Abstract
Objective
Fatigue is important for patients with rheumatoid arthritis (RA) but is poorly understood. We sought to study associations of fatigue with clinical features, disease activity, and synovial histology.
Methods
Patients meeting the American College of Rheumatology/EULAR 1987 and/or 2010 RA criteria were recruited before elective total joint replacement. Demographics, RA characteristics, tender and swollen joints, erythrocyte sedimentation rate (ESR) and C‐reactive protein, and patient‐reported fatigue, categorized as mild, moderate, or severe, were collected. Hematoxylin and eosin stains of sectioned synovium were systematically scored by a pathologist. Relationships between fatigue and studied variables were evaluated with Kendall's tau. A directed acyclic graph (DAG) was used to illustrate associations of exposures, outcome variables, mediators, and confounders. Multivariable ordered logistic regression was used to further study associations.
Results
Of 160 included patients, 85.6% were women, with a median age of 63.5 (55.25–71.40) and mean disease activity scores in 28 joints using ESR (DAS28‐ESR) of 3.91 (SD 1.3). There were no differences in comorbidities across fatigue categories. Fatigue correlated with DAS28‐ESR, synovial lining hyperplasia (SLH), anxiety, depression, and pain. In the DAG, DAS28‐ESR was associated with fatigue, full mediation by pain, partial mediation by depression and anxiety, and confounding by female sex. SLH was independently associated with fatigue but did not confound the relationship between DAS28‐ESR and fatigue. SLH was affected by synovial lymphocytic inflammation. In multivariable models, female sex, DAS28‐ESR, and SLH were all associated with higher fatigue.
Conclusion
Although fatigue is associated with DAS28‐ESR, it is also associated with SLH independently of disease activity.
INTRODUCTION
Fatigue is common and persistent among patients with rheumatoid arthritis (RA), affecting 40% to 80% of patients. 1 , 2 In an international study of 542 patients with RA, 41% reported severe fatigue. 3 Fatigue can have an immense impact on the quality of life of patients with RA, 4 including effects on physical activities, emotions, and relationships. 5 Patients report that fatigue can be overwhelming, difficult to manage, and often unaddressed by clinicians. 5 In a study of 1,588 patients, fatigue was one of the most important drivers of the patient global assessment in the 303 patients near remission (β = 0.15). 6 Despite the importance of fatigue for patients with RA, mechanisms of RA fatigue are poorly understood, making it difficult to manage. Multiple studies showed that fatigue is correlated weakly with RA disease activity as measured by the disease activity score using 28 joints (DAS28). 7 , 8 , 9 , 10 Fatigue and disturbances in sleep have been related to inflammatory cytokines that are elevated in active RA, 11 , 12 whereas patients with RA who received cytokine blocking therapies experienced clinically significant improvements in RA‐related fatigue. 13 Randomized controlled trials show that improved disease activity also results in at least a partial improvement in patient‐reported fatigue. 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 However, fatigue is often still reported by patients even when their RA disease activity is low. 23 When a treat‐to‐target strategy was employed in patients with early RA (ERA), 75% of patients with ERA who reported baseline fatigue on visual analogue scale (VAS) still reported fatigue at one year, despite improvement in their disease activity. 24 In the same study, 15% of patients who initially reported no fatigue at baseline reported worsening fatigue at one year despite improvement in disease activity. Some studies suggest fatigue may be linked to chronic pain or depression rather than RA disease activity, whereas others associate fatigue to factors unrelated to RA, such as female sex, comorbidities, poor sleep, and mental health. 8 , 23 , 25 , 26 , 27 We have previously analyzed synovial histopathology from patients with RA and found that the presence of neutrophils and fibrin in RA synovial tissue were significantly associated with prolonged morning stiffness, 28 and that synovial lining fibroblasts express genes associated with pain. 29 The purpose of this study is to investigate factors associated with fatigue in a cohort of patients with established RA and determine if there are differences in clinical factors or joint histopathology between fatigue categories.
MATERIALS AND METHODS
Study setting
In this cross‐sectional analysis, we studied 160 patients who met ACR/EULAR 1987 and/or 2010 RA criteria undergoing an elective total hip, knee, elbow, or shoulder replacement at a single tertiary care hospital. All patients signed informed consent, and the study was approved by the Hospital for Special Surgery Institutional Review Board (#2014‐233).
Clinical characteristics
Demographics (age, body mass index, sex, race), medical history, C‐reactive protein (CRP), erythrocyte sedimentation rate (ESR), and patient‐reported outcomes were collected preoperatively. Patients were given questionnaires to assess global function and RA disease activity. Patient‐reported fatigue (“How much of a problem has unusual FATIGUE or TIREDNESS been for you in the PAST WEEK?”) was rated by patients on a 0 to 10 scale and stratified into mild (0–3), moderate (4–7), or severe (8–10). Patients were also asked how they dealt with depression/feeling blue and nervousness/anxiety over the last week through the Multi‐Dimensional Health Assessment Questionnaire. RA disease characteristics including physical examination by a physician (medical doctor's global, tender joint count, swollen joint count [SJC]), additional RA laboratory tests (cyclic citrullinated peptide, rheumatoid factor), and RA disease activity measures (DAS28 using the ESR [DAS28‐ESR], DAS28 joints using the CRP [DAS28‐CRP], and clinical disease activity score) were also collected preoperatively.
Histopathology
Following elective arthroplasty, synovial samples from the operative joint were removed by a surgeon and brought to pathology for standard histopathologic evaluation. Inflamed‐appearing synovial samples from the operative joint were selected by a pathologist at gross inspection for further examination microscopically. Hematoxylin and eosin stains of the synovium of the operative joint were prepared, and 10 features were scored by a pathologist systematically as described previously. 30 Example images and a full description of the scoring system are available at https://www.hss.edu/pathology-synovitis.asp. Synovial lymphocytic inflammation (SLI) was graded as none (score of 0), mild (1), moderate (2), marked (3), or band‐like (4). Synovial lining hyperplasia (SLH) was graded as normal (score of 0), 1 to 3 cells thick (1), 3 to 4 cells thick (2), or more than 4 cells thick (3).
Statistical analyses
Descriptive statistics including frequencies, percentages, medians, and interquartile ranges were calculated and compared among low, moderate, and severe fatigue groups by exact, Mann‐Whitey, or chi‐square tests as appropriate. Statistical significance was determined using an α of 0.05.
A correlation plot Figure 1 was prepared to measure correlations using Kendall's tau between fatigue and various RA disease activity measures, histopathology features, and recent mental health status, with statistical significance of α of 0.05.
Figure 1.
Pairwise associations with fatigue using Kendall’s Tau (P < 0.05). DAS28 ESR, disease activity scores in 28 joints using erythrocyte sedimentation rate; RA, rheumatoid arthritis.
A directed acyclic graph (DAG) was used to illustrate associations of exposure and outcome variables. Univariate analysis was used to identify associations (arrows); bivariate analysis was used to identify confounders (oval boxed), or a common cause of both the exposure and the outcome (Figure 2). When the coefficient differed more than 10% with versus without the variable, we defined it as a confounding variable. Mediation analysis was performed to identify mediators. The effect of exposures on outcomes disappeared completely when removing a full mediator, or partially when removing a partial mediator.
Figure 2.
Direct acyclic graph (DAG) showing exposures (disease activity and synovial lining hyperplasia) affecting fatigue, along with mediators (in blue) and confounders (red).
Finally, a multivariable model predicting fatigue was created using ordered logistic regression. All analyses were performed using R version 4.0.5 or SAS 9.4.
RESULTS
Cohort characteristics
Of the 160 patients recruited for this cross‐sectional study as previously described, 31 85.6% were women, with a median age 63.5 (55.25–71.40), and DAS28‐ESR 3.91 (SD1.3), indicating overall moderate disease activity (Table 1). When stratified by fatigue level, 44 (27.5%) reported mild fatigue, 64 (40.0%) reported moderate fatigue, and 52 (32.5%) reported severe fatigue. Among mild, moderate, and severe fatigue groups, there were no notable differences in comorbidities, including history of diagnosed mental illness, anemia, fibromyalgia, thyroid disease, and cancer (Table 2). However, patients with higher fatigue were more likely to be female (P = 0.01), reported higher recent anxiety (P = 0.027) and depression (P = 0.008), reported higher pain due to RA (P < 0.0001), had higher DAS28‐ESR (P = 0.01), and had higher SJCs on physical examination (P = 0.02) (Table 1).
Table 1.
Demographics and disease characteristics stratified by fatigue*
| Total (N = 160) | Mild (n = 44) | Moderate (n = 64) | Severe (n = 52) | P value | |
|---|---|---|---|---|---|
| Age, median [IQR] | 63.5 [55.3–71.4] | 67.1 [56.9–71.6] | 61.7 [53.6–71.9] | 63.5 [55.5–68.8] | 0.51 |
| Sex, female, n (%) | 137 (85.6) | 33 (75.0) | 54 (84.4) | 50 (96.2) | 0.01 |
| Race, White, n (%) | 123 (76.9) | 34 (77.3) | 52 (81.2) | 37 (71.2) | 0.44 |
| BMI, median [IQR] | 28.2 [23.7–33.7] | 27.7 [23.4–34.1] | 28.1 [24.2–32.7] | 29.3 [23.7–34.9] | 0.63 |
| Criteria, n (%) | 0.02 | ||||
| Both | 87 (54.4) | 21 (47.7) | 36 (56.2) | 30 (57.7) | |
| 1987 | 25 (15.6) | 5 (11.4) | 16 (25.0) | 4 (7.7) | |
| 2010 | 48 (30.0) | 18 (40.9) | 12 (18.8) | 18 (34.6) | |
| DAS28‐ESR, mean (SD) | 3.91 (1.30) | 3.52 (1.46) | 3.85 (1.20) | 4.31 (1.18) | 0.01 |
| ESR, median [IQR] | 15.0 [7.0–32.3] | 16.0 [7.8–44.0] | 13.5 [6.8–29.0] | 17.5 [9.0–31.3] | 0.44 |
| CRP, median [IQR] | 1.2 [0.0–2.4] | 1.1 [0.0–2.1] | 1.2 [0.0–2.4] | 1.1 [0.0–2.4] | 0.99 |
| RF, positive, n (%) | 86 (53.8) | 25 (56.8) | 36 (56.2) | 25 (48.1) | 0.61 |
| CCP, positive, n (%) | 122 (76.2) | 32 (72.7) | 46 (71.9) | 44 (84.6) | 0.22 |
| TJC | 2.0 [0.0–6.0] | 1.0 [0.0–3.3] | 2.0 [0.0–6.0] | 2.0 [0.0–7.3] | 0.14 |
| SJC | 4.0 [1.0–9.0] | 2.5 [0.0–5.5] | 6.0 [2.0–9.0] | 4.5 [1.8–9.0] | 0.02 |
| RA pain, median [IQR] | 6.0 [4.0–8.0] | 4.0 [2.0–6.3] | 6.0 [4.0–7.0] | 8.0 [6.0–9.0] | <0.0001 |
| Recent anxiety, mean (SD) a | 0.03 | ||||
| 0 | 66 (43.4) | 26 (59.1) | 29 (45.3) | 14 (26.9) | |
| 1 | 62 (40.8) | 13 (29.5) | 27 (42.2) | 25 (48.1) | |
| 2 | 22 (14.5) | 4 (9.1) | 8 (12.5) | 11 (21.2) | |
| 3 | 2 (1.3) | 1 (2.3) | 0 (0.0) | 2 (3.8) | |
| Recent depression, mean (SD) a | 0.008 | ||||
| 0 | 79 (52.0) | 32 (72.7) | 33 (51.6) | 18 (34.6) | |
| 1 | 63 (41.4) | 9 (20.5) | 30 (46.9) | 27 (51.9) | |
| 2 | 8 (5.3) | 2 (45) | 1 (1.6) | 5 (9.6) | |
| 3 | 2 (1.3) | 1 (2.3) | 0 (0.0) | 2 (3.8) | |
Note: Bolded values significant P < 0.05.
Source: BMI, body mass index; CCP, cyclic citrullinated protein; CRP, C‐reactive protein; DAS28, disease activity score using 28 joints; ESR, erythrocyte sedimentation rate; IQR, interquartile range; RA, rheumatoid arthritis; RF, rheumatoid factor; SJC, swollen joint count; TJC, tender joint count.
Questions from the Multi‐Dimensional Health Assessment Questionnaire:
Over the last week, were you able to:
Deal with feelings of anxiety or being nervous?
Deal with feelings of depression or feeling blue?
0, Without any difficulty | 1, With some difficulty | 2, With much difficulty | 3, Unable to do
Table 2.
Histopathology and comorbidities stratified by fatigue*
| Total (N = 160) | Mild (n = 43) | Moderate (n = 60) | Severe (n = 49) | P value | |
|---|---|---|---|---|---|
| Synovial lymphocytic inflammation | 0.36 | ||||
| None | 29 (18.1) | 11 (25.0) | 10 (15.6) | 8 (15.4) | |
| Mild | 50 (31.2) | 17 (38.6) | 20 (31.2) | 13 (25.0) | |
| Moderate | 35 (21.9) | 6 (13.6) | 13 (20.3) | 16 (30.8) | |
| Marked | 28 (17.5) | 7 (15.9) | 14 (21.9) | 7 (13.5) | |
| Band‐like | 18 (11.2) | 3 (6.8) | 7 (10.9) | 8 (15.4) | |
| Plasma cell inflammation | 0.34 | ||||
| <10% | 98 (61.3) | 30 (68.2) | 38 (59.4) | 30 (57.7) | |
| <50% | 28 (17.5) | 4 (9.1) | 15 (23.4) | 9 (17.3) | |
| >50% | 34 (21.2) | 10 (22.7) | 11 (17.2) | 13 (25.0) | |
| Synovial lining hyperplasia | 0.06 | ||||
| Normal | 5 (3.1) | 4 (9.1) | 0 (0.0) | 1 (1.9) | |
| 1–3 cells | 72 (45.0) | 21 (47.7) | 33 (51.6) | 18 (34.6) | |
| 3–4 cells | 43 (26.9) | 12 (27.3) | 16 (25.0) | 15 (28.8) | |
| >4 cells | 40 (25.0) | 7 (15.9) | 15 (23.4) | 18 (34.6) | |
| Fibrin | 71 (44.4) | 15 (34.1) | 31 (48.4) | 25 (48.1) | 0.28 |
| Binucleate plasma cells | 44 (27.5) | 10 (22.7) | 17 (26.6) | 17 (32.7) | 0.56 |
| Giant cells | 21 (13.8) | 9 (20.5) | 10 (15.6) | 5 (9.6) | 0.32 |
| History of back pain | 47 (29.4) | 6 (13.6) | 18 (28.1) | 23 (44.2) | 0.004 |
| History of FM | 7 (4.4) | 2 (4.5) | 2 (3.1) | 3 (5.8) | 0.89 |
| History of MACE | 23 (14.4) | 8 (18.2) | 8 (12.5) | 7 (13.5) | 0.66 |
| History of anemia/ blood disease | 85 (53.1) | 21 (47.7) | 33 (51.6) | 31 (59.6) | 0.51 |
| History of DVT/PE/VTE | 4 (2.5) | 2 (4.5) | 1 (1.6) | 1 (1.9) | 0.68 |
| History of pulmonary disease | 19 (11.9) | 6 (13.6) | 6 (9.4) | 7 (13.5) | 0.71 |
| History of thyroid disease | 22 (13.8) | 5 (11.4) | 7 (10.9) | 10 (19.2) | 0.43 |
| History of mental illness | 30 (18.8) | 8 (18.2) | 9 (14.1) | 13 (25.0) | 0.32 |
| History of osteoporosis | 15 (9.4) | 4 (9.1) | 5 (7.8) | 6 (11.5) | 0.75 |
| History of cancer | 18 (11.2) | 7 (15.9) | 6 (9.4) | 5 (9.6) | 0.54 |
Note: Bolded values significant P < 0.05.
Source: Data are presented as n (%).
DVT, deep vein thrombosis; FM, fibromyalgia; MACE, major acute cardiac events; PE, pulmonary embolism; VTE, venous thromboembolism.
Univariable correlation plot
Fatigue was weakly to moderately correlated by Kendall's tau with DAS28‐ESR, SLH, anxiety, depression, RA pain, and global pain at an α level of 0.05 (Figure 1).
DAG
The DAG showed that the regression coefficient between DAS28‐ESR and fatigue was statistically significant (P = 0.006) (Figure 2). However, the relationship between DAS28‐ESR and fatigue was fully mediated by the patient‐reported RA‐related pain. Thus, without mediation by RA pain, the relationship between DAS28‐ESR and fatigue would no longer be significant. The indirect effect was significant (P < 0.0001). The effect of DAS28‐ESR on fatigue was also partially mediated by depression (P = 0.0005) and anxiety (P = 0.001). The greatest confounder was female sex, which was associated with higher depression (P = 0.02), anxiety (P = 0.03), DAS28‐ESR (P = 0.02), and fatigue (P = 0.005). SLI affected SLH (P < 0.0001), which was a competing exposure affecting fatigue (P = 0.02). However, SLH did not confound the relationship between DAS28‐ESR and fatigue; it was an independent exposure affecting fatigue.
Multivariable model
Fatigue was significantly associated with DAS28‐ESR, sex, and SLH (P < 0.05) (Table 3). Higher disease activity (DAS28‐ESR ≥ 3.2) compared with low disease activity (DAS28‐ESR < 3.2) was associated with a 2.18 times odds of an increased fatigue rating (eg, moderate vs mild). SLH of three to four cells thick compared with normal lining showed 10 times the odds of an increased fatigue rating (P = 0.04), and lining that was greater than four cells thick compared with normal lining showed nearly 16 times odds of a higher fatigue rating (P = 0.01).
Table 3.
Multivariable model to classify fatigue (n = 160)*
| Variable | Odds | Confidence interval | P value |
|---|---|---|---|
| Sex (female vs male) | 2.18 | 1.10–4.31 | 0.03 |
| Synovial lining hyperplasia a | |||
| 1–3 cells vs normal | 6.30 | 0.76–52.01 | 0.09 |
| 3–4 cells vs normal | 10.02 | 1.17–86.14 | 0.04 |
| >4 cells vs normal | 15.88 | 1.83–138.26 | 0.01 |
| DAS28‐ESR (≥3.2 v <3.2) | 3.34 | 1.36–8.20 | 0.008 |
Note: Bolded values significant P < 0.05.
Source: DAS28, disease activity score in 28 joints; ESR, erythrocyte sedimentation rate.
Synovial lining hyperplasia grading: normal (score of 0), 1–3 cells thick (1), 3–4 cells thick (2), or >4 cells thick (3).
DISCUSSION
In this single‐center cross‐sectional analysis of 160 patients with RA, our DAG showed that both DAS28‐ESR and SLH are independent exposures predicting patient‐reported fatigue. The relationship between DAS28‐ESR and fatigue is fully mediated by patient‐reported RA‐related pain, partially mediated by depression and anxiety, and confounded by female sex. Our multivariable models support this, showing patient‐reported fatigue was significantly associated with DAS28‐ESR, female sex, and SLH. In unadjusted analyses, our results are consistent with prior studies noting that patients with higher fatigue are (1) more likely to have higher DAS28‐ESR scores and report higher levels of pain 1 , 7 , 8 , 9 , 10 ; (2) more likely to be female 7 ; and (3) more likely to report anxiety or depression. 23 , 25
In addition to association with DAS28‐ESR, our results indicate that fatigue may be partially explained by persistent histologic changes in the joints that are not necessarily captured by DAS28‐ESR. In our multivariable model, even when adjusting for DAS28‐ESR, SLH was associated with increasing fatigue. Our DAG supports this, as SLH was a competing exposure affecting fatigue independently of DAS28‐ESR.
SLH is likely complex. Others have shown that SLH can also be seen in other diseases of the joint, such as cartilaginous injury. 33 Although fibroblasts have been linked to global synovial hyperplasia, much of the focus has been on those in the vascular sublining as opposed to the synovial lining implicated in our study. 34 , 35 We have previously found that synovial lining fibroblasts express genes associated with pain. 29 In this study, the DAG shows that SLI influenced SLH, which in turn influenced fatigue. Future studies are needed to better understand the underlying pathophysiology of these observations.
Clinically, disparities in fatigue improvement among RA therapies of different mechanisms hint at possible underlying processes that may contribute to fatigue. In a meta‐analysis of 32 randomized control trials, fatigue improvement was greater in nontumor necrosis factor (TNF) inhibitor treatment compared with TNF inhibitor treatment groups. Post‐hoc analyses of clinical trial data showed that patients on the JAK inhibitor tofacitinib and interleukin‐6 receptor antagonist sarilumab both reported greater improvements in fatigue compared with those on TNF inhibitor adalimumab. 18 , 19 , 36 However, more research is necessary to understand the mechanisms that underlie the development of fatigue in relation to specific cytokines and SLH in RA.
Despite the well‐established relationship between depression, anxiety, and fatigue in non‐RA populations, our DAG shows that depression and anxiety did not independently contribute to fatigue in our RA cohort. Depression and anxiety were partial mediators of the relationship of DAS28‐ESR with fatigue but not competing exposures. Because fatigue is not fully explained by anxiety or depression, this may be consistent with previous studies that showed that cognitive behavioral therapy aimed at fatigue resulted in only small improvements in RA fatigue. 32 This is likely because fatigue has noncognitive, nonemotional components that need to be addressed to see greater improvements in fatigue.
Our study has several limitations. Our cohort was relatively small and heterogeneous, making it difficult to stratify, perform subgroup analyses by disease activity or medications, or control for potential confounders such as comorbidities, medication use, and disease duration. It is possible that there was sampling error. Although symmetric, RA does not necessarily affect all joints equally, and only one joint was sampled in this study. Further, within a given joint, synovial inflammation is patchy, and only one section was sampled for this study. However, an experienced musculoskeletal pathologist (ED) directly inspected the entire joint explant and selected the most grossly inflamed tissue for further analysis. 37 Furthermore, our results are cross‐sectional. We only have DAS28‐ESR scores at one time point without information about the stability of DAS28‐ESR with changes in fatigue. Fatigue is subjective and challenging to quantify, but ordinal scales like the one we used are generally accepted and thought to be sensitive to change. 38 Finally, the DAG has limitations. DAGs are nonparametric and do not specify the size of associations. Additionally, they depict systematic but not random error. Any graphical model may oversimplify complex biologic reality. 39 Moreover, our study was observational and not randomized, so although our independent variable were statistically related to the mediators in the DAG, we may not have included other factors with causal influences on fatigue. Lastly, our results might not be generalizable. All patients were from a high‐volume tertiary referral hospital.
In conclusion, our single‐center cross‐sectional analysis of 160 patients with RA showed that both in multivariable models and DAG, patient‐reported fatigue is affected by SLH independently of DAS28‐ESR. It is likely that multiple previously studied RA‐related, personal, and emotional factors all contribute to RA fatigue. The DAG also shows that without pain, the relationship between DAS28‐ESR and fatigue is no longer significant. Depression and anxiety are not independent exposures predicting fatigue, but rather they are partial mediators that influence DAS28‐ESR. Finally, although female sex plays an important role in fatigue, it confounds all relationships studied except SLH. Although our DAG shows that SLI affects SLH, more research is necessary to understand how SLI and other factors contribute to SLH. Understanding the etiology of SLH could help tailor future targeted treatments for RA fatigue.
AUTHOR CONTRIBUTIONS
All authors contributed to at least one of the following manuscript preparation roles: conceptualization AND/OR methodology, software, investigation, formal analysis, data curation, visualization, and validation AND drafting or reviewing/editing the final draft. As corresponding author, Author Diyu Pearce‐Fisher confirms that all authors have provided the final approval of the version to be published, and takes responsibility for the affirmations regarding article submission (eg, not under consideration by another journal), the integrity of the data presented, and the statements regarding compliance with institutional review board/Helsinki Declaration requirements.
Supporting information
Disclosure form
Clinical Trials Registration Number: NCT02111057
Supported by the Clinical and Translational Science Center, Weill Cornell Medicine (UL1‐TR002384, UL1TR001866) and Accelerating Medicines Partnership Autoimmune and Immune‐Mediated Diseases: Disease Teams for Rheumatoid Arthritis (AMP‐AIM Consortium) (UC2 AR 081025).
1Diyu Pearce‐Fisher, BS: Hospital for Special Surgery, New York City, Stony Brook University, Stony Brook, New York; 2Melanie H. Smith, MD, PhD, Bella Y. Mehta, MBBS, MS, Edward DiCarlo, MD, Susan M. Goodman, MD: Hospital for Special Surgery and Weill Cornell Medicine, New York City, New York; 3Edoardo Spolaore, BS, Dongmei Sun, PhD, MSPH: Hospital for Special Surgery, New York City, New York.
Author disclosures are available at https://onlinelibrary.wiley.com/doi/10.1002/acr2.11772.
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