Disease activity trajectories for early and established rheumatoid arthritis: Real-world data from a rheumatoid arthritis cohort.

OBJECTIVES: Disease activity status described at fixed time points does not accurately reflect disease course in chronic and relapsing diseases such as rheumatoid arthritis (RA). We described longitudinal disease activity trajectories in early and established RA.
METHODS: Patients with available 28-Joint Disease Activity Score-erythrocyte sedimentation rate (DAS28-ESR) and Clinical Disease Activity Index (CDAI) over two years were included. Using latent growth curve modelling (LCGM), subgroups of patients following distinct patterns were identified.
RESULTS: 1920 patients were included with 34.4% in early RA (< 2 years' disease duration). Three subgroups were identified using DAS28-ESR in early RA: 1) low disease activity to remission (LDA-REM: 19.1%); 2) moderate disease to remission (MD-REM: 54%); 3) high to moderate disease (HD-MD: 26.9%). The HD-MD group had a significantly higher number of comorbidities, biologic and steroid use and lower post-secondary education. Using CDAI, we identified seven subgroups with only 1.9% remission in early RA. In established RA, seven subgroups were identified using either DAS28-ESR or CDAI. Using DAS28-ESR 27.8% with HD showed improvement in disease status (14.2% HD-REM, 10.3% HD-LDA and 3.3% HD-MD) while using CDAI 17.9% showed improvement.
CONCLUSION: Disease course was different in early and established RA. Only 14.2% of established RA reached DAS28-ESR remission compared to 73.1% of early RA. Using CDAI only 1.9% of early RA and none of the established RA achieved remission, likely reflecting the impact of the patient global assessment on this score. Findings also illustrate the impact of sociodemographic characteristics and early treatment on disease course.

Introduction

Disease activity profiles vary overtime within and between individual patients with rheumatoid arthritis (RA). Thus, describing disease activity status at fixed time points modelled as continuous or dichotomous variable (e.g. remission (REM) or low disease activity (LDA)) does not reflect the patient’s disease course in chronic and relapsing RA. Previous studies have looked at disease trajectories over time mostly using disease activity score-28 (DAS28) in early RA [1] or after biologic treatment initiation [2, 3]. However, disease course may be different in early and established disease. For example, established RA patients are less likely to be biologic naïve, and more likely to be older, have more comorbidities, and use more polypharmacy, all of which would affect disease management in this group of patients. Moreover, considering other disease activity composite measures such as clinical disease activity index (CDAI), which is commonly used in routine clinical practice due to non-reliance on acute phase reactant [4], may reveal different patterns of disease course for patients. These potential differences may have an impact on the treat to target strategy which aims to improve health outcomes of patients with RA.

In this study, we aimed to identify disease trajectories for both patients with early and established RA using the two most common composite measures of diseases activity; DAS28- erythrocyte sedimentation rate (ESR) and CDAI.

Methods

Data source

The Ontario Best Practice Research Initiative (OBRI) is a multicenter registry across Ontario, Canada, collecting data from both rheumatologists and patients with RA at enrolment and at follow-up. It incorporates rheumatologist assessments from approximately one-third of the rheumatologists in the province of Ontario. Patients are eligible to participate if they are ≥ 16 years of age at the time of diagnosis, ≥ 18 years of age at enrolment, and have a rheumatologist confirmed RA diagnosis. Enrolled patients are interviewed every 6 months by phone and are seen by their rheumatologist as per routine care.

Data collection

At enrolment, patients are asked about their general medical history and comorbidities, including cardiovascular disease (CVD), RA disease activity and inflammatory markers. Tender and swollen joint counts, data on socio-demographics, smoking status, height, weight as well as any prior and current medications are collected during the rheumatologist enrolment visit or during the patient’s interview. Patient-reported outcomes for functional status are also collected.

At follow-up visits, all the aforementioned information is updated. RA medication changes (including discontinuation and reasons for discontinuation) between visits are also captured. Rheumatologists report any incident of comorbidity and re-assess disease activity during every follow-up visit.

For this study, patients enrolled in the OBRI between 1st Jan 2008 and 1st Jan 2020 were included and categorized as early (disease duration since diagnosis < 2 year) or established RA (disease duration since diagnosis ≥ 2 year). Patients must also have had at least 2 physician visits and disease activity data (DAS28-ESR and CDAI) available over two years of follow-up (Fig 1).

Fig 1

Cohort flowchart.

We defined disease activity based on DAS28-ESR as: Remission (REM): DAS-ESR< = 2.6; LDA: DAS-ESR< = 3.2; Moderate (MD): 3.25.0.

We also defined disease activity based on CDAI as: Remission (REM) CDAI< = 2.8; LDA: CDA< = 10; Moderate (MD): 1022.

Ethic statement

All sites had ethics approval to enroll patients. All patients signed informed consent as below:

Consent was informed

Consent was written

Consent did not include minors, i.e., participants had to be 18 years of age or older.

Ethics approval: REB# is 07–0729 AE (University Health Network).

Data analysis

Baseline demographics and disease parameters were described using means and standard deviation (SD) for continuous variables or counts and proportions for categorical variables. Main comorbidity was defined as the presence of hypertension, cardiovascular disease (CVD), Diabetes mellitus, Lung diseases, cancer, gastrointestinal disease, and depression. Variables in the early and established groups were compared using the t-test or Wilcoxon Rank-Sum test for continuous variables and the chi-square or Fisher’s Exact test for categorical variables.

Using latent growth curve modelling (LCGM) and a semi-parametric statistical technique proposed by Nagin [5], subgroups of patients following distinct patterns of DAS28-ESR or CDAI change over time were identified. To specify the shape of each trajectory, a single quadratic trajectory model was first tested. If the quadratic component of this model was significant, the analysis for the quadratic model for two trajectories was performed. This process was repeated with an increasing number of trajectories until the model of best fit was obtained, as determined by comparing the Bayesian information criterion (BIC) values [6]. A low BIC indicates the best fitting distribution and number of trajectory subgroups to describe the data. Just briefly, fit statistics and model selection was based on “log Bayes factor which is calculated as:

If log Bayes factor is a negative value, we stop and select the previous model. If log Bayes factor is a positive value, we move to the next model by adding another group [5, 7] (Table A1 in S1 Appendix as example). Subjects are then assigned to the group they most likely belong, the criterion (e.g. based on that group being estimated to have the highest posterior probability of the subject being allocated to it) that is used to make this allocation.

Primary analysis identified trajectory subgroups in patients with early and established RA using DAS28- ESR, separately.

Secondary analysis was conducted to identify disease trajectory subgroups based on CDAI for both early and established RA.

Additionally, we compared sociodemographic, disease and treatment variables between trajectory subgroups identified for DAS28-ESR in patients with early RA, by using one-way ANOVA or Kruskal-Wallis test for continuous variables and the chi-square or Fisher’s Exact test for categorical variables. The analysis was carried out in SAS (version 9.4) using the “proc traj” application which used a general quasi-Newton procedure to estimate parameters that maximize the likelihood function [1, 6, 7].

Results

A total of 1920 patients were included, 660 (34.4%) with early and 1260 (65.6%) with established RA (Table 1). At baseline, patients with early RA were significantly younger (mean 56.6 vs. 58.9 years) had higher DAS28-ESR (mean 4.6 vs. 4.1), CDAI (mean 22.8 vs. 19.4), higher ESR (mean 25.3 vs. 22.2), C-reactive protein (CRP) (mean 14.5 vs. 11.2), and were more likely to use concurrent steroids (23.5% vs. 17.4%). These patients were also less likely to have an erosion (24.0% vs. 58.4%), to be RF-positive (68.9% vs. 74.0%), to use prior biologic disease-modifying antirheumatic drugs (bDMARDs) (9.5% vs. 39.0%), and to start new bDMARDs at enrolment (8.8% vs. 29.9%). There was no significant difference in average number of visits between two groups (mean 13.0 vs. 14.0).

Table 1

Baseline characteristics of patients with RA.

	Disease onset status at enrolment
	Total (N = 1,920)	Early RA (< 2 years) (N = 660)	Established RA (≥ 2 years) (N = 1260)	P Value
Female (%)	1506 (78.4)	487 (73.8)	1019 (80.9)	< .001
Age, years, Mean ± SD	58.1 ± 12.6	56.6 ± 13.3	58.9 ± 12.1	< .001
Marital status, married (%)	1340 (69.8)	471 (71.4)	869 (69.0)	0.257
Post-secondary education (%)	1081 (56.3)	379 (57.4)	702 (55.7)	0.474
Current smoker (%)	318 (16.6)	110 (16.7)	208 (16.5)	0.960
Disease duration, years, Mean ± SD	7.9 ± 9.4	0.3 ± 0.5	11.9 ± 9.5	< .001
PtGA, Mean ± SD	4.8 ± 2.8	5.1 ± 2.7	4.6 ± 2.8	0.008
PhGA, Mean ± SD	4.3 ± 2.5	4.7 ± 2.4	4.0 ± 2.5	< .001
28SJC, Mean ± SD	5.4 ± 4.9	5.8 ± 4.9	5.2 ± 4.8	0.273
28TJC, Mean ± SD	6.1 ± 6.2	7.1 ± 6.5	5.7 ± 6.0	0.005
CDAI, Mean ± SD	20.5 ± 13.5	22.8 ± 13.6	19.4 ± 13.4	0.003
CDAI LDA/REM (CDAI< = 10) (%)	517 (26.9)	130 (19.7)	387 (30.7)	< .001
CDAI REM (CDAI< = 2.8) (%)	85 (4.4)	11 (1.7)	74 (5.9)	< .001
DAS28-ESR, Mean ± SD	4.3 ± 1.6	4.6 ± 1.5	4.1 ± 1.6	< .001
DAS28-ESR LDA/REM (CDAI< = 3.2) (%)	480 (25.0)	117 (17.7)	363 (28.8)	< .001
DAS28- REM (CDAI< = 2.6) (%)	304 (15.8)	77 (11.7)	227 (18.0)	< .001
ESR(mm/hr), Mean ± SD	23.3 ± 20.6	25.3 ± 20.7	22.2 ± 20.5	0.013
	N = 1779	N = 621	N = 1158
CRP (mg/L), Mean ± SD	12.4 ± 20.6	14.5 ± 22.3	11.2 ± 19.4	< .001
	N = 1610	N = 583	N = 1027
HAQ-DI, Mean ± SD	1.1 ± 0.70	1.1 ± 0.70	1.2 ± 0.70	0.528
Presence of erosion (%)	730 (46.7)	128 (24.0)	602 (58.4)	< .001
	N = 1564	N = 533	N = 1031
Positive RF (%)	1322 (73.3)	436 (68.9)	866 (74.0)	0.002
	N = 1804	N = 633	N = 1171
Number of main comorbidities, Mean ± SD	1.1 ± 1.2	1.1 ± 1.1	1.1 ± 1.2	0.202
Hypertension (%)	688 (35.8)	227 (34.4)	461 (36.6)	0.341
CVD (%)	217 (11.3)	66 (10.0)	151 (12.0)	0.192
Diabetes Mellitus (%)	165 (8.6)	61 (9.2)	104 (8.3)	0.463
Lung diseases (%)	262 (13.6)	84 (12.7)	178 (14.1)	0.396
Cancer (%)	152 (7.9)	52 (7.9)	100 (7.9)	0.965
Depression (%)	331 (17.2)	123 (18.6)	208 (16.5)	0.241
Gastrointestinal diseases	335 (17.5)	95 (14.4)	240 (19.1)	0.02
Prior use of biologic (%)	554 (28.9)	63 (9.5)	491 (39.0)	< .001
Prior use of csDMARDs (%)	1573 (81.9)	355 (53.8)	1218 (96.7)	< .001
New bDMARDs start (%)	304 (21.3)	49 (8.8)	255 (29.9)	< .001
	N = 1409	N = 555	N = 854
New csDMARDs start (%)	737 (52.2)	424 (75.9)	313 (36.7)	< .001
	N = 1412	N = 559	N = 853
Current use of steroids (%)	374 (19.5)	155 (23.5)	219 (17.4)	0.001
Current use of NSAIDs (%)	477 (24.8)	139 (21.1)	338 (26.8)	0.005

Numbers are presented as N and (%), unless indicated otherwise. RA, rheumatoid arthritis; RF, rheumatoid factor; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; HAQ-DI, Health Assessment Questionnaire-Disability Index; PtGA, patient global assessment; PhGA, physician global assessment; SJC28, swollen joint count-28; TJC28, tender joint counts-28; CDAI, clinical disease activity index; DAS28 ESR, Disease Activity Score 28-erythrocyte sedimentation rate; csDMARDs, conventional synthetic disease-modifying antirheumatic drugs; bDMARDs, biologic disease-modifying antirheumatic drugs; NSAID, non-steroidal anti-inflammatory drug; LDA: low disease activity; REM: remission

Disease trajectories in early RA

DAS28-ESR

In patients with early RA, three subgroups of patients were identified by LCGM (BIC: -5455.78) (Table A1 in S1 Appendix). Group 1 with Low disease activity (LDA) reached remission rapidly by 6 months and remained in this state at 2 years (LDA-REM: 19.1%). Group 2 with moderate disease (MD) improved to LDA at 6 months and then gradually to REM at 2 years (MD-REM: 54%). Group 3 with high disease (HD) showed slight improvement to moderate disease (MD) state over two years (HD-MD: 26.9%) (Fig 2A and Table 2). Overall, all patients with early RA showed an improvement in their disease activity status over two years of follow-up, with 73.1% reaching remission.

Fig 2

Observed and fitted trajectories from latent growth curve analysis for disease course over 2 years in patients with early RA.

A. DAS28-ESR. DAS28-ESR category: Remission: DAS-ESR< = 2.6; LDA: DAS-ESR< = 3.2; Moderate: 3.25.0. B. CDAI. CDAI category: Remission (REM): CDAI< = 2.8; LDA: CDA< = 10; Moderate (MD): 1022. Dashed line: fitted values. Solid line: observed values.

Table 2

The mean (95% CI) DAS28-ESR values at each time point for trajectory classes in patients with early RA.

N = 660	Group 1	Group 2	Group 2
	LDA-REM	MD-REM	HD-MD
	N = 110	N = 371	N = 179
Group percent	19.1%	54.0%	26.9%
Baseline	2.84 (2.29–3.19)	4.73 (4.49–4.79)	5.47 (5.25–5.57)
6 months	1.84 (1.83–2.33)	3.22 (3.28–3.55)	5.10 (4.98–5.23)
12 months	1.78 (1.48–1.94)	2.69 (2.51–2.85)	4.66 (4.69–4.91)
18 months	1.67 (1.37–1.82)	2.59 (2.25–4.38)	4.53 (4.38–4.62)
24 months	1.66 (1.47–2.48)	2.56 (2.48–2.82)	4.24 (4.04–4.34)

DAS28-ESR category: Remission (REM): DAS-ESR< = 2.6; LDA: DAS-ESR< = 3.2; Moderate (MD): 3.25.0

CDAI

Using CDAI, seven subgroups were identified in patients with early RA (BIC: -11713.4). Group 1 (MD-REM: 1.9%) with moderate disease at baseline rapidly achieved remission at 6 months. Group 2 (MD-LDA: 44.4%) with moderate disease at baseline improved gradually to LDA. Group 3 (MD-MD: 22.1%) with initial moderate disease activity remained in a moderate state. Group 4 (HD-LDA: 19.5%) with initial high disease rapidly improved to LDA. Group 5 (4.9%) and group 7 (2.9%) remained in high disease status over 2 years of follow-up (HD-HD: 7.8%). Group 6 with initial very high disease (VHD) rapidly improved to low disease activity (LDA) state (VHD-LDA: 4.3%) (Fig 2B and Table A2 in S1 Appendix). Overall, using CDAI as a composite measure to describe activity in patients with early RA showed that 30% of patients with moderate or high disease activity had no improvement over two years of follow-up (Group 3, 5, and 7). Of interest, using CDAI to identify disease course in early RA showed only 2% of patients reached remission (Fig 2B) within two years.

A cross tabulation for CDAI and DAS28 subgroups in patients with early RA is shown in Table A3 in S1 Appendix. Almost 60% of patients who were classified as MD-REM by CDAI were assigned to the LDA-REM group using DAS28, confirming disease remission at 24 months. Almost 90.0% of patients who were classified as VHD-MD by CDAI were assigned to the HD-MD group using DAS28, implying an improvement in diseases status using both measures.

Disease trajectories in established RA

Using DAS28-ESR, seven subgroups were identified in patients with established RA (BIC: -10000.81) (Fig 3A and Table A3 in S1 Appendix). Group 1 (REM-REM: 18.3%), group 2 (HD-REM: 14.2%), group 3 (LDA-LDA: 29.8%), group 4 (MD-MD: 18.1%), group 5 (HD-LDA: 10.3%), group 6 (HD-MD: 3.3%), and group 7 (HD-HD: 6.1%). Overall 27.8% of established RA patients with high disease activity showed an improvement in their disease status (group 2, 5, and 6) (Fig 3A and Table A3 in S1 Appendix).

Fig 3

Observed and fitted trajectories from latent growth curve analysis for disease course over 2 years in patients with established RA.

A. DAS28-ESR. DAS28-ESR category: Remission: DAS-ESR< = 2.6; LDA: DAS-ESR< = 3.2; Moderate: 3.25.0. B. CDAI. CDAI category: Remission: CDAI< = 2.8; LDA: CDA< = 10; Moderate: 1022. Dashed line: fitted values. Solid line: observed values.

Using CDAI, seven subgroup of patients were also identified in patients with established RA (BIC: -22010.8) (Fig 3B and Table A5 in S1 Appendix). Group 1 (LDA-LDA: 37.1%), group 2 (MD-MD: 31.1%), group 3 (HD-HD: 8.9%), group 4 (HD-LDA: 10.9%), group 5 (HD-HD: 4.4%), group 6 (VHD -LDA: 7%), and group 7 (VHD-VHD: 0.6%). Only 17.9% of patients with HD showed an improvement in their disease status (group 4 and 6) (Fig 3B and Table A5 in S1 Appendix).

A cross tabulation for CDAI and DAS28 subgroups in patients with established RA is shown in Table A6 in S1 Appendix. There were strong associations between subgroups identified by CDAI and DAS28 trajectories. Almost 97% of patients who remained in LDA at 24 months, based on CDAI (group 1), were assigned to the LDA or REM group (group 1, 2, 3, 5) using DAS28. Sixty-one percent of patients who were classified as HD-HD by CDAI (group 5) were also assigned to the HD-HD group using DAS28 (group 7). Seventy-five percent of patients in the VHD-VHD CDAI group (n = 8), were also assigned to the HD-HD DAS28 group.

DAS28-ESR trajectories group characteristic in patients with early RA

Table 3 shows the sociodemographic, disease, and treatment profile of patients with early RA in three DAS28-ESR trajectories groups. Compared to the other two groups, patients in the HD-MD group (group 3) were significantly less likely to be married (66.5%, p = 0.03), and have post secondary education (48.6%, p = 0.02) at enrolment. Physical function measured by HAQ-DI (mean = 1.6, p<0.001) and patient reported pain (mean = 1.9, p<0.001) was also significantly worse in these patients compared to the other two groups. The mean number of main comorbidities was significantly higher in this trajectory group (mean = 1.3, p<0.001). A significantly higher proportion of patients in this group used prior bDMARDs (14%, p = 0.04), started new bDMARDs at enrolment (12.3%, p = 0.02), and were currently using steroids (23.7%, p<0.001) compared to groups 1 and 2.

Table 3

Baseline characteristics of patients with early RA across DAS28-ESR trajectory groups.

N = 660	Trajectories Group
	Group 1	Group 2	Group 3	P Value
	LDA-REM (N = 110)	MD-REM (N = 371)	HD-MD (N = 179)
Female (%)	74 (67.3)	272 (73.3)	141 (78.8)	0.093
Age, years, Mean ± SD	54.7 ± 12.5	56.8 ± 13.5	57.5 ± 13.3	0.210
Marital status, married (%)	89 (80.9)	263 (70.9)	119 (66.5)	0.030
Post-secondary education (%)	69 (62.7)	223 (60.1)	87 (48.6)	0.017
Current smoker (%)	17 (15.5)	56 (15.1)	37 (20.7)	0.277
HAQ-DI, Mean ± SD	0.6 ± 0.5	1.1 ± 0.7	1.6 ± 0.6	< .001
HAQ-pain, Mean ± SD	0.9 ± 0.7	1.4 ± 0.8	1.9 ± 0.8	< .001
ESR (mm/hr), Mean ± SD	9.8 ± 9.2	26.4 ± 19.4	32.6 ± 23.3	< .001
CRP (mg/L) Mean ± SD	5.3 ± 12.2	15.7 ± 23.1	17.8 ± 23.9	< .001
Positive RF (%)	72 (65.5)	253 (68.2)	111 (62.0)	0.648
Number of main comorbidities, Mean ± SD	0.8 ± 1.1	1.0 ± 1.1	1.3 ± 1.2	< .001
Hypertension (%)	34 (30.9)	119 (32.1)	74 (41.3)	0.071
CVD (%)	11 (10.0)	38 (10.2)	17 (9.5)	0.963
Diabetes Mellitus (%)	5 (4.5)	35 (9.4)	21 (11.7)	0.121
Lung diseases (%)	12 (10.9)	42 (11.3)	30 (16.8)	0.165
Cancer (%)	9 (8.2)	27 (7.3)	16 (8.9)	0.788
Depression (%)	10 (9.1)	63 (17.0)	50 (27.9)	< .001
Gastrointestinal diseases	12 (10.9)	54 (14.6)	29 (16.2)	0.46
Prior use of bDMARDs (%)	11 (10.0)	27 (7.3)	25 (14.0)	0.043
Prior use of csDMARDs (%)	75 (68.2)	176 (47.4)	104 (58.1)	< .001
New bDMARDs start (%)	5 (4.5)	22 (5.9)	22 (12.3)	0.021
New csDMARDs start (%)	52 (47.3)	256 (69.0)	116 (64.8)	0.003
Current use of steroids (%)	155 (23.5)	9 (8.2)	88 (23.7)	< .001

DAS28-ESR category: Remission (REM): DAS-ESR< = 2.6; LDA: DAS-ESR< = 3.2; Moderate (MD): 3.25.0

Numbers are presented as N and (%), unless indicated otherwise. CDAI, clinical disease activity index; PtGA, patient global assessment; PhGA, physician global assessment; SJC28, swollen joint count-28; TJC28, tender joint counts-28; DAS28 ESR, Disease Activity Score 28-erythrocyte sedimentation rate; HAQ-DI, Health Assessment Questionnaire-Disability Index; RF, rheumatoid factor; bDMARDs, biologic disease-modifying antirheumatic drugs csDMARDs, conventional synthetic disease-modifying antirheumatic drugs.

Compared to the other two groups, a lower proportion of patients in group 1 (LDA-REM) started a new traditional DMARD at enrolment (47.3%, p = 0.003) and were more likely to be using them before enrolment (68.2%, p<0.001). No significant differences in age, gender, rheumatoid factor positivity, and current smoking status were found between the three groups (Table 3).

Discussion

In this study we used two composite measures of disease activity to look at disease course in early and established RA patients enrolled in the OBRI. Using DAS28-ESR and CDAI, we detected seven discrete trajectories for established RA categorizing patients’ disease activity at the time of registry enrolment and two years later.

Using DAS28-ESR we found that almost 27.8% of patients experienced some degree of improvement, while one-fourth (24.2%) did not show any improvement. Using CDAI to determine disease course in established patients resulted in similar patterns, however only 17.9% showed improvement. The lack of response in this group of established RA patients may be the result of inappropriate treatment strategies as well as varied comorbidity profiles compared to early RA.

Only 40% of early RA and 30% of established RA patients reached either REM or LDA within 6 months. While the assessment of treat-to-target strategies is beyond the scope of this study, these findings suggest that further investigations are required to better understand why most patients are not reaching the primary target (i.e., LDA or REM) after 6 months of treatment. Perhaps certain clusters or subgroups of RA patients, for example those with more comorbidities, require more aggressive treatment strategies or it is possible that rheumatologist are using other health outcomes (i.e., not DAS and CDAI scores) to assess improvements.

One study from the British Society for Rheumatology Biologics Register for RA (BSRBR-RA) identified four district trajectories (maximal response HD-REM: 8.7%; substantial response HD-LDA: 32%; modest response HD-MD: 55%; minimal response HD-HD: 4.5%) for patients with RA after TNFi initiation [3]. Disease activity was defined as severe (mean DAS28-ESR: 6.5) for the whole cohort as a requirement for starting a biologic treatment in the UK. As they did not present the results by disease duration, a direct comparison with our results was not possible. However, if we compare the HD-REM cohorts, our established cohort showed a greater response (Group 2 in established RA: HD-REM; 14.2%) compared to their maximal response group (HD-REM; 8.7%) [3]. The minimal response rate (HD-HD: 4.5%) derived form their analysis [3] was almost similar to group 7 (HD-HD: 6.1%) in our established RA cohort. Another recent study from UK, using 4 component DAS28-CRP also found three trajectories among 2991 patients with baseline means DAS 5.6 and disease duration of 10 years during 12 months follow-up (rapid responders: 67%, gradual responders: 30.7% and poor responders: 2.3%) [8].

A study from the DREAM-RA registry [9] investigated disease course over two years of follow-up in 180 patients with established RA (categorised into two groups based on their pain phenotype). Compared to our study (DAS28-ESR mean in established RA: 4.1), disease activity measured by DAS28 was lower in their group (the mean values for the DAS28 in the non-nociceptive and nociceptive pain groups at baseline were 2.8 and 2.1, respectively). In terms of disease course during follow-up, they showed no significant change in DAS28 scores over time for the total cohort. One possible explanation for the lack of subgroup trajectories in their study is that all patients included in their cohort were in LDA/remission state at baseline and remained the same during two years, suggesting that the established patients (mean disease duration 8 year) [9] in the DREAM-RA registry were all well managed.

Using data from nine different national registries, another study identified different groups of trajectories following a new biologic treatment [2]. The mean disease duration for 3898 patients included in the study was 12 years. They identified three discrete groups of patients: 1) gradual responders (91.7%) with a baseline mean DAS28 of 4.1; 2) rapid responders (5.6%) with baseline DAS28 of 5.8; and 3) inadequate responders (2·6%) with at baseline DAS28 of 5.1. Compared to our stablished cohort which showed almost 30% improvement from HD to LDA or remission for DAS28-ESR, none of the identified groups in this study reached LDA or remission over two years of follow-up and only 6% of patients with high disease at baseline reached moderate disease status [2]. Heterogeneity of treatment strategy and reimbursement policy across the nine different countries in this pooled analysis may explain the inconsistent findings.

In our study we found that compared to established RA, the disease courses and number of trajectory subgroups identified was different for patients with early RA, where more than 70% showed improvement over two years of follow-up using CDAI and 100% using DAS28-ESR. Barnabe et al. 2015 [1] identified 5 subgroup of 1586 patients with early RA across Canada, with all showing some degree of improvement over two years of follow-up. However, the disease severity in our study cohort was slightly lower (mean of DAS28-ESR: 4.6 vs. 5.1) [1]. Almost half of their patients were in high disease status at cohort inception, while only 12% of early RA patients in our study showed high disease activity suggesting most patients enrolled in the OBRI have been well managed. Another explanation for this difference might be the definition used for early RA in these two studies. In our registry, we define early RA as disease duration less than 2 years whereas they defined early RA as less than 1 year.

Similar to our study, Siemons et al. 2014 [10] identified three trajectories in patients with early RA following a treat to target strategy over 1 year of follow-up. They found 83% with fast decreasing disease activity and stabilizing remission at 9 months. These results are comparable to our findings where 19% and 54% of early RA patients were in stable DAS28-ESR remission at 6 and 12 months, respectively (Table 2). RA_Map Consortium also identified three DAS28-CRP trajectory classes in 267 untreated RA patients from 18 UK centres; 21.7% as inadequate responders, 21.3% as higher baseline activity and 57% as lower baseline activity (moderate status at baseline) both with sustained improvement over 18 months. Lower HAQ-DI, better mental wellbeing, use of dual RA medication at baseline, alcohol consumption, and being female was associated with lower DAS-CRP over time [11]. Their mean DAS28-CRP at baseline was similar to our cohort [4.85 (SD: 0.84)] indicating similar disease course over time (3 trajectory groups in early RA) between the two studies. The RA-Map consortium in another study, conducted in the UK found three DAS28 trajectory classes among 3290 patients from non-biologic arms of phase II and III clinical trials between 2002 and 2012. Latent class mixed model identified differential non–biologic response with three trajectory subpopulations in both MTX-naïve and MTX-exposed patients [12].

In our study we also found that using DAS28-ESR identified fewer subgroups of early RA patients (three discrete trajectories) compared to using CDAI (seven discrete trajectories). Furthermore, by using CDAI, 30% of patients with early RA did not show any degree of improvement, including remission, whereas using DAS28-ESR all patients showed improvement. The presence of patient global assessment (PtGA) as one of the components for CDAI may explain this difference. In another recent study [13], we found that agreement in the classification of LDA/remission between CDAI (≤10) and DAS28-ESR (≤3.2) was fair to moderate, while agreement in the classification of remission between CDAI (≤2.8) and DAS28 (≤2.6) was poor to fair. PtGA also showed the lowest correlation with the remaining CDAI components which became gradually lower towards lower CDAI disease scores [13]. Other studies have shown low agreement between PtGA, joint counts and markers of inflammation especially when ACR/EULAR Boolean remission was not obtained, and that PtGA remained high compared to joint counts and other markers of disease activity [14, 15]. Nevertheless a cross-tabulation between subgroups identified by CDAI and DAS28 showed some association between these subgroups. A stronger association between subgroups of CDAI and DAS28 was also shown in established RA.

Using CDAI for remission makes it more difficult to show improvements in health outcomes, however, its use is more practical in a clinical setting as it does not require laboratory measures (ESR or CRP).

We additionally compared baseline characteristics between the three subgroups of trajectories which were identified in the early RA cohort using DAS28-ESR. We showed that being married, having post secondary education, lower HAQ-DI, lower patient reported pain, lower ESR, and fewer comorbidities were predictive of reaching remission. Siemon et al. 2014 [10] also compared these characteristics between trajectories and found only male sex was a predictive factor for a fast response. Barnabe et al. 2015 [1] found that patients showing the largest improvement (HD-REM) and the best prognosis (MD-REM) are less than 50 years old and have less comorbidity. Similar to our study they also showed that patients starting with high disease are more likely to have lower levels of education [1]. Norton et al. 2014 [16] described sociodemographic differences between trajectory groups and found that groups with the highest level of HAQ-DI were more likely to have higher comorbidity scores, and lower education, social class, and employment level.

In an exploratory analysis we also found meaningful associations between DAS28 subgroups and improvement in functional disability (HAQ-DI and HAQ-pain) at 12 and 24 months follow-up (Table A7 in S1 Appendix) which contributes the validity of subgroup trajectories developed in this study.

We used LCGM, as the most common developed approach, to show patient’s disease course and heterogeneity between subjects over time. This approach has been previously used by other studies in the field of rheumatic disease to identify disease course [1, 3, 10, 17] and swollen joint count trajectories in juvenile inflammatory arthritis [18]. Using clinical data from two Canadian pediatric rheumatology centers they identified five trajectory groups with significant differences in the international League of Associations for Rheumatology categorizations (ILAR) [18].

One of the limitations of longitudinal studies is lost to follow-up. The cohort included in our analysis had complete data for two years of follow-up after baseline, i.e., at 6, 12, 18, and 24 months, which can be considered a strength. However, unmeasured variables in our study could explain some of the heterogeneity seen between trajectory groups. Another limitation for this study is the possibility of selection bias as we applied several inclusion and exclusion criteria for our patients, therefore the results may not generalizable to other RA population. There is also a possibility of more clinic visits by patients with high disease activity compared to those with LDA status, which may affect the impression of disease course toward persistent high disease activity in these patients.

In conclusion disease course is different in early and established RA. After 2 years of follow-up, only 14.2% of established RA patients reached DAS28-ESR remission compared to 73.1% of early RA patients. When CDAI was used as a measure of disease activity, none of the established RA patients reached remission and only 2% of the early RA patients reached remission over 2 years’ follow-up. This may reflect the impact of the PtGA component on CDAI as a composite measure for disease activity. The findings also suggest that sociodemographic characteristics and early treatment impact disease course.

(DOCX)

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6 Apr 2022

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Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

**********

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Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors analyzed the disease course of early and established RA according to trajectories group identified by LCGM. They found that different measure of disease activity may show different pattern of disease course; specifically lower remission rates in CDAI than in DAS28-ESR over 2-years follow up. The reviewer think that the authors need to add the additional analysis and discussion as listed below.

1. The reviewer could not find the definition of major comorbidities in this study. Please add the definition of major comorbidities in the method section.

2. The authors just compared the number of main comorbidities between early RA and established RA groups in table 1. The authors should add the comparisons of each comorbidity based on the detail of the above definition between early RA and established RA groups.

3. Because the authors found the significant difference about remission rates based on DAS28-ESR or CDAI in an identical early RA cohort, the authors should discuss which is better to use DAS28-ESR or to use CDAI in clinical practice to prevent radiographic progression or to achieve better outcome of patients’ quality of life.

4. In table 3, please add the comparisons of each comorbidity based on the detail of the above definition.

5. In the results section, please add the analysis for CDAI trajectories group characteristics in patients with early RA.

6. In the results section, please add the analysis for DAS28-ESR trajectories group characteristics in patients with established RA.

7. In the results section, please add the analysis for CDAI trajectories group characteristics in patients with established RA.

8. Please add the discussion about selection bias as one of the limitation of the study because a significant proportion of patients were excluded at the final analysis as shown in Figure 1.

Reviewer #2: I have a number of comments regarding this paper

1. Literature review: There are a number of papers that have performed similar analyses to the one in this paper which have not been cited. For example, Dagliati et al, A&R (2020); RA-MAP Consortium, Ther Adv in MusculSkelet Dis (2021); RA-MAP Consortium, RMD Open, 2018; McWilliams et al, AR&T (2016). These should be added. It would also be useful for the authors to explain what they work will add to that which is out there already.

2. Definition of LDA, MD, HD, Remission: The authors should provide in the main text of the paper the definitions for LDA, MD, HD, Remission with respect to DAS28-ESR or CDAI, rather than relegating only to the footnotes under Figures 2b, 3a

and 3b.

3. Validation: There is an opportunity here to perform some form of validation of the clustering/subgroup structures found using DAS28-ESR and CDAI across early and established RA either for example through some form of cross-validation or by splitting the data into a testing and training set. My concern is that the clusters/subgroups found are not necessarily robust either across similar populations or even within the same population, especially the very small clusters/subgroups. These subgroups would be more meaningful/convincing if the authors could show at the least that they are stable or they associate meaningfully to later health outcomes such as damage progression (through use of X-rays) or functional disability.

4. Given that the number and presumably the clusters themselves may be different depending on whether DAS28-ESR or CDAI is used, it would be helpful to present the cross-tabulations of the clustering structure based on DAS28-ESR and the clustering structure based on CDAI for both early and established RA, so as to understand how these structure differ. Also it would be helpful for the authors to discuss what the implications of these differing clustering structures on treat-to-target strategies which aim to improve health outcomes of patients with RA.

5. Statistical Methods: The authors should provide more details on how they assign patients to subgroups based on the latent growth curve model (LCGM or LGCM?).

6. In Figures 2a, 2b, 3a and 3b , the authors should make the figures clearer. For example, (i) explicitly state that the solid lines correspond to the observed and the dashed lines to the expected; (ii) add a legend indicating that Group 1 corresponds to the red lines, Group 2 to the green lines and Group 3 to the blue lines, and make it clear that the numbers alongside the lines are the percentage of patients within these groups; and (iv) add the confidence bands around the curves to reflect the uncertainty.

7. The actual results (estimates and standard errors) from the final latent growth curve models should be provided in the Supplementary.

8. The authors need to discuss the possible impact selection bias has on their findings given that they only look at patients with complete outcome data (DAS28-ESR and CDAI) at baseline and all follow-up visits up to 24-months.

Reviewer #3: Dear authors

Thank you for an interesting paper that attempts to identify the trajectories of disease activity in a large cohort of patients with rheumatoid arthritis. I think that the paper reads well.

I have some concerns regarding the methods and presentation of data.

Major concerns:

1. I find it odd that all patients with early RA can be categorised into just three categories of DAS28. Were there no patients who remained in high disease activity for the duration of the two years? Similarly, did no patients go from MD to HD in the established RA group? To this end it would be useful to include either standard deviations or IQR in table 2.

2. I missed tables similar to table 2 and 3 for the other categorisations? If there is not enough space in the manuscript, then included them in the appendix.

3. Were there some patients who participated in borth early and late

4. Although telephone calls are made every 6-months, CDAI and DAS28 cannot be collected by telephone. How often were the routine care visits and is it possible that routine care visits were initiated by patients who experienced high disease activity, thus giving a false impression of persistently high disease activity?

Minor

1.The abstract states that "Only 14.2% of established RA reached DAS28-ESR remission" whereas the discussion states "Using DAS28-ESR we found that 48% of established patients were in remission and low disease status at

enrolment and retained their disease status over two years’ follow-up. Almost 27.8% of patients

experienced some degree of improvement". While both may be true I think there should be a better alignment between the message given in the abstract and in the discussion.

2. What was the average number of visits in each group? It would be helpful if you could show the number of patients with visit at each time point in each trajectory to help the reader judge the validity of the information.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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2 Jun 2022

We have provided answers to reviewers and uploaded related file.

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19 Jul 2022

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PLOS ONE

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: No

Reviewer #2: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: 1. The authors have not addressed (or attempted to address) my concern regarding validation (Comment 3). As mentioned in my earlier review, "My concern is that the clusters/subgroups found are not necessarily robust either across similar populations or even within the same population, especially the very small clusters/subgroups. These subgroups would be more meaningful/convincing if the authors could show at the least that they are stable or they associate meaningfully to later health outcomes such as damage progression (through use of X-rays) or functional disability."

2.Related to my Comment 4 in my previous review, I believe it is unsatisfactory to not provide the cross-tabulations of the clustering structures of DAS28-ESR by CDAI for early RA and established RA, irrespective of whether some of the cells have small numbers. These cross-tabulations without much effort can be provided as supplementary material. Additionally the authors have not discussed what the implications of these differing clustering structures on treat-to-target strategies which aim to improve health outcomes of patients with RA.

3. In the Discussion, the authors added the following sentence "There is also a possibility of more clinic visits by patients with high disease activity compared to those with LDA status, which may affect the impression of disease course toward persistent high disease activity in these patients." This does not make sense to me as all patients analysed had the same number of visits (i.e. 5). The authors should clarify.

4. In the Data Analysis section, the authors need to add to the sentence "Subjects are then assigned to the group they most likely belong", the criterion (e.g. based on that group being estimated to have the highest posterior probability of the subject being allocated to it) that is used to make this allocation.

5. Table A1 Suppl: The column headings (Group1, Group2, Group3, Group4) do not make sense.

6. Table A2 Suppl: Based on the 24-month mean value of 12.8 for Group 6 (VHD-LDA), it is unclear why this group is described as VHD-LDA, when LDA based on CDAI is defined to have a CDAI value <= 10.

Reviewer #3: Thank you for revising this interesting manuscript according to our suggestions. I have no further comments.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

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9 Aug 2022

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: 1. The authors have not addressed (or attempted to address) my concern regarding validation (Comment 3). As mentioned in my earlier review, "My concern is that the clusters/subgroups found are not necessarily robust either across similar populations or even within the same population, especially the very small clusters/subgroups. These subgroups would be more meaningful/convincing if the authors could show at the least that they are stable or they associate meaningfully to later health outcomes such as damage progression (through use of X-rays) or functional disability."

Authors’ response: Thanks for this comment. As recommended, we addressed stability by performing cross-tabulation of CDAI and DAS28 ESR in both early and established RA and added results in the supplementary material (Tables A3 and A6 Suppl). As an exploratory analysis, to show some meaningful association between DAS28 subgroups and functional disability (HAQ-DI and pain) we have added a table for early RA patients, in the supplementary material (Table A7 Suppl). We also added a related statement in the discussion.

2.Related to my Comment 4 in my previous review, I believe it is unsatisfactory to not provide the cross-tabulations of the clustering structures of DAS28-ESR by CDAI for early RA and established RA, irrespective of whether some of the cells have small numbers. These cross-tabulations without much effort can be provided as supplementary material. Additionally the authors have not discussed what the implications of these differing clustering structures on treat-to-target strategies which aim to improve health outcomes of patients with RA.

Authors’ response: Thanks for this comment. We did cross-tabulation of CDAI and DAS28 ESR in both early and established RA and added them in the supplementary material (Tables A3 and A6 Suppl). We also discussed the association between subgroups identified by CDAI and DAS28 and implication of CDAI and DAS28 as two most common disease activity indices in the clinic setting. We have added a paragraph on treat-to target strategies in the discussion.

3. In the Discussion, the authors added the following sentence "There is also a possibility of more clinic visits by patients with high disease activity compared to those with LDA status, which may affect the impression of disease course toward persistent high disease activity in these patients." This does not make sense to me as all patients analysed had the same number of visits (i.e. 5). The authors should clarify.

Authors’ response: Thanks for this comment. To clarify this, all patients had data at baseline, 6 month, 12 months, and 24 months follow-up. However, they may have had different numbers of visits over 24 months (Mean: 5.7; SD: 2.0).

4. In the Data Analysis section, the authors need to add to the sentence "Subjects are then assigned to the group they most likely belong", the criterion (e.g. based on that group being estimated to have the highest posterior probability of the subject being allocated to it) that is used to make this allocation.

Authors’ response: Thanks for this comment. We added this to the sentence.

5. Table A1 Suppl: The column headings (Group1, Group2, Group3, Group4) do not make sense.

Authors’ response: Thanks for this comment. We removed them.

6. Table A2 Suppl: Based on the 24-month mean value of 12.8 for Group 6 (VHD-LDA), it is unclear why this group is described as VHD-LDA, when LDA based on CDAI is defined to have a CDAI value <= 10.

Authors’ response: Thanks for this comment. We corrected them.

Reviewer #3: Thank you for revising this interesting manuscript according to our suggestions. I have no further comments.

Submitted filename: Review Comments to the Authors_responses_round 2_20220808.docx

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25 Aug 2022

Disease Activity Trajectories for Early and Established Rheumatoid Arthritis:  Real-World Data from a Rheumatoid Arthritis Cohort

PONE-D-22-02930R2

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Reviewer #2: No

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Reviewer #2: The authors have addressed my comments now.

There are a few minor edits for the authors to make:

1. Disease Trajectories in early RA, CDAI section, p7: Please replace the labelling and description of Group 6 to VHD-MD from VHD-LDA and from low disease activity to moderate disease activity.

2. Disease Trajectories in early RA, CDAI section, last paragraph, p7: Although the sentence "Almost 60% of patients who were classified as MD-REM by CDAI were assigned to the LDA-REM group using DAS28, confirming disease remission at 24 months" is correct. In fact, 100% of these patients were in either the LDA-REM or MD-REM groups using DAS28. Therefore a much higher proportion than the 60% actual were in confirmed remission at 24 months. Therefore you can make the case even stronger.

3. Disease Trajectories in established RA, DAS28-ESR section, pp7-8: Table A3 Suppl should be Table A4 Suppl.

4. Discussion section, p 11, last paragraph: It is not correct that the 267 patients in the RA-MAP consortium were "untreated RA patients". They were untreated at enrollment in the study, but thereafter were treated. Please delete "untreated" and replace appropriately.

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Reviewer #2: No

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26 Aug 2022

PONE-D-22-02930R2

Disease Activity Trajectories for Early and Established Rheumatoid Arthritis:  Real-World Data from a Rheumatoid Arthritis Cohort

Dear Dr. Movahedi:

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on behalf of

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Investigator	Affiliation
Dr. Carter Thorne	Assistant Professor, University of Toronto, Toronto, ON, Canada
Dr. Janet Pope	Professor of Medicine, Western University, Schulich School of Medicine & Dentistry, London, ON, Canada
Dr. Alfred Cividino	Professor Emeritus, McMaster University, Hamilton, ON, Canada
Dr. Jane Purvis	Courtesy staff Peterborough Regional Health Centre, Peterborough, ON, CanadaQueens University Adjunct Faculty, Department of Family Medicine, Kingston, ON, Canada
Dr. Vandana Ahluwalia	Division of Rheumatology, Department of Internal Medicine, William Osler Health System, Brampton, ON, Canada
Dr. Sangeeta Bajaj	William Osler Health System, Brampton, ON, CanadaUniversity of Toronto, Toronto, ON, CanadaMcMaster University, Hamilton, ON, Canada
Dr. Arthur Karasik	Community practice, Toronto, ON, Canada
Dr. Andrew Chow	Assistant Clinical Professor Medicine, McMaster University, Hamilton, ON, Canada
Dr. Brian Hanna	Community practice, Cambridge, ON, Canada
Dr. Catherine Alderdice	Community practice, Guelph, ON, Canada
Dr. Nader Khalidi	St. Joseph’s Healthcare/McMaster University, Hamilton, ON, Canada
Dr. Ali Shickh	Community practice, Bowmanville, ON, Canada
Dr. Frances Leung	Community practice, Toronto, ON, Canada
Dr. Bindee Kuriya	Sinai Health System, University of Toronto, Toronto, ON, Canada
Dr. Edward Keystone	Professor Emeritus, University of Toronto, Toronto, ON, Canada
Dr. Jacqueline Hochman	Women’s College Hospital, Toronto, ON, Canada
Dr. Claire Bombardier	Toronto General Research Institute, University Health Network, Toronto, ONMount Sinai Hospital, Toronto, ONDivision of Rheumatology, Faculty of Medicine, University of Toronto, Toronto, ONIHPME, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
Dr. Pooneh Akhavan	Mount Sinai Hospital, Toronto, ON, Canada
Dr. Elaine Soucy	Trillium Health Partners, Mississauga, ON, CanadaLecturer (adjunct) University of Toronto, Toronto, ON, Canada
Dr. Felix Leung	Community practice, Toronto, ON, Canada
Dr. Ami Mody	Community practice, Mississauga, ON, Canada
Dr. Angela Montgomery	Community practice, Ottawa, ON, Canada
Dr. Michael Aubrey	Markham Stouffville Hospital, Markham, ON, Canada
Dr. E. Ng Tung Hing	Centre of Arthritis Excellence, Newmarket, ON, CanadaTAP research program, Newmarket, ON, Canada
Dr. Heather McDonald-Blumer	Division of Rheumatology, Mount Sinai Hospital, Toronto, ON, CanadaUniversity Health Network, Toronto, ON, Canada
Dr. Zareen Ahmad	Toronto Scleroderma Program, Division of Rheumatology, Toronto Western and Mount Sinai Hospitals, Toronto, ON, CanadaDepartment of Medicine, University of Toronto, Faculty of Medicine, Toronto, ON, Canada
Dr. Mark Matsos	Associate Professor, McMaster University, Hamilton, ON, Canada
Dr. Raj Carmona	Associate Clinical Professor, Director, Medical Foundation 4, MD Program, McMaster University, Hamilton, ON, Canada
Dr. Shikha Mittoo	Community practice, Toronto, ON, Canada
Dr. Allan Kagal	Mackenzie Health, Richmond Hill, ON, Canada
Dr. Sankalp Bhavsar	McMaster University, Hamilton, ON, Canada
Dr. Arthur Bookman	Professor Medicine, University of Toronto, Staff Rheumatologist, University Health Network, Toronto, ON, Canada
Dr. Lori Albert	Faculty of Medicine, University of Toronto, Toronto, ON, CanadaRheumatology Clinic, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
Dr. Saeed Shaikh	Assistant Clinical Professor, McMaster University, Hamilton, ON, Canada
Dr. Jonathan Stein	Department of Medicine, University of Toronto, Toronto, ON, CanadaDepartment of Medicine, St Joseph’s Health Centre, Toronto, ON, Canada
Dr. Nicole le Riche	Department of Medicine, Western University, London, ON, CanadaDivision of Rheumatology, St. Joseph’s Health Care, London, ON, Canada
Dr. Andy Thompson	Associate Professor of Medicine, Western University, London, ON, Canada
Dr. Gina Rohekar	University of Western Ontario, London, ON, Canada
Dr. Sherry Rohekar	Associate Professor, Division of Rheumatology, Department of Medicine, Western University, London, ON, Canada
Dr. William Bensen	St. Joseph’s Healthcare, McMaster University, Hamilton, ON, Canada
Dr. Viktoria Pavlova	Community practice, Hamilton, ON, Canada
Dr. Sanjay Dixit	Assistant Clinical Professor, Department of Medicine, Division of Rheumatology, McMaster University, Hamilton, ON, Canada
Dr. Manisha Mulgund	Community practice, Hamilton, ON, Canada
Dr. Dana Cohen	Community practice, Vaughan, ON, Canada
Dr. Patricia Ciaschini	Community practice, Sault Ste. Marie, ON, Canada
Dr. Simon Carette	Division of Rheumatology, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
Dr. Sindhu Johnson	Division of Rheumatology, Toronto Western Hospital and Mount Sinai Hospital, Toronto, ON, CanadaInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
Dr. Nigil Haroon	Schroeder Arthritis Institute, Krembil Research Institute, University Health Network, and University of Toronto, Toronto, ON, Canada
Dr. Nooshin Samadi	Community practice, Newmarket, ON, Canada
Dr. Louise Perlin	St. Michael’s Hospital, Toronto, ON, Canada
Dr. Rachel Shupak	University of Toronto, Toronto, ON, Canada
Dr. Dharini Mahendira	Division of Rheumatology, Department of Medicine, University of Toronto, Toronto, ON, Canada
Dr. Thanu Ruban	Department of Medicine, Markham Stouffville Hospital, Markham, ON, Canada
Dr. Raja Bobba	Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
Dr. Rajwinder Dhillon	Assistant Clinical Professor (Adjunct), McMaster University, Hamilton, ON, Canada
Dr. Douglas Smith	Associate Professor, Division of Rheumatology, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
Dr. Jacob Karsh	Rheumatology, University of Ottawa Faculty of Medicine, Ottawa, ON, Canada
Dr. Anna Jaroszynska	Community practice, Burlington, ON, Canada
Dr. Derek Haaland	Rheumatologist, Clinical Immunologist & Allergist, Medical Director, The Waterside Clinic, Barrie, ON, CanadaAssociate Clinical Professor, McMaster University, Hamilton, ON, CanadaAssistant Professor, Northern Ontario School of Medicine, Laurentian University Campus, Sudbury, ON, Canada
Dr. Arthur Lau	Department of Medicine, Division of Rheumatology, McMaster University, Hamilton, ON, Canada
Dr. Maggie Larche	Professor, Department of Medicine, McMaster University, Hamilton, ON, Canada
Dr. Raman Joshi	Brampton Civic Hospital, William Osler Health Systems, Brampton, ON, Canada
Dr. Tripti Papneja	William Osler Health System, Brampton, ON, Canada
Dr. Antonio Cabral	Division of Rheumatology, Department of Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada
Dr. Sibel Aydin	Professor, Faculty of Medicine, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
Dr. Ines Midzic	Division of Rheumatology, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
Dr. Nataliya Milman	University of Ottawa, The Ottawa Hospital, The Ottawa Hospital Research Institute, Ottawa, ON, Canada
Dr. Rafat Faraawi	McMaster University, Hamilton, ON, Canada
Dr. Julie Brophy	Community practice, Guelph, ON, Canada
Dr. Mary Bell	Sunnybrook Health Sciences Centre, Department of Medicine, Division of Rheumatology, Associate Scientist, Sunnybrook Research Institute, Toronto, ON, CanadaProfessor of Medicine, University of Toronto, Toronto, ON, Canada
Dr. Gregory Choy	Division of Rheumatology, Sunnybrook Health Sciences Centre, Toronto, ON, CanadaAssistant Professor, University of Toronto, Toronto, ON, Canada
Dr. Sharron Sandhu	Division of Rheumatology, Sunnybrook Health Sciences, Toronto, ON, CanadaUniversity of Toronto, Toronto, ON, Canada
Dr. Emily McKeown	Sunnybrook Health Sciences Centre, Division of Rheumatology, Toronto, ON, Canada
Dr. Shirley Lake	Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, Toronto, ON, Canada
Dr. Tooba Ali	Assistant Professor, Department of Medicine, Queen’s University, Kingston, ON, Canada
Dr. Saara Rawn	Algoma District Medical Group, Sault Ste. Marie, ON, CanadaAssistant Professor, Northern Ontario School of Medicine, Thunder Bay, ON, Canada
Dr. Raman Rai	Community practice, Brampton, ON, Canada