had the smallest proportion of women (36.8%) and the group without DMARDs the highest (63.3%). Patients with B-cell, T-cell and JAKi had higher DAS28-ESR scores (5.0–5.2) and more chronic diseases in addition to RA and ILD (1.5, 1.6 to 1.4, respectively) than patients on other treatments. In contrast, patients with csDMARD therapy or TNFi were less affected overall. Those receiving IL6i had a comparable number of comorbidities as patients with csDMARDs or TNFi (1.3, 1.1 and 1.3, respectively). Patients not exposed to any DMARD differed from other treatment groups in having a less active disease, but a higher number of comorbidities (1.9) and prior treatments (1.8).
Characteristics stratified by treatment group at ILD reporting
The majority of patients with RA-ILD on a non-TNFi-DMARDs or JAKi at ILD diagnosis had been ever treated with TNFi prior to the ILD diagnosis, varying between about 37.7% in csDMARD treated patients and 73.3% in patients with no DMARD treatment at the ILD diagnosis (online supplemental table 4). Many patients were treated with the same therapy in the 6 months prior to ILD diagnosis as at the time of diagnosis and at the same time many patients had csDMARDs or had not received any DMARD therapy during this period (online supplemental table 5). Of the patients who received TNFi therapy in the 6 months prior to ILD, 51 patients remained on therapy and 42 discontinued it. A total of 63 patients started the therapy (online supplemental table 5). Treatment patterns within the first 5 years of follow-up stratified by vital status are shown in figure 2. At the time of ILD reporting, TNFi was the most prescribed therapy in all patients. In patients who survived, patterns differed with a higher proportion of patients exposed to csDMARDs. Eleven of 97 patients who died (11.3%) and 19 of 284 patients who survived (6.7%) had received no DMARD at baseline. Of the 11 deceased patients, 9 remained without DMARD treatment until death and only 6 of the 19 patients without DMARDs at baseline who survived remained without DMARD treatment until the end of the observation.
Treatment pattern within the first 5 years of follow-up stratified by vital status. *Death or end of 5 years after ILD reporting; 14 out of 97 (14.4%) deaths were observed longer than 60 months. B-cell, B-cell-targeted therapy; csDMARD, conventional synthetic disease-modifying antirheumatic drug; DMARD, disease-modifying antirheumatic drug; IL6i, interleukin-6 inhibitor; ILD, interstitial lung disease; JAKi, Janus kinase inhibitor; TNFi, tumour necrosis factor inhibitor; T-cell, T-cell co-stimulation modulator.
Two-thirds (53/78=68%) of the patients with b/tsDMARDs at baseline who died (median follow-up 2.0 years) and 75% (159/212) of the survivors (median follow-up 3.1 years) had remained on their b/tsDMARD during follow-up (change to csDMARD or no DMARD not accounted for).
Results for adjusted models for both approaches are shown in table 4. In the main Cox regression (model 2), aHRs <1 were observed for all DMARDs compared with TNFi, but without statistical significance. Using csDMARDs, B-cell-targeted or T-cell-targeted therapies as reference category in the main model resulted in aHRs for TNFi that were numerically well above 1 but were only significant with B-cell-targeted treatments as reference (online supplemental table 14). In the cumulative approach (model 1), the coefficients pointed in the same direction. Furthermore, if IL6i, T-cell, B-cell and JAKi were combined and compared with TNFi, the aHR was significant protective in model 2 with aHR 0.56 (95% CI 0.33 to 0.97) and in model 1 of the same direction. In addition, the consideration of therapies prior to the ILD reporting led to similar results, with the csDMARD estimate exceeding the threshold of 1 (online supplemental table 12). The results of the analysis of lung-specific mortality differed only marginally from the main results, but the estimates had wider CIs (online supplemental table 13).
HRs of multivariable adjusted regression models stratified by treatment group
A comparison between no DMARD treatment versus any treatment is shown in table 5. By applying an induction period, the risk of no treatment with DMARDs decreased considerably from models 3A/4A to models 3B/4B, with an aHR in model 4A of 3.68 (95% CI 2.33 to 5.80) and in model 4B of 2.03 (95% CI 1.23 to 3.35). In a sensitivity analysis including smoking and obesity in the models, all results remained the same (data not shown). Results of models with time-varying covariates were similar (online supplemental tables 10 and 11).
HRs of multivariable adjusted regression models for the comparison of exposure with no DMARD versus any DMARD
Discussion
The results of this study provide new insights into the relationship between disease activity, RA treatment and mortality in patients with RA-ILD. The findings demonstrate that independently of DMARD treatment, disease activity measured by DAS28 is significantly associated with increased mortality risk. An even more pronounced association was detected with high inflammatory biomarkers. Treatment with any bDMARD other than TNFi was indicative of a potential signal associated with reduced mortality. Furthermore, no DMARD treatment was associated with an increased risk compared with any DMARD treatment. No significant associations could be found for specific DMARD categories.
Over the last two decades, treatment options for RA expanded, and overall RA-related mortality rates decreased markedly.41 42 However, optimal DMARD therapy in patients with RA-ILD with the aim to reduce mortality remains a scientific question. A discussion on the subject of the treatment with TNFi, ABA and RTX has emerged. In 2010, Dixon et al13 addressed this issue and reported that TNFi were not associated with higher mortality compared with csDMARDs in patients with RA-ILD. Palmer et al suggested in 2014 that treatment with RTX rather than with TNFi leads to longer survival, but without significant result.30 Later, Druce et al compared mortality in patients with RA-ILD who received RTX or TNFi as their first biological and stated that the adjusted mortality risk was halved in the RTX cohort, but the difference was not statistically significant.25 Recently, Mena-Vázquez et al28 found higher survival rates in patients treated with bDMARDs other than TNFi (ABA, TOC, RTX) compared with patients treated with TNFi, but results were based on a smaller sample size. As reported by Kelly et al, in a cohort combining 584 patients with RA-ILD from different studies, a longer survival for RTX compared with TNFi could be demonstrated.43 Recent reviews also conclude that TNFi treatment may be disadvantageous in terms of mortality, particularly compared with RTX and ABA.9 20 44 The most recent scientific contribution on the association between mortality and DMARDs is an abstract presented at the 2023 ACR congress.45 In a new-user study of patients with RA-ILD (92% being male) followed in the Veterans Health Administration, TNFi and non-TNFi/JAKi initiators were matched by propensity score (both n=237). Over a 3-year and 1-year follow-up period, respiratory-related hospitalisations, respiratory-related deaths and deaths from all causes were examined. No significant differences in the risk for these outcomes were found between the drug groups. Although when analysing patients who had previously received b/tsDMARDs, a non-significant risk reduction for non-TNFi/JAKi versus TNFi was indicated. Evidence for IL-6 and JAK inhibitors is scarce with conflicting results,9 18 20 43 however both treatments are recommended in Spain if there is inadequate response or contraindication to RTX and ABA.46
Consistent with prior research, our time-varying Cox regression analyses numerically pointed in the direction of reduced risk of mortality among individuals receiving any bDMARD treatment other than TNFi. Cumulative analysis of treatment exposures yielded similar results. The cumulative treatment model assumes that the effect of treatment on mortality is constant over time and reflects the total effect of treatment. However, the cumulative model does neither consider the sequence of therapy episodes nor the time points at which therapy switches occur. In contrast, the well-established time-varying treatment model respects changes in the effect of treatment over time and therefore better captures the dynamic nature of the exposure. Due to a more recent marketing authorisation of JAKi, their duration in our study is limited impeding conclusions. However, there is consistent stability of results for IL6i, T-cell and B-cell treatments across all sensitivity analyses. The observed tendency towards lower mortality may be attributed to a more precise and nuanced modulation of the immune response by these medications in patients with RA-ILD, rather than an overall detrimental effect of TNFi. Given the involvement of autoantibody-mediated neutrophil activation in the development of RA-ILD, bDMARDs that specifically target the adaptive immune responses, such as T-cell-targeted therapy or B-cell-targeted therapy, are promising as these medications play a significant role in reducing ACPA levels.9
A substantial number of deceased patients (35%) had not received any DMARD therapy during the last months before death. No use of any DMARD was associated with a significant increase in mortality risk, similar to the results of Rodriguez-Rodriguez et al34 in an RA population not selected for comorbid ILD. The decision to discontinue DMARD treatment in an individual patient may be due to severe comorbidities and resulting contraindications or to poor survival prospects47 rather than a low RA disease activity. Patients who discontinued treatment shortly before death are likely to make a significant contribution to the mortality risk due to the short time, they were not DMARD exposed, and thus probably cause an overestimation of the risk. This undesired effect could be reduced by including details on severity of comorbidities, but this information is not captured in RABBIT. Instead, we additionally fitted models in which death occurring within the first 6 months of no DMARD treatment was assigned to the previous treatment (model 3/4 B). In the corresponding time-varying Cox regression model, mortality risk dropped to an aHR of 2 but was still statistically significant.
Patients with RA-ILD are at high risk of multimorbidity.48 49 Our study results showed an association between the number of comorbid conditions and mortality risk. Furthermore, we identified high systemic inflammation as a risk factor for mortality which was not indicated by the number of swollen or tender joints. Two possible explanations are offered by Brooks et al15: either systemic features of RA are not adequately captured by joint counts could explain the effect of inflammation on survival, or ILD itself could elevate the inflammation. The risk associated with higher systemic inflammation demonstrates the importance of the treatment decision. While the treat-to-target approach may increase the risk of adverse events on the one hand, it may prevent inflammation-associated comorbidities on the other. The less frequent use of DMARDs in patients with RA-ILD may be due to fear of pulmonary toxicity.43 In patients with RA-ILD, the treatment discontinuation rate was higher than in those without ILD (33% vs 25%), and the most common reasons for withdrawal in RA-ILD were adverse events (45%) and lack of efficacy (36%).50 Our study supports the recommendation to use DMARDs if no life-threatening disease is implying a contraindication. However, the trade-off between adverse events, comorbidities and high inflammation is complex and further studies are needed to look more closely at the discontinuation of therapies and the reasons for it, and to examine in more detail which patients might benefit from continued prescription.
A strength of our study is the detailed information at multiple, predefined time points during follow-up, both on DMARD treatment and clinical measures. This enabled us to use time-varying regression models to account for changes in patient characteristics such as disease activity and inflammation, and also for treatment switches. Furthermore, direct comparison of different treatments within one cohort is provided. Moreover, we accounted for selective dropout using IPCW. With 381 patients with RA-ILD in our analysis, this is one of the largest cohorts with detailed treatment and disease activity information.
Limitations of our study were the inability to assess lung function impairment, ILD-specific treatments, for example, antifibrotic therapy, and type and severity of ILD due to missing imaging data. Thus, patients with ILD in progressive and stable stages could have been mixed, and our cohort might consist of more patients with serious ILD compared with the general population with RA due to the nature of our data collection. Beside this, our analyses take into account the treatment at the time of ILD reporting, but not at the time of diagnosis. However, we adjusted for antecedent therapies before ILD reporting as sensitivity analyses. A further limitation is that we were mainly able to analyse all-cause mortality and not lung-specific mortality, as the number of cases was limited (only 51 deaths related to lungs) which precluded adjustment for the various confounders. However, when analysing these deaths alone, the results remained stable but had much wider CIs. We carefully adjusted for the choice of treatment using a comprehensive list of risk factors and confounders. However, residual confounding cannot be ruled out as the sample size limited the number of variables, and adjustment was only possible for known confounders. In addition, the majority of patients with RA-ILD on a non-TNFi-bDMARD or JAKi at the time of diagnosis had been treated with TNFi priorly. Theoretically, if a patient survived treatment with TNFi and switched to a b/tsDMARD, this may result in a positive selection of patients treated with other b/tsDMARDs after TNFi. This in turn may bias the results in favour of other b/tsDMARDs. On the other hand, almost half of the patients who were on a TNFi at the time of ILD also had received it prior to reporting of the diagnosis. This could have led to a positive selection of patients with good response to TNFi and thus may have biased the results in favour of TNFi. Furthermore, the generalisability of the results from the RABBIT registry is limited by the inclusion criteria of the cohort. Patients are enrolled either with a b/tsDMARD treatment or with a csDMARD after at least one DMARD failure, resulting in a patient population with more severe or established RA disease.
To the best of our knowledge, this is the first study to investigate the risk of all-cause mortality in RA-ILD for all available DMARD treatments, using monthly level data. In accordance with the few existing recommendations and studies, our results support the assumption of a beneficial effect of non-TNFi biologics when compared with TNFi in treating patients with RA-ILD. Receiving no DMARD therapy was associated with a high risk of mortality, which means that any effective treatment is better than not treating at all. Elevated disease activity, in particular systemic inflammation, as well as comorbidities are major contributors to mortality risk in patients with RA-ILD.
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Data availability statement
No data are available.
Ethics statements
Patient consent for publication
Ethics approval
The study protocol was approved by the ethics committee of the Charité University Medicine Berlin, Germany (EA4/123/21). Participants gave informed consent to participate in the study before taking part.
Acknowledgments
The authors acknowledge the invaluable contributions of all participating consultant rheumatologists and their patients. In particular, we would like to thank those rheumatologists who enrolled the highest numbers of patients: Kaufmann J, Klopsch T, Baraliakos X, Eisterhues C, Schwarze I, Worsch M, Kneitz C, Möbius C, Krause A, Liebhaber A, Zinke S, Rockwitz K, Eder A, Feuchtenberger M, Kovács M, Kühne C, Berger S, Harmuth W, Fricke-Wagner H, Schmalzing M, Kellner H, Balzer S, Gräßler A, Wernitzsch H, Krummel-Lorenz B, Syrbe U, Lebender S, Hamann F, Bohl-Bühler M, Bruckner A, Röser M, Burmester G, Lorenz H, Blank N, Wiesmüller G, Baumann C, Müller B, Stille C, Wassenberg S, Brandt H, Karberg K, Roßbach A, Reckert S, Bergerhausen H, Schmeiser T, Weiß K, Krüger K, Kapelle A, Seifert A, Prothmann U, Thiele A, Pagel K, Krause D, Meier L, Schmitt-Haendle M, Heel N, Herzer P, Mauß-Etzler U, Häckel B, Detert J, Marycz T, Dahmen G, Haas F, Wiesent F, Manger K, Feist E, Gause A, Zänker M, Reck A, Aringer M, Nerenheim A, Bielecke C, Claußnitzer A, Herzberg C, Eidner T, Holst A, Bestler D, Edelmann E, Behringer W, Aurich M, Boldemann R, Borvendég T, Spengler L, Hauser M, Alliger K, Linhart B, von Hinüber U, Schibinger H, Fuchs P, Richter J, Menne H, Mark S, Häntsch J, Donath G, Wahl B, Schulze-Koops H, Jendro M, Geißler K, Engel J, Bartner R. We also acknowledge the significant contributions of Angela Zink, Berlin, Peter Herzer, Munich, Bernhard Manger, Erlangen and Matthias Schneider, Düsseldorf, as members of the advisory board.
References
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Footnotes
MS and AS contributed equally.
Presented at Preliminary results of this study were presented at the EULAR 2023 congress in Milan (Rudi T, Zietemann V, Schaefer M, et al POS0174 The Impact of Different DMARDs on Mortality in Patients with Rheumatoid Arthritis and Prevalent Interstitial Lung Disease Annals of the Rheumatic Diseases 2023;82:311).
Contributors TR, MS, VZ, YM and AS had full access to all data of this study and take responsibility for data integrity and accuracy of the analysis. Study concept and design: TR, MS, VZ, YM and AS. Acquisition of the data: AK, CK, H-ML. Analysis of the data: TR. Interpretation of the data: TR, MS, VZ, YM, AZ and AS. Drafting the manuscript: TR. Critical revision of the manuscript for important intellectual content: TR, MS, VZ, YM, AZ, AK, CK, H-ML and AS. Obtaining funding: AS and AZ. All authors read and approved the manuscript. AS is responsible for the overall content as guarantor.
Funding RABBIT is supported by a joint, unconditional grant from AbbVie, Amgen, BMS, Celltrion, Fresenius Kabi, Galapagos, Hexal, Eli Lilly, MSD, Pfizer, Samsung Biosepis, Sanofi Aventis, Viatris Sante and UCB, and previously by Roche. The study was supported by the Federal Ministry of Education and Research within the network TARISMA (01EC1902A).
Competing interests AK: consulting fees: AbbVie, Gilead, Boehringer Ingelheim, Novartis, Janssen, UCB, Lilly and payment for lectures, presentations: AbbVie, Gilead, Boehringer Ingelheim, Roche, Novartis, Janssen, UCB, Lilly and support for attending meetings and/or travel: AbbVie, Boehringer Ingelheim and participation on a Data Safety Monitoring Board or Advisory Board: Pfizer and leadership or fiduciary role in other board, society, commitee or advocacy group, paid or unpaid: German Society for Rheumatology. CK: payment for lectures, presentations: AbbVie, AstraZeneca, BMS, Boehringer Ingelheim, Celltrion, Galapagos, GSK, Hexal, Janssen, Lilly, Medical School Hamburg, MSD, Novartis, Pfizer, Roche, Sanofi, UCB and participation on a Data Safety Monitoring Board or Advisory Board: Boehringer Ingelheim, Novartis. H-ML: grants or contracts from any entity: Pfizer, Novartis and consulting fees: AbbVie, AstraZeneca, Actelion, Amgen, Bayer Vital, Boehringer Ingelheim, BMS, Celgene, GSK, Gilead/Galapagos, Janssen, Lilly, Medac, MSD, Novartis, Pfizer, Roche/Chugai, Sanofi, UCB and payment for lectures, presentations: AbbVie, AstraZeneca, Actelion, Amgen, Bayer Vital, Boehringer Ingelheim, BMS, Celgene, GSK, Gilead/Galapagos, Janssen, Lilly, Medac, MSD, Novartis, Pfizer, Roche/Chugai, Sanofi, UCB and support for attending meetings and/or travel: AbbVie, AstraZeneca, Boehringer Ingelheim, BMS, Celgene, GSK, Gilead/Galapagos, Janssen, Lilly, MSD, Novartis, Pfizer, Roche/Chugai, Sanofi, USB and participation on a Data Safety Monitoring Board or Advisory Board: AbbVie, AstraZeneca, Amgen, Boehringer Ingelheim, BMS, Celgene, GSK, Gilead/Galapagos, Janssen, Lilly, Medac, MSD, Novartis, Pfizer, Roche/Chugai, Sanofi, UCB. AS: lecture honoraria from AbbVie, Amgen, BMS, Celltrion, MSD, Lilly, Pfizer, Roche, UCB.
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