Remission of rheumatoid arthritis in clinical practice: Application of the American College of Rheumatology/European League Against Rheumatism 2011 remission criteria

Remission of Rheumatoid Arthritis in Clinical Practice:Application of the ACR/EULAR 2011 Remission Criteria

Shadi H. Shahouri, M.D,Arthritis and Rheumatology Clinics of Kansas, Wichita, KS, University of Kansas School ofMedicine, Wichita, KS

Kaleb Michaud, Ph.D.,University of Nebraska Medical Center, Omaha, Nebraska, National Data Bank for RheumaticDiseases, Wichita, Kansas

Ted R. Mikuls, M.D., M.S.P.H.,Omaha VA Medical Center and University of Nebraska, Omaha, Nebraska

Liron Caplan, M.D., Ph.D.,Denver VA Medical Center and University of Colorado, Denver, Colorado

Timothy S. Shaver, M.D,Arthritis and Rheumatology Clinics of Kansas, Wichita, KS, University of Kansas School ofMedicine, Wichita, KS

James D. Anderson, M.D,Arthritis and Rheumatology Clinics of Kansas, Wichita, KS, University of Kansas School ofMedicine, Wichita, KS

David N. Weidensaul, M.D,Arthritis and Rheumatology Clinics of Kansas, Wichita, KS, University of Kansas School ofMedicine, Wichita, KS

Ruth E. Busch, A.R.N.P,Arthritis and Rheumatology Clinics of Kansas, Wichita, KS, Wichita State University, Wichita, KS

Shirley Wang, M.D, andArthritis and Rheumatology Clinics of Kansas, Wichita, KS, University of Kansas School ofMedicine, Wichita, KS

Frederick Wolfe, M.D.National Data Bank for Rheumatic Diseases, Wichita, Kansas, University of Kansas School ofMedicine, Wichita, Kansas

AbstractPurpose—To describe use of the ACR/EULAR (AE) rheumatoid arthritis (RA) remissioncriteria in clinical practice.

Corresponding Author: Frederick Wolfe, M.D., National Data Bank for Rheumatic Diseases, 1035 N. Emporia, Suite 288, Wichita,KS 67214, Tel: (316) 263-2125, Fax: (316) 263-0761, [email protected] conflicts of interest: None.Contribution of authors: The manuscript was drafted by F Wolfe. The statistical analyses were performed by F Wolfe and KMichaud. All authors reviewed and aided in the preparation of the manuscript, and approved submission of the manuscript. Note: Dr.Shahouri and Dr. Michaud contributed equally to this study.

NIH Public AccessAuthor ManuscriptArthritis Rheum. Author manuscript; available in PMC 2012 November 1.

Published in final edited form as:Arthritis Rheum. 2011 November ; 63(11): 3204–3215. doi:10.1002/art.30524.

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Methods—We examined remission in the US Veterans Affairs RA (VARA) registry of 1,341patients (91% men) with 9,700 visits and a community rheumatology practice (ARCK) of 1,168patients (28% men) with 6,362 visits. We studied cross-sectional and cumulative probabilities,agreement among various remission criteria, and aspects of reliability using Boolean definitionsand CDAI and SDAI methods proposed by AE.

Results—By AE definition for community practice (swollen and tender joints ≤1, patient global≤1), cross-sectional remission was 7.5% (6.4, 8.7) for ARCK and 8.9% (7.9, 9.9) for VARA.Cumulative or remission at any observation was 18.0% (ARCK) and 24.4% (VARA) over a meanof 2.2 years. Addition of ESR or CRP to criteria reduced remission to 5.0-6.2%, and use of CDAI/SDAI increased proportions to 6.9-10.1%. 1.8%-4.6% of patients met remission criteria at ≥2visits. Agreement between criteria definitions was good by Kappa and Jaccard measures. Amongpatients in remission, the probability of a remission lasting 2 years was 6.0%-14.1%. Among allpatients the probability of a remission lasting 2 years was <3%. Remission and examination resultsvaried substantially among physicians by multilevel analyses.

Conclusion—Cross-sectional remission occurs at 5.0%-10.1%, with cumulative remission 2-3times greater. Long-term remissions are rare. Problems with reliability and agreement limit criteriausefulness in the individual patient. However, the criteria can be an effective method formeasuring clinical status and treatment effect in groups of patients in the community.

KeywordsRheumatoid arthritis; Remission; Reliability

Remission in rheumatoid arthritis (RA) was first described and quantified by Pemberton in1927 (1), followed by Thompson in the next decade (2). In 1981, Pinals et al. publishedPreliminary Criteria for Remission in Rheumatoid Arthritis (3). These criteria, whichbecame official American College of Rheumatology (ACR) criteria, were difficult to useand did not have a clear basis in scientific measurement. Over the years, a series of different,often ad hoc criteria were proposed or used in publications. These additional criteria havebeen described in detail in a number of important publications (4-6).

In 2011, the ACR and European League Against Rheumatism (EULAR) jointly publishedThe American College of Rheumatology/European League against Rheumatism PreliminaryDefinition of Remission in Rheumatoid Arthritis for Clinical Trials (6). In this paper theauthors also suggested “that a definition of remission be developed for clinic based practicethat would not require an acute phase reactant, as long as it would capture remission asstringently as the measure employed for clinical trials,” and furthermore that “… core setmeasures should be used to define remission and that any definition of remission in clinicaltrials should look toward and make possible a similar definition in clinical practice.”

Remission in clinical practice is an important issue. For groups of patients assessed inobservational studies, remission can be a marker of disease severity and treatment response.The ACR/EULAR recommendation for the use of remission criteria in clinic-based practicesuggests, in addition, that the determination of remission be extended to the individualpatient. If applied to the individual patient, remission or the lack of it could serve as ameasure of treatment success that could be used by the patient, and by 3rd party payers andregulatory authorities to characterize the quality of health care, dictate access to care orgovern the use of specific therapies.

A large recent study of cross-sectional remission included 5,848 patients from 67 sites in 24countries (4). The authors evaluated 8 different criteria, 3 of which are particularly ofinterest to the current study: Clinical Disease Activity Index (CDAI) (7), Disease Activity

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Score-28 (DAS-28)(8) and RAPID-3 remission. Of these, only CDAI remission isrecognized by the new ACR/EULAR criteria. In this study of Sokka et al. the proportion inremission by CDAI criteria varied strikingly among countries, ranging from 0% to 35.3%,with a value of 18.5% in the US.

In the current study we obtained data from all patients and all clinic visits in multi-physiciansites, including a private practice rheumatology specialty group and 9 US VeteransAdministration outpatient rheumatology clinics that included 38 physicians. We used multi-level, repeated measures methods to examine the probability of remission at a given clinicvisit, the cumulative probability of remission, the probability of a second remission, and theduration of remission for each of the ACR/EULAR definitions. In addition, we evaluated thedegree of physician bias, the effect of patient global changes, and the agreement between thevarious definitions.

MethodsPatients and variables

From 10/5/2006 to 11/16/2010 1,435 patients with 15,152 visits were seen at the Arthritisand Rheumatology Clinics of Kansas (ARCK), a 5-physician rheumatology specialty clinic(9). All patients underwent assessments that included a count of 28 swollen joints (SJC), 28tender joint counts (TJC), physician and patient visual analog scale (VAS) globals(PhGlobal and PtGlobal), and the Health Assessment Questionnaire Disability Index-II(HAQ-II) (10). The erythrocyte sedimentation rate (ESR) was not systematically obtained atall visits for clinical and insurance reasons. In this study we evaluated the 1,168 patientswith 6,362 visits who had complete data for SJC, TJC, PtGlobal and ESR. All patients wereseen as part of routine medical care.

We also evaluated patients who were part of the US Veterans Affairs RA (VARA) registry,consortium of 9 sites (Dallas, Washington DC, Omaha, Salt Lake City, Denver, Jackson,Portland, Brooklyn, Iowa City) and 38 physicians, during the period 12/11/2002 through9/24/2010 (11, 12). Data collected included SJC, TJC, PtGlobal, PhGlobal, ESR, C-reactiveprotein (CRP), and the multi-dimensional Health Assessment Questionnaire Disability IndexMDHAQ (13). From 1,510 patients with 11,915 visits, we studied 1,341 patients and 9,700visits by restricting our analyses to those with complete data for SJC, TJC, PtGlobal andESR. All patients were seen as part of routine medical care.

From the above variables we calculated indices of RA disease activity including the DAS-28(8), the Patients Activity Scale- II (PAS-II) (14), the Routine Assessment of Patient IndexData 3 (RAPID-3) (15), the Simplified Disease Activity Index (SDAI) (7) and the CDAI (7).The PAS-II and RAPID 3 are essentially the same scale except that the PAS-II uses theHAQ-II and RAPID-3 uses the MDHAQ. The results of both scales are equivalent. Thescales represent (PtGlobal + patient pain + (3 × HAQ-II or MDHAQ)) /3. The SDAI is thesum of the TJC (on a 0-28 scale), SJC (0-28), PtGlobal (0-10), PhGlobal (0-10) and CRP(mg/dL). The CDAI is the sum of the TJC (0-28), SJC (0-28), PtGlobal (0-10), andPhGlobal (0-10).

From the above scales we created the following remission criteria as defined in the ACR/EULAR (AE) remission paper (6): AE 3 = ≤1 SJC + ≤1 TJC + PtGlobal ≤1. AE 3 ESR = ≤1SJC + ≤1 TJC + PtGlobal ≤1 + ESR <20 (men) or < 30 (women). AE CRP = ≤1 SJC + ≤1TJC + PtGlobal ≤1 + CRP ≤1. AE 4 = ≤1 SJC + ≤1 TJC + PtGlobal ≤1 + PhGlobal ≤1. AECDAI = CDAI ≤ 2.8. AE SDAI = SDAI ≤ 3.3.



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We also evaluated criteria that were not included in the ACR/EULAR recommendations:DAS-28 remission = DAS-28 <2.6 (4). PAS-II/RAPID-3 = PAS-II ≤1 or RAPID-3 ≤1 (4).For comparison with the ACR/EULAR paper we evaluated Minimal Disease Activity(MDA) criteria: MDA was present if the patient satisfied at least 5 of the following 7conditions: VAS pain ≤2 (range 0–10), SJC ≤1 (0–28), TJC ≤1 (0–28), HAQ ≤0.5 (0 –3),PtGlobal ≤2 (0 –10), PhGlobal ≤1.5 (0–10), and ESR ≤20 mm/hour, or if the patientsatisfied the following conditions: had no swollen joints, no tender joints, and an ESR ≤10mm/hour.

Statistical methodsPatients with complete data for the AE ESR criteria were included in Table 1; the cohortswere compared by randomly selecting an observation for each subject. We tested fordifferences between groups for individual variables by t-tests and chi square tests, and forjoints (SJC and TJC) and other activity variables (PtGlobal, PhGlobal, HAQ and PAS)simultaneously with multivariate means tests (Stata MVTEST procedure).

To determine the probability of remission we used all observations from each patient whomet AE ESR entry criteria. We determined the probability of remission at a givenobservation with Stata's population averaged XTREG procedure together with the Marginsprocedure. Separate evaluations were performed stratified by cohort (ARCK and VARA)and patients' gender (Table 2). We also determined probabilities of cumulative remission(remission at any time during follow up), probabilities of a second remission, and theprobabilities of remaining in remission for 3, 12 and 24 months. To determine the marginalprobability of one or more remissions we used Stata's random effects XTREG procedurefollowed by determination of the Intraclass correlation and marginal probability procedure(16). Additional probabilities were calculated for non-ACR/EULAR remission criteria(Table 2). We determined the durability of study remissions by the Kaplan-Meier life tableprocedures (Figure 1) (17).

In order to explore whether the same patients are classified as in remission by differentcriteria, we assessed agreement between remission measures by the Kappa statistic andJaccard's coefficient (18). We used the interpretation of Landis and Koch for kappa values:< 0 as indicating no agreement and 0–0.20 as slight, 0.21–0.40 as fair, 0.41–0.60 asmoderate, 0.61–0.80 as substantial, and 0.81–1.0 as almost perfect agreement (19). BothKappa and the Jaccard coefficients can be interpreted as % values. Kappa can be interpretedas the % agreement after correcting for chance. The Jaccard coefficient can be interpreted asthe % agreement after excluding joint negative pairs. Its utility lies in its ease ofdemonstrating the extent of clinically understandable agreement after exclusion of agreedupon criteria negative pairs.

To examine physician heterogeneity we determined the median odds ratio (MOR) afterperforming multilevel analyses using the Stata XTMELOGIT procedure and modelingphysicians and patients in separate random effects equations (Table 4). We used the MOR toexpress examiner variance in remission criteria (20, 21). The MOR quantifies differences(i.e., variance between examiners) by comparing patients with the same covariates but from2 randomly chosen examiners. This procedure yields a distribution of ORs, with 1 OR foreach comparison pair. The MOR is the median of this distribution of pairwise ORs. That is,the MOR expresses how much (in median) the individual probability of obtaining aremission would increase if a patient were evaluated by another examiner with a higherproportion of remissions, assuming the patients had the same covariates. If the MOR is 1,then there are no differences in remission prevalence between examiners. If there areconsiderable examiner differences, then the MOR is large. The measure is directlycomparable to fixed-effects ORs, which makes quantification of examiner variance easier to



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appreciate in terms of the familiar ORs (22). Roughly, given a remission proportion of 8%,MORs of 1.5 and 2.0 translate to probabilities of remission of about 12% and 16%. MORanalyses have not been used previously to assess groups of physicians.

We calculated Harrell's c to determine the predictor strength and discriminatory ability ofindividual and combined predictors for each set of criteria (Appendix I). Harrell's c has theinterpretation of a receiver operating characteristic (ROC) area under the curve (AUC).

Statistical significance was set at p <0.05. All analyses were performed with Stata 11.1(College Station, TX).

ResultsClinical characteristics

There were important and statistically significant differences between the ARCK and VARAgroups (Table 1). The VARA group consisted of a much higher proportion of men (90.9%vs. 25.8%) was older (65.3 vs. 59.3 years), had longer duration of RA (13.2 vs. 10.0 years),higher ESR values, were more often rheumatoid factor positive, and were more often treatedwith prednisone (42.1 vs. 26.0%). In addition, they had higher and swollen and tender jointcounts. By contrast, clinical activity measures including HAQ-II/MDHAQ, pain, PtGlobal,PhGlobal, and PAS were higher in the ARCK group. Combined tender and swollen jointsand combined activity measures were significantly different between groups.

Remission probabilities by ACR/EULAR and other criteriaThe probability of remission at a given clinic visit using AE 3 was 7.5% (6.4, 8.7) in theARCK group and 8.9% (7.9, 9.9) in VARA. With other AE remission criteria definitions,probabilities varied in both groups, from 5.0% to 6.9% in ARCK and 5.0% to 10.1% inVARA (Table 2). In the VARA data set we were also able to examine probabilities for AECRP 7.0% (5.9, 8.0) and AE SDAI 9.0% (7.7, 10.3), as these measures were recommendedby ACR/EULAR for use in clinical trials. In addition, the probability of remission generallyincreased over the course of the study. Adjusted for age, sex, and use of prednisone,methotrexate and biologics, the annual increase in the probability of remission was: AE 30.9% (0.2, 1.6), AE ESR 0.9% (0.02, 1.5), AE CDAI 1.3% (0.6, 1.9) for ARCK, and AE 30.7% (0.2, 1.1), AE ESR 0.1% (-0.1, 1.3), AE CRP 0.7% (0.2, 1.1), AE CDAI 1.1% (0.5,1.7), AE SDAI 0.6% (-.0.06, 1.2) for VARA. Methotrexate and biologics were notsignificantly associated with this increase in any model examined.

Regardless of which remission criteria set was selected, the cumulative or “ever” probabilityfor all criteria were considerably higher, ranging from 13.6% to 17.8% in ARCK and 16.5%to 24.4% in VARA. The ever probabilities were determined over a mean follow-up of 2.2years for ARCK, with a mean (IQR) duration of between visits of 2.7 (1.1 to 3.1) months,and 2.1 years follow-up for VARA with duration between visits of 3.8 (2.1 to 4.7) months.During this period of time the mean number of clinic visits was 10.6 for ARCK and 7.2 forVARA.

While the probability of ever meeting a remission criteria was greater than the probability ofmeeting criteria at a given clinic visit, the probability of any patient having 2 or more visitsin remission (not necessarily contiguous) was considerably smaller. The probability of suchevents ranged from 1.8% to 3.0% in ARCK and 1.5% to 4.6% in VARA. To the extent thatmeeting criteria at least twice defines a meaningful remission, these probabilities might beused to further clarify the probability and meaning of remission in RA.



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For those patients achieving remission, Table 3 provides data on the probability ofremaining in remission. At 12 months after the start of remission, 19.7% to 33.8% remainedin remission. At 2 years the probability of remaining in remission ranged from 6.0% to14.1%. By contrast, the probability of remaining in MDA was 42.9% at 12 months and24.8% at 24 months in ARCK and 43.9% at 12 months and 22.0% at 24 months in VARA.To put the remission data into perspective, less than 3% of all RA patients can be expectedto experience a remission lasting 2 years or more. Figure 1 demonstrates representativeKaplan-Meier survival curves for remaining in remission.

We also calculated non-ACR/EULAR probabilities that may be of interest (Table 2). Themajority of these definitions resulting in remissions substantially higher than those achievedunder the ACR/EULAR criteria. In particular DAS-28 remission was observed in 28.3% inARCK and 24.0% in VARA. PAS remission, depending only on patients self-report, was9.2% in ARCK and 9.1% in VARA. Finally, the minimal disease activity criterion wassatisfied by 22.9% in ARCK and 21.3% in VARA.

Agreement among criteriaSimilar cross-sectional probabilities do not necessarily mean that the same patients areidentified by the different criteria. To investigate agreement, we selected a randomobservation for each patient and then applied Kappa and Jaccard statistics. AE CRP and AESDAI had a Jaccard statistic of 0.66 (Table 4). The best Jaccard agreement for AE SDAIwas with AE CDAI (0.80), as might be suspected because of the similarity of these criteria.The best Jaccard agreement with AE CRP was with AE 3 (0.75). In ARCK data, whichlacked CRP, AE 3 and AE ESR had a Jaccard statistic of 0.80, a statistic of 0.64 for AE 4,and a value of 0.45 for AE CDAI.

The same pattern was noted for Kappa statistics, with generally moderate or substantialagreement beyond chance. The Kappa for AE SDAI and AE CRP was 0.77. The Kappa forAE 3 and SDAI was 0.75 and for AE 3 and AE CRP was 0.84. Because of the interest inpure patient based criteria, we evaluated statistics for the PAS-II/RAPID-3 and DAS-28.The Kappa and Jaccard coefficients between RAPID-3 remission and AE SDAI were 0.46,0.35; RAPID-3 remission and AE CRP were 0.40, 0.29, DAS-28 remission and AE SDAIwere 0.40, 0.32, and DAS-28 remission and AE CRP were 0.33, 0.25 in VARA.

We also examined the relation between MDA and the various remission criteria bydetermining the percent remission positive, given MDA is positive and the percent remissionpositive, given MDA is negative. For ARCK: AE 3 (32.2%/1.1%), AE ESR (26.7%/0.6%),CDAI (33.2%/3.0%); and for VARA: AE 3 (36.6%/3.9%), AE ESR (22.9%/2.0%), AE CRP(27.4%/3.2%), AE CDAI (42.3%/1.7%), and AE SDAI (37.0%/1.4%).

Importance of individual predictorsWe calculated Harrell's c to determine the predictor strength and discriminatory ability ofindividual and combined predictors for each set of criteria (Appendix I). PtGlobal was thevariable with the best discriminatory ability, with a value as high as 0.97. In criteria thatcontained PhGlobal, PhGlobal ranged from 0.90 to 0.93. Together these variables dominatedthe predictors in discriminatory ability. By contrast, tender and swollen joints had valuesbetween 0.74 and 0.77. Even when considered simultaneously, the tender and swollen jointsscores produced c scores that were less than the globals.

PtGlobal and remission criteria positive and negative statesAs PtGlobal was the strongest contributor to criteria positivity, we examined PtGlobalgraphically in patients who met joint and ESR criteria for AE 3 ESR remission in Figure 2a.



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Among patients otherwise remission criteria positive there was a wide distribution ofPtGlobal scores, including many within 1 point of the PtGlobal criterion for remission.Figure 2b demonstrates that among patients AE positive at the previous clinic visit, therewere many patients no longer AE positive on the basis of slight changes in PtGlobal (>1.0).

The effect of physician differences on criteria positivityWe addressed the issue of whether physicians differed in their examinations and ratings byusing multilevel analyses and calculating the median odds ratio (MOR) in Table 4. In thisanalysis patients are nested within physicians. The MOR represents the degree of variationbetween examiners. The highest MOR for criteria components was found for SJC (MOR 2.0– 2.7) and PhGlobal (2.4 - 2.7), indicating considerable physician heterogeneity. Slightlyless heterogeneity was seen for TJC (MOR 1.7 – 2.0). When applied to specific criteria,observed bias was noted generally in the VARA data set, but only for AE 4 and AE CDAI inthe ARCK data set. These data indicate physician differences influence remission diagnosis.

DiscussionThe ACR/EULAR recently established criteria for remission in RA that were “stringent butachievable and could be applied uniformly in clinical trials (6).” Among the 2 definitions putforth were 1) ≤1 for TJC, SJC, CRP (mg/dl), and PtGlobal; and 2) SDAI ≤3.3. The groupalso suggested possibilities for criteria that might be used in clinical practice. The basis ofthis suggestion required that a definition of “remission be developed for clinic based practicethat would not require an acute phase reactant, as long as it would capture remission asstringently as the measure employed for clinical trials.” Thus “… a Boolean measurecomprising tender joint count, swollen joint count and patient global assessment [couldprovide] similar statistical results as the same measures encompassing CRP and the CDAI,[but which] does not contain CRP…” The committee indicated that such definitions ofremission “may be used in clinical practice until better measures for that purpose becomeavailable.” In addition, the committee suggested clinical practice cut points for ESR (<20 formen and <30 for women) in the event laboratory tests were used in the clinical practicesetting.

The central difference between remission in clinical trials (and observational research) andremission in clinical practice is that trial data refer to a group of patients while clinicalpractice remission refers to an individual patient and an individual examiner. In a trial wheredifferent definitions provide similar proportions in remission, it does not matter substantiallywhich valid definition is used. In clinical practice, however, if different patients areidentified by different criteria, it may matter a great deal.

It is not surprising that remission probability differs by remission definition. However,differences seem generally small and in accord with probabilities from clinical trials noted inthe ACR/EULAR remission paper (6). Where our results differ, perhaps conceptually, fromthe ACR/EULAR results is in our observation of the tenuousness and sporadic nature ofremission. ACR/EULAR regarded the duration of remission as worthy of a separate studyand noted that they did not address it in the primary remission paper. We observed thatwithin 12 months, 65-80% of those who had experienced remission no longer met remissioncriteria; at 24 months 6%-14% still met criteria (Table 3 and Figure 1). If, as Table 2indicates, the probability of ever being in remission is 13.0%-24.4%, then the probability ofbeing in remission for as long as 2 years is between 1.0 and 3.0%. These results areremarkably similar to those of Wolfe and Hawley in 1985 (23) who noted an 18.1%remission proportion by application of the 1981 ACR remission criteria to 458 patients in aclinical practice (3). In addition, they found that “only 15% of remissions lasted longer than24 months.” Thus, only 3% of patients had a remission that lasted as long as 2 years.



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Another indication of the potential tenuous nature of remission comes from our observationthat 1.5% to 4.6% of RA patients had 2 or more physician visits in remission (Table 2,column 6) compared to 13.0%-24.4% of patients who ever experience a remission visit(Table 2, column 5).

We addressed several issues with respect to misclassification. First, we examined the degreeof agreement among the different criteria. In the current study, Jaccard's coefficient betweenAE SDAI and AE CRP, the two recommended clinical trial criteria, showed 66% agreementbetween criteria in the VARA data set. When the ACR/EULAR recommended clinicalcriteria (AE 3) was compared with AE SDAI and AE CRP, Jaccard coefficients of 0.63 and0.75 were noted. These levels of agreement, as well as the Kappa values in Table 4 aresufficient for clinical trials. However, at the level of the individual patient clinicallysignificant misclassification can occur, underscoring the difference between group criteriaand individual criteria with respect to levels of reliability.

Misclassification will also occur if physician examiners differ in their ratings. “Reliabilityconcerns the degree to which patients can be distinguished from each other, despitemeasurement error. High reliability is important for discriminative purposes if one wants todistinguish among patients, e.g., with more or less severe disease (as in diagnosticapplications) (24).” In general, reliability coefficients ≥ 0.9 are required to make decisionsabout individual patients. Values from 0.80 to 0.89 represent good reliability, suitable forresearch and use in groups of patients. However, there is substantial evidence that inter-raterreliability is poor with respect to the joint examination (25-28). Using the MOR inmultilevel analyses, we also found evidence of important physician heterogeneity in the jointexamination and in the PhGlobal rating. MOR scores of 2 (Table 4), for example, indicatethat the probability of remission can vary twofold according to physician examinerirrespective of the degree of disease activity. Such rater variability is not likely to be aproblem in clinical trials unless there is a systematic bias. At the clinical level, however,physician differences can lead to misclassification.

The sole patient measure used in the ACR/EULAR criteria sets is the PtGlobal. As shown inAppendix I, PtGlobal has the highest c statistic and is the best discriminatory variableamong the components of the various remission definitions. Lassere et al. has shown thatPtGlobal has poor test-retest reliability (Intraclass coefficient = 0.75) at the level of theindividual patient (26). This finding is consistent with the data of the current study. Figure2a suggests that when remission is first identified there are many patients with a PtGlobalscore close to the remission level that do not satisfy the remission definition. And when weexamined our results in the next visit for patients who had been in remission (Figure 2b), itcan be seen that many previously in remission patients were no longer in remission becauseof changes in PtGlobal. Thus, remission in this setting depends on PtGlobal, which may bereflect true sensitivity to changes in RA activity or represent reliability issues whereremission status changes while RA activity actually remains the same.

One approach that avoids physician bias is the use of the RAPID-3 (or PAS) (4). However,the components of these scales – pain, global and HAQ – also have poor reliability (26). Inaddition, we found unsatisfactory agreements with ACR/EULAR recommended measures,including Jaccard statistics of 0.35 (SDAI remission) and 0.29 (AE CRP).

We note a number of potential limitations to our study. Among the possible methodologicconcerns is that we chose to analyze individual physicians in the MOR VARA analyses. Wedid this to be consistent with analyses of ARCK patients. Another approach would havebeen to analyze VARA sites rather than physicians. Although we did not report these



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analyses in this paper, we found no substantial difference when we substituted sites forphysicians in sensitivity analyses.

We did not attempt to discern reasons for the differences in results between the VARA andthe ARCK data, as that was not the purpose of our study. Patients mix and socio-demographic characteristics might explain some of the differences. In multivariate analysesof remission criteria we noted that men were more likely to achieve remission than womenfor CDAI in ARCK, but not in any other ARCK or VARA criteria described in Table 2.

Although we attributed high MOR scores (MOR >1) to physician differences, it is possiblethat some physicians were assigned patients with greater disease activity. That does notappear to have been a matter of policy in ARCK or VARA, and we found no evidence tosupport that possibility.

In summary, the proportion of patients in remission at a given visit ranged from 5.0% to10.1%, and was 7.5% to 8.9% by the AE 3 criteria recommended by ACR/EULAR. Duringthe ∼2.2 years of follow-up 18.4% to 24.4% entered AE 3 remission. Prolonged remissionswere rare, with <3% of patients experiencing a remission lasting as long as 2 years.

AcknowledgmentsThe authors thank Grant Cannon, M.D. for his helpful and thoughtful comments.

Support: VARA has been supported by the VA HSR&D.

Dr. Mikuls is supported by a VA Merit grant.

Dr. Caplan is supported by a VA Career Development Award CDA 07-221.

Kaleb Michaud received partial funding from the Arthritis Foundation's New Investigator Award and NIH ARRAgrant #1RC1AR058601-01.

AppendixAppendix I. The association and discriminatory abilityof criteria variables for remission criteria

ARCK VARA

AE 3 ESR AUC (Harrell's c) AUC (Harrell's c)

SJC ≤1 0.77 (0.76, 0.79) 0.75 (0.74, 0.76)

TJC ≤1 0.75 (0.74, 0.77) 0.76 (0.74, 0.77)

PtGlobal ≤1 0.96 (0.96, 0.97) 0.94 (0.93, 0.95)

ESR <20,<30 0.67 (0.66, 0.69) 0.77 (0.75, 0.78)

SJC ≤1 &TJC ≤1 0.85 (0.84, 0.87) 0.83 (0.81, 0.84)

SJC ≤1 &TJC ≤1 + ESR 0.90 (0.89, 0.91) 0.92 (0.91, 0.93)

AE 3

SJC ≤1 0.77 (0.76, 0.79) 0.76 (0.75, 0.78)

TJC ≤1 0.76 (0.74, 0.77) 0.77 (0.76, 0.78)

PtGlobal ≤1 0.97 (0.97, 0.98) 0.96 (0.95, 0.97)

SJC ≤1 &TJC ≤1 0.86 (0.84, 0.87) 0.84 (0.83, 0.85)

SJC ≤1 &TJC ≤1 + ESR 0.80 (0.75, 0.84) 0.70 (0.66, 0.74)



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ARCK VARA

AE 3 ESR AUC (Harrell's c) AUC (Harrell's c)

AE 4

SJC ≤1 0.77 (0.75, 0.78) 0.76 (0.74, 0.77)

TJC ≤1 0.75 (0.74, 0.76) 0.76 (0.75, 0.78)

PtGlobal ≤1 0.96 (0.95, 0.97) 0.95 (0.94, 0.96)

PhGlobal ≤1 0.93 (0.92, 0.94) 0.93 (0.92, 0.94)

SJC ≤1 &TJC ≤1 0.85 (0.83, 0.86) 0.83 (0.82, 0.85)

SJC ≤1 &TJC + ESR 0.81 (0.76, 0.86) 0.71 (0.67, 0.76)

AE CDAI

SJC ≤1 0.77 (0.76, 0.79) 0.76 (0.74, 0.78)

TJC ≤1 0.76 (0.74, 0.77) 0.77 (0.75, 0.78)

PtGlobal ≤1 0.78 (0.73, 0.84) 0.85 (0.82, 0.89)

PhGlobal ≤1 0.90 (0.87, 0.93) 0.88 (0.85, 0.90)

SJC ≤1 &TJC ≤1 0.86 (0.84, 0.87) 0.83 (0.82, 0.85)

SJC ≤1 &TJC ≤1 + ESR 0.79 (0.75, 0.84) 0.72 (0.68, 0.76)

AE SDAI

SJC ≤1 0.74 (0.71, 0.76)

TJC ≤1 0.75 (0.73, 0.78)

PtGlobal ≤1 0.88 (0.84, 0.91)

PhGlobal ≤1 0.86 (0.82, 0.89)

SJC ≤1 &TJC ≤1 0.81 (0.78, 0.83)

CRP ≤1 0.61 (0.58, 0.64)

AE CRP

SJC ≤1 0.75 (0.73, 0.77)

TJC ≤1 0.76 (0.74, 0.77)

PtGlobal ≤1 0.95 (0.94, 0.96)

SJC ≤1 &TJC ≤1 0.83 (0.81, 0.84)

SJC ≤1 &TJC ≤1 + ESR 0.71 (0.65, 0.76)

CRP ≤1 0.65 (0.64, 0.67)

See methods for criteria definition.

References1. Pemberton R, Pierce EC. Clinical and statistical study of chronic arthritis based on 1100 cases.

AmJMedSci. 1927; 173:31–6.2. Thompson HE, wyatt BL, Hicks RA. Chronic atrophic arthritis. AnnInternMed. 1938; 11:1792–805.3. Pinals RS, Baum J, Bland J, Fosdick WM, Kaplan SB, Masi AT, et al. Preliminary criteria for

clinical remission in rheumatoid arthritis. Arthritis Rheum. 1981; 24:1308–15. [PubMed: 7306232]4. Sokka T, Hetland ML, Makinen H, Kautiainen H, Horslev-Petersen K, Luukkainen RK, et al.

Remission and rheumatoid arthritis: Data on patients receiving usual care in twenty-four countries.Arthritis Rheum. 2008 Sep; 58(9):2642–51. [PubMed: 18759292]

5. van Tuyl LH, Vlad SC, Felson DT, Wells G, Boers M. Defining remission in rheumatoid arthritis:results of an initial American College of Rheumatology/European League Against Rheumatismconsensus conference. Arthritis Rheum. 2009 May 15; 61(5):704–10. [PubMed: 19405006]



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6. Felson D, Smolen J, Wells G, Zhang B, van Tuyl LH, Funovits J, et al. American College ofRheumatology/European League against Rheumatism Preliminary Definition of Remission inRheumatoid Arthritis for Clinical Trials. Arthritis & Rheumatism. 2011 In press.

7. Aletaha D, Smolen JS. The Simplified Disease Activity Index (SDAI) and Clinical Disease ActivityIndex (CDAI) to monitor patients in standard clinical care. BestPractResClinRheumatol. 2007;21(4):663–75.

8. van der Heijde DMFM, van 't Hof M, van Riel PLCM, Theunisse LAM, Lubberts EW, vanLeeuwen MA, et al. Judging disease activity in clinical practice in rheumatoid arthritis: first step inthe development of a disease activity score. Ann Rheum Dis. 1990; 49:916–20. [PubMed: 2256738]

9. Shaver TS, Anderson JD, Weidensaul DN, Shahouri SS, Busch RE, Mikuls TR, et al. The problemof rheumatoid arthritis disease activity and remission in clinical practice. J Rheumatol. 2008; 35(6):1015–22. [PubMed: 18412311]

10. Wolfe F, Michaud K, Pincus T. Development and validation of the health assessment questionnaireII: a revised version of the health assessment questionnaire. Arthritis Rheum. 2004; 50(10):3296–305. [PubMed: 15476213]

11. Mikuls TR, Kazi S, Cipher D, Hooker R, Kerr GS, Richards JS, et al. The association of race andethnicity with disease expression in male US veterans with rheumatoid arthritis. J Rheumatol.2007; 34(7):1480–4. [PubMed: 17552044]

12. Michaud K, Mikuls T, Call S, Reimold A, Hooker R, Kerr G, et al. Poor to modest agreementbetween rheumatoid arthritis response measures in clinical practice. Clinical and experimentalrheumatology. 27(4):633. [PubMed: 19772796]

13. Pincus T, Swearingen C, Wolfe F. Toward a multidimensional Health Assessment Questionnaire(MDHAQ): assessment of advanced activities of daily living and psychological status in thepatient-friendly health assessment questionnaire format. Arthritis Rheum. 1999 Oct; 42(10):2220–30. [PubMed: 10524697]

14. Wolfe F, Michaud K, Pincus T. A Composite Disease Activity Scale for Clinical Practice,Observational Studies and Clinical Trials: The Patient Activity Scale (PAS/PAS-II). JRheumatol.2005; 32(12):2410–5. [PubMed: 16331773]

15. Pincus T, Bergman MJ, Yazici Y, Hines P, Raghupathi K, Maclean R. An index of only patient-reported outcome measures, routine assessment of patient index data 3 (RAPID3), in twoabatacept clinical trials: similar results to disease activity score (DAS28) and other RAPID indicesthat include physician-reported measures. Rheumatology (Oxford). 2008; 47(3):345–9. [PubMed:18238788]

16. Rodriguez G, Elo I. Intra-class correlation in random-effects models for binary data. Stata Journal.2003; 3(1):32–46.

17. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. Journal of theAmerican Statistical Association. 1958; 53:457–81.

18. Jaccard P. …comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletindel la SociÈtÈ Vaudoise des Sciences Naturelles. 1901; 37:547–79.

19. Landis J, Koch G. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159. [PubMed: 843571]

20. Larsen K, Merlo J. Appropriate assessment of neighborhood effects on individual health:integrating random and fixed effects in multilevel logistic regression. Am J Epidemiol. 2005 Jan 1;161(1):81–8. [PubMed: 15615918]

21. Merlo J, Chaix B, Ohlsson H, Beckman A, Johnell K, Hjerpe P, et al. A brief conceptual tutorial ofmultilevel analysis in social epidemiology: using measures of clustering in multilevel logisticregression to investigate contextual phenomena. J Epidemiol Community Health. 2006 Apr; 60(4):290–7. [PubMed: 16537344]

22. Due P, Hansen E, Merlo J, Andersen A, Holstein B. Is victimization from bullying associated withmedicine use among adolescents? A nationally representative cross-sectional survey in Denmark.Pediatrics. 2007; 120(1):110. [PubMed: 17606568]

23. Wolfe F, Hawley DJ. Remission in rheumatoid arthritis. JRheumatol. 1985; 12:245–52. [PubMed:4032401]



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24. Terwee C, Bot S, de Boer M, van der Windt D, Knol D, Dekker J, et al. Quality criteria wereproposed for measurement properties of health status questionnaires. Journal of ClinicalEpidemiology. 2007; 60(1):34–42. [PubMed: 17161752]

25. Pincus T. Limitations of a quantitative swollen and tender joint count to assess and monitorpatients with rheumatoid arthritis. Bulletin of the NYU hospital for joint diseases. 2008; 66(3):216.[PubMed: 18937635]

26. Lassere MN, van der HD, Johnson KR, Boers M, Edmonds J. Reliability of measures of diseaseactivity and disease damage in rheumatoid arthritis: implications for smallest detectable difference,minimal clinically important difference, and analysis of treatment effects in randomized controlledtrials. JRheumatol. 2001; 28(4):892–903. [PubMed: 11327273]

27. Klinkhoff AV, Bellamy N, Bombardier C, Carette S, Chalmers A, Esdaile JM, et al. An experimentin reducing interobserver variability of the examination for joint tenderness. JRheumatol. 1988;15:492–4. [PubMed: 3379626]

28. Scott DL, Houssien DA. Joint assessment in rheumatoid arthritis. BritJ Rheumatol. 1996; 35:14–8.-32676. [PubMed: 8810685]



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Figure 1.Representative examples of lack of durability of remission. Y axis represents the probabilityof remaining in remission. See methods for definition of AE 3 and SDAI remission.



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Figure 2.Figure 2a (above). Distribution of patient global scores for those who meet tender andswollen joint criteria and ESR criteria for remission at a random clinic visit. Patientsmeeting remission criteria have global scores ≤1 (horizontal line). Small changes in patientglobal scores would result in many patients meeting remission criteria. ARCK patients areon left, VARA on right. Horizontal line at patient global = 2 is added to enhance viewing.Figure 2b (below). Patients who initially met ACR/EULAR criteria assessed at a follow-upclinic visit. All patents meet tender and swollen joint criteria for remission, but those withglobal score above 1 or ESR ≥20 (men) or ≥30 (women) no longer satisfy ACR/EULARcriteria. For most patients not meeting ACR/EULAR criteria the difference betweenremission criteria positive and criteria negative patients is small. ARCK patients are on left,VARA on right. Horizontal line at patient global = 2 is added to enhance viewing.



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Table 1

Characteristics of RA study patients with ACR/EULAR 3 + ESR data at a random observation by group.

ARCK Cohort VARA Cohort

Variable Mean (SD) Mean (SD)

Number of Patients 1,153 1,341

Age (years) 59.3 (13.8) 65.1 (11.3)

Sex (% male) 25.8 90.9

Disease duration (years) 10.0 (9.8) 13.2 (11.5)

Rheumatoid Factor (%+)* 79.0 84.4

HAQ-II / MDHAQ (0-3) 1.1 (0.7) 1.0 (0.6)

Pain (0-10) 4.8 (2.8) 4.3 (2.9)

Patient Global (0-10) 4.4 (2.7) 4.0 (2.5)

Physician Global (0-10) 3.6 (2.1) 3.3 (2.3)

Swollen joint count (0-28) 3.0 (3.0) 3.2 (4.7)

Tender joint count (0-28) 3.5 (5.2) 4.1 (6.2)

PAS-II / RAPID 3 4.2 (2.4) 3.9 (2.1)

ESR (mm/Hr) 22.4 (20.5) 26.4 (23.0)

ESR (Women) (mm/Hr) 23.0 (19.5) 30.2 (25.1)

ESR (Men) (mm/Hr) 19.9 (22.5) 26.1 (22.8)

CRP (units) 1.24 (1.97)

Methotrexate current use (%) 68.2 54.7

Prednisone current use (%) 26.0 42.1

Biologics current use (%) 37.0 33.7

*Ever Rheumatoid Factor positive.

Groups differ at p <0.001 for all variables and grouped joint and activity variables except for Biologics (P = 0.083).

HAQ-II = Health Assessment Questionnaire II; MDHAQ = Multidimensional Health Assessment Questionnaire; PAS-II = Patient Activity ScaleII; RAPID 3 = Routine Assessment of Patient Index Data 3; ESR = erythrocyte sedimentation rate; CRP = C-reactive protein


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Tabl

e 2

Prob

abili

ty o

f rem

issi

on in

rheu

mat

oid

arth

ritis

.

Prob

abili

ty o

f Rem

issi

on %

(95%

CI)

Prob

. 2nd

Rem

issi

on

AE

Cri

teri

aA

ll pa

tient

sW

omen

Men

All

Patie

nts E

ver*

Any

obs

erva

tion

All

patie

nts

A

RC

K

AE

3 ES

R6.

2 (5

.2, 7

.3)

5.7

(4.6

, 6.8

)7.

9 (5

.5, 1

0.3)

14.8

(12.

8, 1

6.9)

2.4

(1.4

, 3.9

)

AE

37.

5 (6

.4, 8

.7)

7.1

(5.8

, 8.3

)9.

1 (6

.5, 1

1.7)

18.0

(15.

8, 2

0.2)

3.0

(1.9

, 4.5

)

AE

45.

0 (4

.1, 5

.9)

4.5

(3.5

, 5.4

)6.

8 (4

.6, 9

.1)

13.0

(11.

1, 1

5.0)

1.8

(0.9

, 3.1

)

AE

CD

AI

6.9

(5.9

, 8.0

)6.

1 (5

.0, 7

.3)

9.8

(7.2

, 12.

4)17

.8 (1

5.6,

20.

0)2.

2 (1

.4, 3

.5)

V

AR

A

AE

3 ES

R5.

0 (4

.3, 5

.8)

6.4

(3.9

, 9.0

)4.

9 (4

.2, 5

.7)

16.5

(14.

6, 1

8.4)

1.5

(0.9

, 2.4

)

AE

3 C

RP

7.0

(5.9

, 8.0

)9.

1 (5

.3, 1

3.0)

6.8

(5.7

, 7.8

)20

.9 (1

8.5,

23.

3)2.

8 (1

.8, 4

.1)

AE

38.

9 (7

.9, 9

.9)

11.6

(7.9

, 15.

3)8.

7 (7

.7, 9

.8)

24.4

(22.

2, 2

6.6)

3.3

(2.4

, 4.4

)

AE

47.

2 (6

.2, 8

.2)

7.3

(4.0

, 10.

5)7.

2 (6

.2, 8

.3)

17.8

(15.

8, 1

9.9)

2.8

(1.8

, 4.2

)

AE

CD

AI

10.1

(8.9

, 11.

3)10

.3 (6

.4, 1

4.2)

10.0

(8.8

,11.

3)22

.5 (2

0.3,

24.

8)4.

6 (3

.3, 6

.2)

AE

SDA

I9.

0 (7

.7, 1

0.3)

9.1

(4.9

, 13.

4)9.

0 (7

.6, 1

0.3)

21.9

(19.

4, 2

4.5)

4.2

(2.8

, 5.9

)

Non

-AE

Crit

eria

A

RC

K

DA

S-28

28.3

(26.

3, 3

0.4)

24.9

(22.

7, 2

7.2)

39.4

(34.

9, 4

3.8)

48.1

(45.

2, 5

1.0)

PhG

loba

l 04.

7 (3

.9, 5

.6)

4.0

(3.1

, 4.8

)7.

8 (5

.5, 1

0.1)

13.8

(11.

8, 1

5.8)

PhG

loba

l ≤1

19.1

(15.

1, 1

7.7)

17.3

(15.

5, 1

9.1)

25.1

(21.

3, 2

9.0)

40.1

(37.

4, 4

2.8)

PAS ≤1

9.2

(7.7

, 10.

6)8.

5 (6

.8, 1

0.1)

11.5

(8.3

, 14.

6)17

.0 (1

4.9,

19.

2)

MD

A22

.9 (2

0.9,

24.

8)20

.7 (1

8.5,

22.

8)29

.9 (2

5.6,

34.

1)41

.9 (3

9.0,

44.

7)

V

AR

A

DA

S-28

24.0

(22.

4, 2

5.6)

19.4

(14.

8, 2

4.1)

24.4

(22.

7, 2

6.1)

48.4

(45.

8, 5

1.0)

PhG

loba

l 02.

1 (1

.6, 2

.6)

1.9

(0.5

, 3.3

)2.

2 (1

.6, 2

.7)

6.7

(5.4

, 8.1

)

PhG

loba

l ≤1

20.8

(19.

2, 2

2.4)

24.8

(18.

8, 3

0.7)

20.5

(18.

8, 2

2.1)

43.4

(40.

7, 4

6.1)

PAS ≤1

9.1

(8.0

, 10.

2)11

.7 (7

.7, 1

5.7)

8.9

(7.8

, 10.

0)21

.8 (1

9.7,

23.

9)

MD

A21

.3 (1

9.6,

23.

0)23

.2 (1

7.0,

29.

3)21

.1 (1

9.3

22.9

)39

.6 (3

7.0,

42.

2)

See

met

hods

for c

riter

ia d

efin

ition

. Rem

issi

on e

stim

ates

are

adj

uste

d fo

r age

, sex

and

dur

atio

n of

RA

.

* Cum

ulat

ive

rem

issi

on: t

he p

roba

bilit

y of

eve

r hav

ing

a re

mis

sion

dur

ing

a m

ean

of 2

.2 y

ears

(AR

CK

) and

2.1

yea

rs (V

AR

A) o

f fol

low

-up.


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Table 3Probability of remaining in remission at 3, 12 and 24 months

Probability of Remaining in Remission (95% CI)

ARCK At 3 months At 12 months At 24 months

AE 3 ESR 82.2 (74.7, 87.7) 33.8 (25.8, 42.0) 10.8 (5.8, 17.5)

AE 3 82.0 (75.3, 87.1) 33.2 (26.0, 46.6) 14.1 (9.0, 20.5)

AE 4 79.2 (72.6, 84.4) 22.0 (16.2, 28.5) 7.0 (3.8, 11.7)

AE CDAI 71.3 (65.5, 76.4) 21.8 (16.8, 27.1) 6.0 (3.3, 10.0)

MDA 83.8 (79.7, 87.2) 42.9 (37.7, 48.0) 24.8 (20.1, 29.8)

VARA At 3 months At 12 months At 24 months

AE 3 ESR 85.0 (79.3, 89.3) 19.7 (14.4, 25.7) 6.6 (3.5, 11.1)

AE 3 CRP 86.3 (80.9, 90.3) 24.7 (18.9, 30.9) 8.1 (4.6,12.9)

AE 3 85.5 (81.0, 89.0) 24.2 (19.3, 29.4) 8.3 (5.3, 12.3)

AE 4 90.7 (85.6, 94.0) 23.8 (17.8, 30.4) 9.6 (5.6, 14.9)

AE CDAI 89.0 (84.2, 92.4) 27.1 (21.3, 33.3) 13.5 (9.0, 18.8)

AE SDAI 89.6 (84.2, 93.3) 31.3 (24.3, 38.4) 13.3 (8.4, 19.4)

MDA 89.5 (86.1, 92.1) 43.9 (38.7, 49.0) 22.0 (17.5, 26.8)

See methods for criteria definition.


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Tabl

e 4

Agr

eem

ent a

nd d

isag

reem

ent i

n re

mis

sion

pro

porti

ons a

t a ra

ndom

obs

erva

tion

in 1

153

(AR

CK

) and

135

8 (V

AR

A) R

A p

atie

nts.

Com

pari

son

AE

3 E

SRA

E C

RP

AE

3A

E 4

AE

CD

AI

AE

3 E

SRA

E 3

CR

PA

E 3

AE

4A

E C

DA

IM

edia

n O

dds R

atio

Kap

paJa

ccar

d

AR

CK

Crit

eria

AE

3 +

ESR

1.00

1.00

1.0

AE

30.

881.

000.

801.

001.

1

AE

40.

720.

761.

000.

580.

641.

002.

0

AE

CD

AI

0.55

0.55

0.66

0.40

0.45

0.52

1.1

Crit

erio

n*

SJ

C2.

0

TJ

C1.

7

Pt

Glo

bal

1.0

Ph

Glo

bal

2.7

ES

R1.

4

VA

RA

Crit

eria

AE

3 ES

R1.

001.

001.

8

AE

3 C

RP

0.64

1.00

0.50

1.00

2.1

AE

30.

700.

841.

000.

560.

751.

002.

2

AE

40.

670.

740.

861.

000.

530.

610.

781.

002.

2

AE

CD

AI

0.57

0.71

0.79

0.76

1.00

0.43

0.59

0.68

0.64

1.00

2.0

AE

SDA

I0.

580.

770.

750.

710.

870.

440.

660.

630.

590.

801.

4

Crit

erio

n*

SJ

C2.

7

TJ

C2.

0

Pt

Glo

bal

1.5

Ph

Glo

bal

2.4

ES

R1.

9

* Crit

erio

n: In

divi

dual

com

pone

nts o

f crit

eria

, SJC

≤1,

TJC

≤1,

Pt G

loba

l ≤1,

PhG

loba

l ≤1,

ESR

<20

(mal

e) <

30 (f

emal

e).


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Shahouri et al. Page 19Se

e m

etho

ds fo

r crit

eria

def

initi

on.

The

Jacc

ard

coef

ficie

nt is

def

ined

as a

nd re

pres

ents

the

prop

ortio

n of

agr

eem

ent i

n ca

ses e

xclu

ding

thos

e in

stan

ces o

f agr

eem

ent i

n ab

senc

e.

CD

AI:

Clin

ical

Dis

ease

Act

ivity

Inde

x; D

AS-

28: D

isea

se A

ctiv

ity S

cale

28;

PA

S: P

atie

nt A

ctiv

ity S

cale


Documents

Remission of rheumatoid arthritis in clinical practice: Application of the American College of Rheumatology/European League Against Rheumatism 2011 remission criteria