http://orcid.org/0000-0001-9614-5199
Figures
Abstract
Objective
To evaluate the responsiveness in terms of correlation of the Hospital Universitario La Princesa Index (HUPI) comparatively to the traditional composite indices used to assess disease activity in rheumatoid arthritis (RA), and to compare the performance of HUPI-based response criteria with that of the EULAR response criteria.
Methods
Secondary data analysis from the following studies: ACT-RAY (clinical trial), PROAR (early RA cohort) and EMECAR (pre-biologic era long term RA cohort). Responsiveness was evaluated by: 1) comparing change from baseline (Δ) of HUPI with Δ in other scores by calculating correlation coefficients; 2) calculating standardised effect sizes. The accuracy of response by HUPI and by EULAR criteria was analyzed using linear regressions in which the dependent variable was change in global assessment by physician (ΔGDA-Phy).
Results
ΔHUPI correlation with change in all other indices ranged from 0.387 to 0.791); HUPI’s standardized effect size was larger than those from the other indices in each database used. In ACT-RAY, depending on visit, between 65 and 80% of patients were equally classified by HUPI and EULAR response criteria. However, HUPI criteria were slightly more stringent, with higher percentage of patients classified as non-responder, especially at early visits. HUPI response criteria showed a slightly higher accuracy than EULAR response criteria when using ΔGDA-Phy as gold standard.
Citation: González-Álvaro I, Castrejón I, Carmona L, on behalf of ACT-RAY, PROAR and EMECAR study groups (2019) The comparative responsiveness of Hospital Universitario Princesa Index and other composite indices for assessing rheumatoid arthritis activity. PLoS ONE 14(4): e0214717. https://doi.org/10.1371/journal.pone.0214717
Editor: Feng Pan, University of Tasmania, AUSTRALIA
Received: August 14, 2018; Accepted: March 11, 2019; Published: April 10, 2019
Copyright: © 2019 González-Álvaro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors would like to confirm that the data from the EMECAR and PROAR are the minimal data set supporting the results of this study. These data are available as Supplementary material in 4 Excel files (EMECAR data wide, EMECAR data long, PROAR data wide and PROAR data long). Further inquiries about this information may be directed to the Research Unit of SER at the following e-mail address: proyectos@ser.es. The data from ACT-RAY were provided by Hoffmann-La Roche Ltd through a data sharing agreement that does not allow for the public sharing of these data. The authors did not enjoy any special access privileges in gaining access to these data. Regarding the possibility that any other researcher would like to request data to replicate the reported study findings, Hoffmann-La Roche Ltd has implemented a Data Sharing policy to align with the ICMJE recommendations: “Qualified researchers may request access to individual patient level data through the clinical study data request platform (www.clinicalstudydatarequest.com). Further details on Roche’s criteria for eligible studies are available here (https://clinicalstudydatarequest.com/Study-Sponsors/Study-Sponsors-Roche.aspx). For further details on Roche’s Global Policy on the Sharing of Clinical Information and how to request access to related clinical study documents, see here (https://www.roche.com/research_and_development/who_we_are_how_we_work/clinical_trials/our_commitment_to_data_sharing.html)”.
Funding: Our study was supported by grants RD16/0012/0011 and PI14/00442 from the Ministerio de Economia y Competitividad (Instituto de Salud Carlos III; Spain) and cofunded by the Fondo Europeo de Desarrollo Regional (FEDER). Data from ACT-RAY clinical trial were kindly provided by Hoffmann-La Roche Ltd. No financial support was received from Hoffmann-La Roche Ltd Data from EMECAR and PROAR cohorts were provided by the Spanish Society of Rheumatology. No financial support was received from the Spanish Society of Rheumatology. None of these institutions played any role in the analysis or interpretation of data, nor were they involved in the writing of the manuscript. Roche and Sociedad Española de Reumatología were involved in the collection of data from ACT-RAY, and EMECAR and PROAR, respectively. However, these funders had no role in study design, analysis, decision to publish, or preparation of the manuscript.
Competing interests: Dr. Gonzalez-Alvaro reports grants from Instituto de Salud Carlos III, during the conduct of the study; consulting and speaking fees less than $10,000 from Lilly, non-financial support from UCB, consulting and speaking fees and non-financial support less than $10,000 from BMS, non-financial support less than $10,000 from Pfizer, speaking fees less than $10,000 from Roche, speaking fees and non-financial support less than $10,000 from Abbvie, non-financial support less than $10,000 from MSD, outside the submitted work during the last 5 years; In addition, Dr. Gonzalez-Alvaro has a patent PCT/ES2015/070182 issued. The remaining authors have no potential conflict of interest with regard to the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Introduction
Objective evaluation of disease activity in rheumatoid arthritis (RA) has become a keystone of disease management. Composite indices measuring disease activity have allowed implementing treat-to-target and tight-control strategies, both contributing the most to the improvement in RA outcome achieved in the last 15 years. The most frequently used indices to evaluate disease activity among rheumatologists are the Disease Activity Score (DAS28) [calculated with C-reactive protein (CRP) or with sedimentation rate (ESR)] and the Simplified Disease Activity Index (SDAI), since they have been widely validated, are endorsed by ACR and EULAR, and are commonly used to assess therapeutic response in clinical trials [1–5]. In addition, the Clinical Disease Activity Index (CDAI) is being increasingly used, as it is easier to calculate than the previous ones, despite limited validation.
However, during the last 10 years, a fair amount of evidence suggested that both, DAS28 and SDAI, present a gender bias, derived from differences between men and women in terms of pain perception and levels of erythrocyte sedimentation rate (ESR)[6–10]. Using these indices, the implementation of T2T strategy would be biased, leading to over-treatment in women, or under-treatment of men. This may lead to excess risk of adverse events in women or lower odds to achieve real disease control in men. In addition, assessment of response to treatment in clinical trials might also be biased [11].
The Hospital Universitario La Princesa Index (HUPI) was developed to avoid a gender bias in the assessment of RA disease activity by adjusting the contribution of tender joint counts and ESR by sex [12]. An additional advantage of HUPI is that it can be calculated with ESR, CRP, or both acute phase reactants (APR), producing an almost identical score and avoiding missing data in longitudinal studies [12, 13].
HUPI was initially developed and validated in PEARL, a longitudinal observational study nested in an early arthritis register, and is calculated as the sum of four variables (graded 0–3 according to their quartile distribution in the PEARL population [see S1 Table]): 28 tender and swollen joint counts, global disease assessment by patient and APR [13]. When both ESR and CRP are used to calculate the index, the average of their scores in S1 Table is used to calculate HUPI. Thus, the index ranges from 0 to 12, and its cut-offs for remission/low disease activity, low/moderate and moderate/high disease activity are 2, 5 and 9 respectively [13]. HUPI may have a “ceiling effect”, especially in groups of patients with very high disease activity, such as those included in clinical trials.
The objective of this study was to evaluate the responsiveness of the HUPI, in parallel to that of the classical indices—DAS28-ESR, DAS28-CRP, SDAI and CDAI—, in terms of correlation, using data from three cohorts. Furthermore, the performance of HUPI-based response criteria was compared with that of EULAR response criteria.
Patients and methods
Patients
As mentioned, HUPI was developed in PEARL, an early arthritis register from Madrid (Spain)[12]. In the present study, we evaluate it in other RA populations, including RA from other countries. HUPI was evaluated in three different scenarios: a) an international clinical trial, the ACT-RAY—very high disease activity at baseline, homogeneous intervention, strict follow-up and patients enrolled in different countries; b) an early arthritis population (PROAR) in which sensitivity to change may be tested in a setting different from the early arthritis population used to develop and validate HUPI; and c) a long term prevalent RA population engaged in the pre-biologic era (EMECAR).
The ACT-RAY clinical trial.
ACT-RAY is a 2-year double-blind clinical trial (NCT00810199, EudraCT No 2008-001847-20) designed to evaluate the efficacy and safety of tocilizumab plus methotrexate or tocilizumab plus placebo in patients with persistent active disease despite methotrexate monotherapy. Inclusion criteria for ACT-RAY were RA classification according to 1987 ACR criteria [14], DAS28>4.4, and erosive disease, as described previously [15]. Data collected included demographics, RA characteristics, as well as baseline and 4-weekly clinical and laboratory data necessary to calculate DAS28-ESR, DAS28-CRP, SDAI, CDAI and HUPI [15, 16].
Since no relevant differences in clinical response were reported between patients treated with tocilizumab in monotherapy or in combination with methotrexate [15], we included patients’ data irrespective of their allocation group. Considering that after the first year, patients in ACT-RAY were allocated into four different T2T strategies [16], for the present study the analysis was performed only with data from baseline and 12, 24 and 52 weeks visits.
The PROAR cohort.
PROAR was a longitudinal multicenter study including 5 consecutive patients from 34 Rheumatology Units in Spain. Patients were included if presented at least one swollen joint for less than a year, irrespective of fulfilling 1987 ACR criteria [14]. At baseline, patients should be naïve for disease modifying anti-rheumatoid drugs (DMARDs) or glucocorticosteroids. Evidence of infectious arthritis or crystal arthritis were considered exclusion criteria [17]. Follow-up was 5 years, from January 2001 to December 2006.
Data collection included all variables needed to calculate DAS28-ESR, DAS28-CRP, SDAI, CDAI and HUPI at baseline and at each 6-monthly visits [17]. For the present study only patients fulfilling the 1987 ACR RA criteria along the follow-up were included. Most of these patients started treatment with DMARDs at the beginning of follow-up (S2 Table). Therefore, for the responsiveness analysis, baseline and 6 months visits were analyzed.
The EMECAR cohort.
EMECAR was a prospective longitudinal cohort of prevalent RA patients fulfilling 1987 ACR criteria [14] selected by random sampling in 34 Rheumatology Units from Spain. Follow-up took place from November 1999 to December 2004 with yearly visits. EMECAR database includes the required variables to calculate DAS28-ESR and HUPI, but not DAS28-CRP, SDAI or CDAI, since global disease assessment by physician (GDA-Phy) was not collected and C-reactive protein (CRP) values provided limited reliability. A detailed description of the EMECAR cohort has been published previously [18].
At baseline, no patient was under treatment with a TNF-antagonist or leflunomide. During 4 years of follow-up, 27% of patient started, at least, one of these treatments. As we have previously described, improvement along the follow-up in this long term RA population was limited [18]. However, since HUPI was developed in patients with early arthritis, we included information about EMECAR in order to compare the performance of HUPI compared to DAS28 in long standing disease.
Ethical statement
This is a secondary analysis of anonymized data from patients included in ACT-RAY, EMECAR and PROAR studies. ACT-RAY clinical trial was approved by the Research Ethical Committee (REC) of all centers included in the study (see Acknoledgement section “Group ACT-RAY”). EMECAR study was approved by the REC of Hospital Universitario La Princesa and this approval was accepted by all centers included in the study (see Acknoledgement section “Group EMECAR”). PROAR study was approved by the REC of Hospital Universitario La Princesa and this approval was accepted by all centers included in the study (see Acknoledgement section “Group PROAR”).
ACT-RAY, PROAR and EMECAR studies were conducted according to the principles expressed in the Helsinki Declaration of 1983. All patients signed the respective written consent before study entry [15, 17, 18].
Statistical analysis
We used STATA 12.0 for Windows (StataCorp LP, College Station, TX). To describe the three populations, means and standard deviation (SD), medians and interquartile range (IQR), as well as absolute and relative frequencies were used, depending on the distribution of variables.
The external responsiveness of HUPI was evaluated as recommended by Husted et al in three different populations[19]. We used Pearson correlation coefficient to describe how changes from baseline (ΔHUPI) to different follow-up visits (ACT-RAY visits 12, 24 or 52 weeks; PROAR visit 6 months; EMECAR visit 4 years) correlated with corresponding changes in the values of global disease activity assessed by patient (ΔGDA-Pat), ΔGDA-Phy, ΔDAS28-ESR, ΔDAS28-CRP. Spearman correlation was used with ΔSDAI and ΔCDAI, since the values of these indices do not follow a normal distribution. Internal responsiveness was also evaluated using standardized effect size (SES) calculated with MS Excell 2007 for Windows as the mean difference between baseline and each previously mentioned time points divided by the pooled standard deviation, as described by Hedges and Olkin [20, 21].
We evaluated how HUPI-based response criteria[13] behave in comparison to EULAR response criteria[22] using data from ACT-RAY. First, we tabulated the response with each set of response criteria and cross-tabulated them. To determine the accuracy of both response criteria, we used the percentage of correctly classified patients from the best fitted models with ΔGDA-Phy as external criterion. ΔGDA-Phy was used to avoid circularity, since neither HUPI nor DAS28 include this variable in their computation. Linear regression models using generalized linear solutions (Stata’s glm command with the default option) were performed with ΔGDA-Phy (from baseline to different time points) as dependent variable and HUPI-based and EULAR response criteria as categorical variables. Beta coefficients with 95% confidence intervals (95%CI) for “Moderate” and “Good response” by either definition were reported, along with the Akaike information criteria (AIC) from each model (S6 and S7 Tables). The later allow us to identify the best model; given two different regression models fitted on the same data, the model with the smallest AIC value is considered the best [23].
Results
Assessment of disease activity with different indices in three different populations
Table 1 shows a description of the three study populations. In all three, about 75% of patients were women and mean age at baseline ranged from 53 to 61 years. As part of the inclusion criteria, patients from the early arthritis cohort had the disease for less than a year in contrast with about 8 years in ACT-RAY and EMECAR. As expected, patients from the clinical trial showed the highest baseline disease activity and disability, EMECAR patients showed mid values, and those from PROAR showed the lowest scores of disease activity and disability (Table 1).
As a result of HUPI allowing calculation from CRP or ESR, whichever available at the study visit—a strategy to minimize missing data—, the HUPI was calculated in more visits than the other indices in the three populations: 99.8% of visits in ACT-RAY; 96.7% in PROAR; and 92.3% in EMECAR, with the only exception of CDAI in PROAR: 98.6% (S3 Table).
In patients from ACT-RAY, baseline HUPI values show a “ceiling effect” with more than 40% of patients at the highest score of the index (12 units; upper left panel in Fig 1). The remaining indices did not show this effect, with less of 5% at the highest value of SDAI and no patient at the highest score of DAS28-ESR, DAS28-CRP and CDAI (remaining panels in Fig 1). All indices showed improvement of disease activity after starting treatment with tocilizumab (Fig 1).
Data are presented as interquartile range (p75 upper edge, p25 lower edge, p50 midline in the box), p95 (line above the box) and p5 (line below the box). Dots represent outliers.
Patients from PROAR and EMECAR showed lower disease activity at baseline, so no “ceiling effect” was observed in HUPI (Fig 1 left panels of mid and lower row). Disease activity improvement with all indices was observed in PROAR after starting DMARD treatment in this early arthritis population (Fig 1 mid row). Limited improvement was observed in EMECAR (Fig 1 lower row).
Responsiveness of HUPI versus traditional indices
Despite its baseline “ceiling effect” in the ACT-RAY clinical trial, the change in HUPI score at week 12 had a good correlation with ΔGDA-Pat and slightly lower with ΔGDA-Phy. Consequently, the correlation was very good with ΔDAS28 either with ESR or CRP, and slightly lower with ΔSDAI or ΔCDAI (Table 2 and Fig 2).
Red dots represent values in which there was clear discordance between ΔHUPI and other measuments of improvement. The correlation coefficients are shown at Table 2.
In ACT-RAY correlations tended to improve when comparing ΔHUPI from baseline and weeks 24 and 52 and the corresponding changes of the other variables (Table 2). Interestingly, Fig 2 shows that there were individual important disparities (red dots) between ΔHUPI and ΔSDAI or ΔCDAI.
We hypothesized that this correlation would not be perfect since HUPI was specifically developed to avoid gender bias of DAS28 and SDAI. In addition, HUPI does not include GDA-Phy in its calculations. So, in order to be able to compare the respective sensitivity to change (Internal responsiveness), we calculated the SES for each variable at the three time points studied. The SES for HUPI was always the highest in the three populations at all times studied (Fig 3 and S4 Table). In addition, the 95% CI of HUPI’s SES did not overlap with those from GDA-Pat, GDA-Phy, SDAI and CDAI at any time in ACT-RAY (Fig 3A and S4 Table).
Data are shown as the standardized effect size (black circle) and its 95% confidence interval (black bar) at A) week 52 for ACT-RAY patients; B) week 24 for PROAR patients; and C) year 4 for EMECAR patients.
Similar findings were observed in patients from PROAR, although lower SES were observed since the baseline disease activity was lower than that of patients in ACT-RAY (Fig 1), with no significant differences across indices (Fig 3B and S4 Table).
On the other hand, limited disease activity improvement had been described with DAS28 in EMECAR[18] and the data with HUPI were consistent with these previous findings (Fig 3C and S4 Table).
Comparison of EULAR response criteria and HUPI-based response criteria
In ACT-RAY, it was possible to determine HUPI response in more patients than with EULAR response criteria either at week 12 (528 vs. 518), week 24 (509 vs. 503) or week 52 (418 vs. 412). The lower number of assessments with EULAR response was due to missing ESR, required to calculate DAS28. In addition, the proportion of patients with no response was higher with HUPI than with EULAR response criteria, although gradually being the proportions closer along the follow-up (Fig 4A and 4B and S1 Fig panel A). Similar findings were observed in PROAR and EMECAR (S2 Fig).
A) Percentage of patients getting none, moderate or good response at week 12. B) Percentage of patients getting none, moderate or good response at week 52 (See S1 Fig panel A for information at week 24). C) Association of change in global disease assessment by physician (GDA-Phy) with the different categories of EULAR response (See S1 Fig panel B for information at weeks 12 and 24). D) Association of change in GDA-Phy with the different categories of HUPI response (See S1 Fig panel B for information at weeks 12 and 24). Data in panels C and D are shown as the predicted mean change in GDA-Phy with its 95% confidence interval for each category obtained from the linear regression models showed in S6 Table with the command marginsplot of Stata.
Table 3 shows that response to treatment was equally classified by both criteria in > 65% of patients at week 12 and it gradually increased to >80% of patients at week 52. Three patients were classified as good responders with EULAR response criteria and non-responders with HUPI at all three time-points, whereas 1 patient was classified as good responder with HUPI but non-responder with EULAR only at week 12. The characteristics of these patients are shown in S5 Table. In summary, 3 patients had high number of tender joints, but low number of swollen joints, at baseline that improved in terms of tender joints but neither in terms of swollen counts, nor APR or GDA-Pat. By contrast, the patient with no response by EULAR criteria but good response with HUPI was a female patient with extremely high number of tender joints that did not improve with treatment, whereas the remaining parameters were low at baseline and improved with treatment.
Finally, to analyze which response set may be more accurate, we used as external criterion the ΔGDA-Phy from baseline to 12, 24 and 52 weeks. As shown in Fig 3C and S1 Fig panel B, in average an improvement of GDA-Phy≤20 was associated with no response at all time points for HUPI response criteria and at week 12 for EULAR-RC. In the following time-points EULAR no response tended to be associated with lower ΔGDA-Phy. Regarding moderate and good responses, by HUPI criteria the average improvement in ΔGDA-Phy tended to be more lineal, whereas by EULAR criteria, higher improvement in ΔGDA-Phy were needed to reach moderate response and then lower improvements were needed to reach good response with respect moderate response (Fig 2C and 2D and S2 Fig panel B). This can be appreciated with the beta coefficients of the linear correlation models, in which the Akaike information criteria was always lower for the models run with HUPI than with EULAR response, suggesting that the former were better fitted (S6 Table). Similar results were observed in the PROAR and EMECAR cohorts (S3 Fig and S7 Table).
Discussion
HUPI was developed in an intent to provide a more accurate tool for assessing disease activity in patients with early RA and undifferentiated arthritis [12]. Validation is an ongoing process and new instruments like the HUPI need to be tested in different populations; therefore, we aimed to further validate HUPI by evaluating its responsiveness and the recently proposed HUPI-based response criteria [13]. This was particularly necessary in patients from clinical trials, whose baseline disease activity, as part of the general inclusion criteria, is usually very high. At present, there is no gold standard to assess disease activity in RA, nevertheless we used pooled indices of multiple measures that have been previously developed based on the Core Data Set proposed by Felson et al[24]. All in all, the data presented in this work showed that HUPI exhibits comparable responsiveness to that of DAS28 and better than SDAI and CDAI. In addition, our data suggest that HUPI-based response criteria are slightly more stringent than EULAR’s.
Baseline data from ACT-RAY have allowed confirming that in a clinical trial setting HUPI has a “ceiling effect”, likely due to its design. Remarkably, this ceiling effect was not detected in the other two cohorts more representative of patients seen in routine care. Nevertheless, in our opinion, patients with 5 or more swollen and tender joints and GDA-Pat higher than 50/100 and CRP levels higher 1 mg/dl show very high disease activity and need special therapeutic approaches irrespective of the magnitude of these variables above these limits.
Despite this “ceiling effect”, HUPI showed the largest sensitivity to change in all three populations, with SES superior to those of SDAI, CDAI and GDA, either by physician and patient. We recognize that SES is not the best statistic to report responsiveness, as it is only assessing internal responsiveness; however, it allows comparison across indices with varying range of values. In addition, similar results were reported using other methods when we described the index [12]. The poorer responsiveness of SDAI and CDAI may be a consequence of their design’s simplicity, leading to non-normally distributed variables with a highly spread range of values in moderate and high disease activity. This can also explain the disparities in response measurements showed at Fig 2.
On the other hand, since the responsiveness of HUPI is quite similar to that of DAS28, the response criteria based in both indices behave very similarly. Small differences have been detected, being HUPI slightly more stringent, with larger percentages of patients considered non-responders in ACT-RAY compared to percentages of EULAR response. These differences decreased along follow-up, although they were still detected at week 52, being the fast effect of tocilizumab on APR a possible explanation, since ESR is highly weighted in DAS28 [25]. Another possibility to explain this discrepancy is the tender joint count, that is also highly weighted in DAS28 and in HUPI is weighted differently by gender [12]. In this regard, it has been described that the presence of fibromyalgia can interfere with the assessment of disease activity with DAS28, since it impacts in the subjective components of the index, such as tender joint count[26].
Nevertheless, it is difficult to know whether being HUPI-based response more stringent than EULAR response may be an advantage or a disadvantage. In ACT-RAY, all patients were treated with tocilizumab plus placebo or methotrexate, showing no statistical differences between groups, but a statistical difference from baseline in both groups [15]. On these grounds, we considered patients from both groups experiencing a similar change; however, it was not our aim to evaluate treatment effect with any index. For this reason, we cannot determine whether HUPI-based response is as stringent in a “real” placebo group as in an active treatment group, nor whether it helps discriminating the effect of the drug.
Conclusion
In summary, despite its “ceiling effect”, HUPI shows good responsiveness in all the scenarios tested. In addition, the response criteria based on this new index seems to be more stringent than the EULAR response criteria, although we need to deepen in the study of this characteristic to determine whether it could be more efficient to detect differences between placebo and active treatment.
Supporting information
S1 Fig. Comparison of EULAR and HUPI response criteria from baseline at different weeks of patients from ACT-RAY.
A) Percentage of patients getting none, moderate or good response at week 24. B) Correlation of change in global disease assessment by physician (GDA-Phy) with the different categories of EULAR response and HUPI response at week 12 and week 24. Data in panels in section B are shown as the predicted mean change in GDA-Phy with its 95% confidence interval for each category obtained from the linear regression models showed in S6 Table.
https://doi.org/10.1371/journal.pone.0214717.s001
(TIF)
S2 Fig. Comparison of EULAR and HUPI response criteria from baseline at different weeks of patients from PROAR and EMECAR.
A) Percentage of patients getting none, moderate or good response at month 6, month 12 and month 24 in PROAR. B) Percentage of patients getting none, moderate or good response at 4 years of follow-up in EMECAR.
https://doi.org/10.1371/journal.pone.0214717.s002
(TIF)
S3 Fig. Correlation of change in global disease assessment by physician (GDA-Phy) with the different categories of EULAR response and HUPI response in PROAR at months 6 (A), 12 (B) or 24 (C) and EMECAR at year 4th (D).
Data are shown as the predicted mean change in GDA-Phy with its 95% confidence interval for each category obtained from the linear regression models showed in S7 Table.
https://doi.org/10.1371/journal.pone.0214717.s003
(TIF)
S1 Table. Scoring of the variables used to calculate HUPI.
https://doi.org/10.1371/journal.pone.0214717.s004
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S2 Table. DMARD prescription during follow-up in PROAR.
https://doi.org/10.1371/journal.pone.0214717.s005
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S3 Table. Number of visits from the different cohorts in which it was possible to calculate each index.
https://doi.org/10.1371/journal.pone.0214717.s006
(DOCX)
S4 Table. Standardized size effects (95% confidence interval) of changes in disease activity assessed with HUPI and several other commonly used to assess response in rheumatoid arthritis.
https://doi.org/10.1371/journal.pone.0214717.s007
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S5 Table. Characteristics of patients with opposite classification with EULAR and HUPI response criteria.
https://doi.org/10.1371/journal.pone.0214717.s008
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S6 Table. Accuracy of EULAR-RC and HUPI-RC assessed by their correlation with change in GDA-Phy between baseline and different visits of ACT-RAY.
https://doi.org/10.1371/journal.pone.0214717.s009
(DOCX)
S7 Table. Accuracy of EULAR and HUPI-based response criteria assessed by their correlation with ΔGDA-Phy in PROAR and ΔGDA-Pat in EMECAR.
https://doi.org/10.1371/journal.pone.0214717.s010
(DOCX)
S1 File. EMECAR data in longitudinal format.
This Excell file includes in longitudinal format all data from EMECAR cohort used to develop the results shown in this paper.
https://doi.org/10.1371/journal.pone.0214717.s011
(XLS)
S2 File. EMECAR data in wide format.
This Excell file includes in wide format all data from EMECAR cohort used to develop the results shown in this paper.
https://doi.org/10.1371/journal.pone.0214717.s012
(XLS)
S3 File. PROAR data in longitudinal format.
This Excell file includes in longitudinal format all data from PROAR cohort used to develop the results shown in this paper.
https://doi.org/10.1371/journal.pone.0214717.s013
(XLS)
S4 File. PROAR data in wide format.
This Excell file includes in wide format all data from PROAR cohort used to develop the results shown in this paper.
https://doi.org/10.1371/journal.pone.0214717.s014
(XLS)
Acknowledgments
We want to thank all researchers involved in data collection at:
ACT-RAY
Lead authors: Dougados, M. CHU Paris Centre—Hôpital Cochin. France. E-mail: maxime.dougados@cch.ap-hop-paris.fr; Huizinga, T. Leiden University Medical Center, Netherlands. E-mail: T.W.J.Huizinga@lumc.nl
Abu Shakra, M. Soroka Medical Center. Israel
Alberts, A. West Broward Rheumatology Associates, Inc. United States
Alperi Lopez, M. Hospital Univ. Central de Asturias. Spain
Amital, H. Chaim Sheba Medical Center. Israel
Aringer, M. Universitätsklinikum "Carl Gustav Carus". Germany
Aslanidis, S. Hippokratio Hospital. Greece
Berenbaum, F. Hopital Saint Antoine. France
Bijlsma, H. Academisch Medisch Centrum Utrecht. Netherlands
Blanco Garcia, FJ. Complejo Hospitalario Universitario A Coruña. Spain
Bliddal, H. Frederiksberg Sygehus. Denmark
Borofsky, M. Clinical Research Center of Reading. United States
Brocq, O. Ch Princesse Grace. Monaco
Buldakov, S. Republican Clinicodiagnostic Center. Russian Federation
Cantini, F. Presidio Ospedaliero Misericordia e Dolce. Italy
Carreño Perez, L. Hospital General Universitario Gregorio Marañon. Spain
Chahade, W. Hospital Estadual do Servidor Publico. Brazil
Ciconelli, R. Universidade Federal de Sao Paulo. Brazil
Codreanu, C. Centrul de Boli Reumatismale Dr. Ioan Stoia. Romania
Dahlqvist, SR. Norrlands Universitary Hospital. Sweden
Damjanov, N. Institut Za Reumatologiju. Serbia
Diamantopoulos, A. Sørlandet Sykehus Kristiansand. Norway
Dimdina, L. Clinical University Hospital Gailezers. Latvia
Dimic, A. Institut Za Prevenciju, Lecenje I Rehabilitaciju. Serbia
Dorokhov, A. State Institution of Health Care—Territorial Clinical Hospital. Russian Federation
Dubikov, A. City Clinical Hospital # 2. Russian Federation
Fadienko, G. Glpu Tjumen Regional Clinical Hospital #1. Russian Federation
Fanø, N. Sjællands Universitetshospital, Køge. Denmark
Ferreira, G. Hospital das Clinicas–UFMG. Brazil
Gabrielli, A. Uni Politecnica Delle Marche; Ist. Di Clinica Medica Generale Ematologia Ed Immunologia Clinica. Italy
Gaffney, K. Norfolk & Norwich Hospital. United Kingdom
Gaudin, P. Hopital Sud. France
Gerlag, DM. Academisch Medisch Centrum. Netherlands
Gerli, R. Osp S. Maria Misericordia Dip. Italy
Gonçalves, CR. Hospital das Clinicas–FMUSP. Brazil
Hansen, MS. Gentofte Hospital. Denmark
Hanvivadhanakul, P. Thammasat University Hospital. Thailand
Høili, C. Sykehuset Ostfold Moss HF. Norway
Hou, A. Inland Rheumatology; Clinical Trials, Inc. United States
Hunter, J. Gartnavel General Hospital. United Kingdom
Ilic, T. Clinical Centre of Vojvodina. Serbia
Ionescu, R. Spitalul Sf Maria. Romania
Kaine, J. Sarasota Arthritis Center. United States
Kakurina, N. Clinical Hospital of Daugavpils. Latvia
Kamalova, R. Republican clinical hospital. Russian Federation
Kelly, T. Innovative Health Research. United States
Knyazeva, L. GMU Kursk Regional Clinical Hospital. Russian Federation
Krumina, L. L.Krumina GP practice. Latvia
Kurthen, R. Praxis Dr. med. Reiner Kurthen. Germany
Lagrone, RP. St. Thomas Hospital. United States
Lapadula, G. Ospedale Policlinico Di.M.I.M.P. Italy
Lavrentjevs, V. P.Stradins Clinical University Hospital. Latvia
Lawson, JG. Piedmont Arthritis Clinic. United States
Lazic, Z. Clinical Center Kragujevac. Serbia
Lejnieks, A. Rakus Clinic Linezers. Latvia
Levy, Y. Meir Medical Center. Israel
Lexberg, Å. Drammen sykehus Vestre Viken HF. Norway
Mader, R. Haemek Hospital. Israel
Mariette, X. Ch De Bicêtre. France
Markovits, D. Rambam Medical Center. Israel
Martin Mola, E. Htal. La Paz. Spain
Maugars, Y. Hopital Hotel Dieu Et Hme. France
Maymo Guarch, J. Hospital del Mar. Spain
Mazurov, VI. Sbei Of Hpe "Northwestern State Medical University N.A. I.I.Mechnikov". Russian Federation
Mikkelsen, K. Revmatismesykehuset. Norway
Morovic Vergles, J. Clinical Hospital Dubrava. Croatia
Nabizadeh, S. Martina Hansen Hospital. Norway
Nanagara, R. Khon Kaen University. Thailand
Nasonov, EL. Fsbi "Scientific Research Institute Of Rheumatology" Of Russian Academy Of Medical Sciences. Russian Federation.
Navarro Sarabia, F. Hospital Universitario Virgen Macarena. Spain
Neumann, T. Universitätsklinikum Jena. Germany
Novak, S. Rheumatology and Clinical Immunology. Croatia
Olech, E. Oklahoma Medical Research Foundation. United States
Oza, M. Arthritis/Osteoporosis Treatment Center. United States
Paran, D. Sourasky / Ichilov Hospital. Israel
Parsik, E. North Estonian Regional Hospital. Estonia
Pegram, S. Rheumatic Disease Clin Res Ctr. United States
Pombo Suarez, M. Hospital Nuestra Señora de la Esperanza. Spain
Popova, T. Municipal Autonomous Institution Of Healthcare "City Clinical Hospital #40". Russian Federation
Puechal, X. Ch Du Mans. France
Raja, N. Agilence Arthritis and Osteoporosis Medical Center, Inc. United States
Ridley, D. St. Paul Rheumatology. United States
Rosner, I. Bnei Zion Medical Center. Israel
Rubbert-Roth, A. Klinik der Uni zu Köln. Germany
Rudin, A. Sahlgrenska Universitetssjukhuset. Sweden
Saraux, A. Hopital La Cavale Blanche. France
Saulite-Kandevica, D. D.Saulite-Kandevica Private Practice. Latvia
Settas, L. Ahepa Hospital. Greece
Sfikakis, P. Laiko General Hospital. Greece
Sheeran, T. Cannock Chase Hospital. United Kingdom
Sizikov, A. FSBI Scientific Research Institute of Clinical Immunology of SB of RAMS. Russian Federation
Stamenkovic, D. Clinical Hospital Centre Rijeka. Croatia
Stefanovic, D. Military Medical Academy. Serbia
Stolow, JB. Texas Arthritis Research Center. United States
Tan, AL. Chapel Allerton Hospital. United Kingdom
Tebib, J. Ch Lyon Sud. France
Tishler, M. Assaf Harofe. Israel
Tony, HP. Universitätsklinikum Würzburg. Germany
Troum, OM. United States
Uaratanawong, S. Vajira Hospital. Thailand
Ucar Angulo, E. Hospital de Basurto. Spain
Valenzuela, G. Berma Research Group. United States
van der Laken, K. VU Medisch Centrum. Nehterlands
Van Laar, J. School of Clinical Medical Services. United Kingdom
van RIEL, P.L.C.M. Akademisch Ziekenhuis St. Radboud. Netherlands
Vasilopoulos, D. Hippocrateio Hospital of Athens. Greece
Veldi, T. East Tallinn Central Hospital. Estonia
Vinogradova, I. State Institution of Healthcare Ulyanovsk Regional Clinical Hospital. Russian Federation
Vosse, D. Academisch Ziekenhuis Maastricht. Netherlands
Wassenberg, S. Evangelisches Fachkrankenhaus. Germany
Weidmann, C. Medvin Clinical Research. United States
Weitz, M. Center For Arthritis. United States
Wollenhaupt, J. Schön Klinik Hamburg-Eilbek Klinik für Rheumatologie. Germany
Xavier, R. Hospital das Clinicas–UFRGS. Brazil
Yakupova, S. Kazan State Medical University. Russian Federation
Zagar, I. Klinicki Bolnicki Centar Zagreb. Croatia
Zavgorodnaja, T. P.Stradins Clinical University Hospital. Latvia
Zemerova, E. Khanty-Mansiysk Autonomous Area—Ugri Region Clinical Hospital. Russian Federation
Zisman, D. Carmel Hospital. Israel
Zonova, E. FSBI Scientific Research Institute of Clinical and Experimental Lymphology of SB of RAMS. Russian Federation
EMECAR
Lead author: Loreto Camona, InMusc. Spain. E-mail: loreto.carmona@inmusc.eu
Abasolo Alcazar L, Hospital Clínico Universitario San Carlos, Madrid. Spain
Alegre de Miguel C, Hospital de Malalties Reumatiques, Barcelona. Spain
Andreu Sánchez JL, Clínica Puerta de Hierro. Majadahonda. Spain
Aragón Díez A, Hospital Nuestra Señora del Prado. Talavera de la Reina. Spain
Balsa Criado A, Hospital La Paz. Madrid. Spain
Batlle Gualda E, Hospital General Universitario de Alicante. Spain
Belmonte Serrano MA, Hospital General de Castellón. Spain
Beltrán Audera J, Hospital Clínico Universitario de Zaragoza. Spain
Beltrán Fabregat J, Hospital General de Castellón. Spain
Bonilla Hernan G, Hospital La Paz. Madrid. Spain
Caro Fernández N, Hospital Nuestra Señora del Prado. Talavera de la Reina. Spain
Casado E, Hospital Universitario Germans Trias i Pujol. Badalona. Spain
Cebrian Mendez L, Hospital Gregorio Marañón. Madrid. Spain
Corteguera Coro M, Hospital Nuestra Señora de Sonsoles. Avila. Spain
Cuadra Díaz JL, Hospital Nuestra Señora del Carmen. Ciudad Real. Spain
Cuesta E, Hospital Virgen de La Luz. Cuenca. Spain
Fiter Aresté J, Hospital Son Dureta. Palma de Mallorca. Spain
Freire Gonzalez M, Hospital Gregorio Marañón. Madrid. Spain
Galindo Izquierdo M, Hospital 12 de Octubre. Madrid. Spain
García Meijide JA, Hospital Clínico Universitario de Santiago de Compostela. Spain
García Gómez MC, Hospital Universitario de Bellvitge. Hospitalet de Llobregat. Spain
Giménez Ubeda E, Hospital Clínico Universitario de Zaragoza. Spain
Gómez Centeno E, Hospital Clinic i Provincial. Barcelona. Spain
Gómez Vaquero C, Hospital Universitario de Bellvitge. Hospitalet de Llobregat. Spain González Fernández MJ, Hospital de Malalties Reumatiques. Barcelona. Spain
González Gómez ML, Hospital Gregorio Marañón. Madrid. Spain
González Hernández T, Instituto Provincial de Rehabilitación. Madrid. Spain
González-Alvaro I, Hospital Universitario la Princesa. Madrid. Spain
González-Montagut Gómez C, Hospital Virgen de La Luz. Cuenca. Spain
Grandal Delgado Y, Hospital General de Jerez de La Frontera. Spain
Gratacos Masmitja J, Complejo Hospitalario del Parc Tauli. Sabadell. Spain
Hernández del Río A, Hospital Juan Canalejo. A Coruña. Spain
Instxaurbe AR, Hospital de Basurto. Bilbao. Spain
Irigoyen Oyarzabal MV, Hospital General Carlos Haya. Malaga. Spain
Jiménez Palop M, Hospital Nuestra Señora de Sonsoles. Avila. Spain
Juan Mas A, Hospital Son Llatzer. Palma de Mallorca. Spain
Júdez Navarro E, Hospital Clínico Universitario San Carlos. Madrid. Spain
Larrosa Padro M, Complejo Hospitalario del Parc Tauli. Sabadell. Spain
López Longo FJ, Hospital Gregorio Marañón. Madrid. Spain
Loza Santamaria E, Hospital Clínico Universitario San Carlos. Madrid. Spain
Maese Manzano J, Fundación Española de Reumatología.
Manero Ruiz FJ, Hospital Clínico Universitario de Zaragoza. Spain
Mateo Bernardo I, Hospital 12 de Octubre. Madrid. Spain
Mayordomo González L, Hospital Universitario de Valme. Sevilla. Spain
Mazzucheli R, Hospital Fundación de Alcorcón. Spain
Medrano San Idelfonso M, Hospital Clínico Universitario de Zaragoza. Spain
Naranjo Hernández A, Hospital de Gran Canaria Dr. Negrín. Las Palmas. Spain
Pecondón Español A, Hospital Clínico Universitario de Zaragoza. Spain
Peiró Callizo E, Hospital Virgen de La Luz. Cuenca. Spain
Quirós Donate J, Hospital Fundación de Alcorcón. Spain
Ramos López P, Hospital Príncipe de Asturias. Alcala de Henares. Spain
Rivera Redondo J, Instituto Provincial de Rehabilitación. Madrid. Spain
Rodríguez Gómez M, Complejo Hospitalario Cristal-Piñor. Ourense. Spain
Rodríguez López M, Hospital Arquitecto Marcide. Lugo. Spain
Roselló Pardo R, Hospital General San Jorge. Huesca. Spain
Sampedro Alvarez J, Hospital Virgen de La Salud. Toledo. Spain
Sanmartí Sala R, Hospital Clinic i Provincial. Barcelona. Spain
Santos Rey Rey J, Hospital Virgen de La Salud. Toledo. Spain
Tena Marsá X, Hospital Universitario Germans Trias i Pujol. Badalona. Spain
Tenorio Martín M, Hospital del Insalud-Ceuta. Spain
Torres Martín MC, Hospital Nuestra Señora de Sonsoles. Avila. Spain
Ureña Garnica I, Hospital General Carlos Haya. Malaga. Spain
Valdazo de Diego JP, Hospital General Virgen de La Concha. Zamora. Spain
Valls M, Hospital Universitario Germans Trias i Pujol. Badalona. Spain
Villaverde García V, Hospital La Paz. Madrid. Spain
Zarco Montejo P, Hospital Fundación de Alcorcón. Spain
Zubieta Tabernero J, Hospital Virgen de La Salud. Toledo. Spain
PROAR
Lead authors: Alejandro Balsa. Hospital Universitario La Paz, Madrid. Spain. E-mail: alejandro.balsa@salud.madrid.org; Raimon Sanmartí. Hospital Clinic, Barcelona. Spain. E-mail: sanmarti@clinic.ub.es
Cabezas, JA. Complejo Hospitalario San Millan-San Pedro, Logroño. Spain
Cantalejo, M. Hospital Univ de Getafe. Spain
Chamizo, E. Hospital de Mérida. Spain
Ciruelo, E. Hospital Gral de Segovia. Spain
Corrales, A. Hospital Comarcal de Laredo. Spain
Cruz, A. Hospital Severo Ochoa, Leganés. Spain
Díaz, C. Hospital de La Santa Creu i Sant Pau, Barcelona. Spain
Fiter, J. H Son Dureta, Palma de Mallorca. Spain
Freire, MM. Hospital Juan Canalejo, A Coruña. Spain
Galindo, M. Hospital 12 de Octubre, Madrid. Spain
García de Vicuña, MR. Hospital La Princesa, Madrid. Spain
Gelman, SM. Hospital Gral de Manresa, Spain
González Crespo, R. Hospital 12 de Octubre, Madrid. Spain
González Fernández, C. Hospital Gregorio Marañon, Madrid. Spain
González Fernández, MJ. Institut Dexeus, Barcelona. Spain
González Hernández, T. Instituto Prov de Rehabilitación, Madrid. Spain
Gracia, A. Hospital de Sagunto. Spain
Granados, J. Hospital Mutua de Terrassa. Spain
Guzman, MA. Hospital Virgen de las Nieves, Granada. Spain
Irigoyen, MV. Hospital Gral Carlos Haya, Malaga. Spain
Juan, A. H Son Dureta, Palma de Mallorca. Spain
Juanola, X. (Hospital de Bellvitge, L'Hospitalet de Llobregat. Spain
Laiz, A. Hospital de La Santa Creu i Sant Pau, Barcelona. Spain
Manero, FJ. Hospital Univ Miguel Servet, Zaragoza. Spain
Martínez, A. Hospital Gral Univ de Alicante. Spain
Martinez, F. Hospital Univ Reina Sofía, Córdoba. Spain
Mata, C. Hospital Comarcal Sierrallana, Torrelavega. Spain
Maymo, J. Imas, Barcelona. Spain
Navarro, FJ. Hospital Gral Univ de Elche. Spain
Peiro, E. Hospital Virgen de La Luz, Cuenca. Spain
Pérez, F. Hospital Gral de Requena. Spain
Pérez, G. Hospital del Insalud, Ceuta. Spain
Pérez, M. Hospital Gral Carlos Haya, Malaga. Spain
Pujol, M. Hospital Mutua de Terrassa. Spain
Quirós, J. Hospital Fundación Alcorcón. Spain
Ribas, B. Hospital San Juan de Dios, Palma de Mallorca. Spain
Riera, M. Hospital Creu Roja, Barcelona. Spain
Rivera, J. Instituto Prov de Rehabilitación, Madrid. Spain
Rodríguez, JM. Hospital Univ de Getafe. Spain
Rodríguez Gómez, M. Complejo Hospitalario Cristal-Piñor, Orense. Spain
Roselló, R. Hospital Gral San Jorge, Huesca. Spain
Tenorio, M. Hospital del Insalud, Ceuta. Spain
Toyos, FJ. Hospital Univ Virgen Macarena, Sevilla. Spain
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