The validity of a rheumatoid arthritis medical records-based index of severity compared with the DAS28

Inferring disease severity in rheumatoid arthritis using predictive modeling in administrative claims databases

Background

Confounding by disease severity is an issue in pharmacoepidemiology studies of rheumatoid arthritis (RA), due to channeling of sicker patients to certain therapies. To address the issue of limited clinical data for confounder adjustment, a patient-level prediction model to differentiate between patients prescribed and not prescribed advanced therapies was developed as a surrogate for disease severity, using all available data from a US claims database.

Methods

Data from adult RA patients were used to build regularized logistic regression models to predict current and future disease severity using a biologic or tofacitinib prescription claim as a surrogate for moderate-to-severe disease. Model discrimination was assessed using the area under the receiver (AUC) operating characteristic curve, tested and trained in Optum Clinformatics^® Extended DataMart (Optum) and additionally validated in three external IBM MarketScan^® databases. The model was further validated in the Optum database across a range of patient cohorts.

Results

In the Optum database (n = 68,608), the AUC for discriminating RA patients with a prescription claim for a biologic or tofacitinib versus those without in the 90 days following index diagnosis was 0.80. Model AUCs were 0.77 in IBM CCAE (n = 75,579) and IBM MDCD (n = 7,537) and 0.75 in IBM MDCR (n = 36,090). There was little change in the prediction model assessing discrimination 730 days following index diagnosis (prediction model AUC in Optum was 0.79).

Conclusions

A prediction model demonstrated good discrimination across multiple claims databases to identify RA patients with a prescription claim for advanced therapies during different time-at-risk periods as proxy for current and future moderate-to-severe disease. This work provides a robust model-derived risk score that can be used as a potential covariate and proxy measure to adjust for confounding by severity in multivariable models in the RA population. An R package to develop the prediction model and risk score are available in an open source platform for researchers.

Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms

Objective

The accurate and efficient collection and documentation of disease activity measures (DAMs) is critical to improve clinical care and outcomes research in rheumatoid arthritis (RA). This study evaluated the performance of an automated process to extract DAMs from medical notes in the electronic health record (EHR).

Methods

An automated text processing system was developed to extract the Disease Activity Score for 28 joints (DAS28) and its clinical and laboratory elements from the Veterans Affairs EHR for patients enrolled in the Veterans Affairs Rheumatoid Arthritis (VARA) registry. After automated text processing derivation, data accuracy was assessed by comparing the automated text processing system and manual extraction with gold standard chart review in a separate validation phase.

Results

In the validation phase, 1569 notes from 596 patients at 3 sites were evaluated, with 75 (6%) notes detected only by automated text processing, 85 (5%) detected only by manual extraction, and 1408 (90%) detected by both methods. The accuracy of automated text processing ranged from 90.7% to 96.7% and the accuracy of manual extraction ranged from 91.3% to 95.0% for the different clinical and laboratory elements. The accuracy of the two methods to calculate the DAS28 was 78.1% for automated text processing and 78.3% for manual extraction.

Conclusion

The automated text processing approach is highly efficient and performed as well as the manual extraction approach. This advance has the potential for significant improvements in the collection, documentation, and extraction of these data to support clinical practice and outcomes research relevant to RA as well as the potential for broader application to other health conditions.

A paradigm shift in studies based on rheumatoid arthritis clinical registries

Clinical research is the study of aspects of patient health or illness that are closely related to clinical practice. In the late 20th and early 21th century, outcomes for patients with rheumatoid arthritis (RA) improved dramatically due to breakthroughs in new drugs. Patient-reported outcome measures now play a significant role in the drug development process as study endpoints in clinical trials of new therapies, and this has led to increased interest in the patient's perspective, drug safety and treatment outcomes in clinical practice. In accordance with these needs, many prospective cohorts for RA patients and registries of biologic disease modifying anti-rheumatic drugs have been actively conducted in the United States and European and Asian countries. A gradual shift is taking place in the major outcomes of clinical research using these prospective cohorts and registries. This article will introduce representative registries for RA in each country set up in the early 2000s and will discuss future perspectives in clinical research on RA patients using such clinical registries.

Improved flare and remission pattern in rheumatoid arthritis over recent decades: a population-based study

Objective

To assess trends in the occurrence of flares and remission in RA over recent decades.

Methods

A retrospective medical records review of each clinical visit was performed in a population-based cohort of patients with RA (age ⩾30 years; 1987 ACR criteria met in 1988-2007) to estimate flare and remission status. RA flare was defined as any worsening of RA activity leading to an initiation, change or increase of therapy (OMERACT 9). The primary definition for remission required the absence of RA disease activity (i.e. tender joint count 0, swollen joint count 0 and ESR ⩽10 mm/h) (OMERACT 7). All subjects were followed until death, migration or 1 July 2012.

Results

The study included 650 RA patients (mean age 55.8 years; 69% female) with a mean follow up of 10.3 years. Patients were flaring at 2887 (17%) visits. There was a significant decline in the RA flare rate across disease duration (P < 0.001), predominantly in the first 5 years after diagnosis of RA. Patients diagnosed with RA in more recent years experienced fewer flares during first few years of RA (P < 0.001). There was no difference between the sexes in trends of flare rates over time (P = 0.42) Current smokers had higher flare rates than non-smokers (P = 0.047) and former smokers were not different from non-smokers (P = 0.87).

Conclusion

Patients diagnosed in more recent years have lower flare rates than those diagnosed in prior decades. Flare rates declined fastest in the first 5 years of disease and tended to be stable thereafter. Current smoking was associated with an adverse flare profile.

Severity indices in rheumatoid arthritis: A systematic review

OBJECTIVE

To identify tools designed to evaluate the severity of patients with rheumatoid arthritis (RA) in order to use them in the investigation of prognostic markers in early arthritis.

METHODS

We conducted a systematic review of studies that developed/validated an index for RA disease severity. They were analyzed using the COSMIN checklist to assess their methodological quality. In addition, all the variables included were evaluated for their clarity of definition, feasibility and probability of being present in each outcome during the first 2 years of the disease course. To estimate redundancy, variables were grouped by domains.

RESULTS

After reviewing 3,519 articles, 3 studies were included. The first study, the PAS, assessed whether current and lifetime treatment with disease-modifying antirheumatic drugs and/or biologics accurately predicted RA severity, as measured by the patient-reported PAS. Treatment variables did not fully distinguish patients in the highest and lowest quartiles of PAS scores. Another severity index, the Claims-Based Index for RA Severity (CIRAS), included the variables age, sex, Felty's syndrome, number of rehabilitation and rheumatology visits, test for inflammatory markers, number of chemistry panels/platelet counts ordered and rheumatoid factor test. The correlation was low (r=0.56) with an index previously validated by the same research group, the RA medical records-based index of severity (RARBIS), with Disease Activity Score-C-reactive protein (DAS28-PCR) (r=0.07) and Multidimensional Health Assessment Questionnaire (MD-HAQ) (r=0.008). Finally, the RARBIS, used to validate the CIRAS, was devised as an RA severity index based on medical records. It includes as domains surgery, radiology, extra-articular manifestations, clinical and laboratory variables, previously chosen by an expert panel. RARBIS had a weak correlation with treatment intensity (r=0.35) and with DAS28 (r=0.41).

CONCLUSION

There is no index to assess the severity of RA based on the course of the first 2 years of follow-up that is adapted to the current strategy of therapeutic management of this disease. Therefore, we believe it is reasonable to develop a new ad hoc severity index for patients with early arthritis.

Models solely using claims-based administrative data are poor predictors of rheumatoid arthritis disease activity

BACKGROUND

This study developed and validated a claims-based statistical model to predict rheumatoid arthritis (RA) disease activity, measured by the 28-joint count Disease Activity Score (DAS28).

METHOD

Veterans enrolled in the Veterans Affairs Rheumatoid Arthritis (VARA) registry with one year of data available for review before being assessed by the DAS28, were studied. Three models were developed based on initial selection of variables for analyses. The first model was based on clinically defined variables, the second leveraged grouping systems for high dimensional data and the third approach prescreened all possible predictors based on a significant bivariate association with the DAS28. The least absolute shrinkage and selection operator (LASSO) with fivefold cross-validation was used for variable selection and model development. Models were also compared for patients with <5 years to those ≥5 years of RA disease. Classification accuracy was examined for remission (DAS28 < 2.6) and for low (2.6-3.1), moderate (3.2-5.1) and high (>5.1) activity.

RESULTS

There were 1582 Veterans who fulfilled inclusion criteria. The adjusted r-square for the three models tested ranged from 0.221 to 0.223. The models performed slightly better for patients with <5 years of RA disease than for patients with ≥5 years of RA disease. Correct classification of DAS28 categories ranged from 39.9% to 40.5% for the three models.

CONCLUSION

The multiple models tested showed weak overall predictive accuracy in measuring DAS28. The models performed poorly at predicting patients with remission and high disease activity. Future research should investigate components of disease activity measures directly from medical records and incorporate additional laboratory and other clinical data.

An external validation study reporting poor correlation between the claims-based index for rheumatoid arthritis severity and the disease activity score

INTRODUCTION

We conducted an external validation study to examine the correlation of a previously published claims-based index for rheumatoid arthritis severity (CIRAS) with disease activity score in 28 joints calculated by using C-reactive protein (DAS28-CRP) and the multi-dimensional health assessment questionnaire (MD-HAQ) physical function score.

METHODS

Patients enrolled in the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) and Medicare were identified and their data from these two sources were linked. For each patient, DAS28-CRP measurement and MD-HAQ physical function scores were extracted from BRASS, and CIRAS was calculated from Medicare claims for the period of 365 days prior to the DAS28-CRP measurement. Pearson correlation coefficient between CIRAS and DAS28-CRP as well as MD-HAQ physical function scores were calculated. Furthermore, we considered several additional pharmacy and medical claims-derived variables as predictors for DAS28-CRP in a multivariable linear regression model in order to assess improvement in the performance of the original CIRAS algorithm.

RESULTS

In total, 315 patients with enrollment in both BRASS and Medicare were included in this study. The majority (81%) of the cohort was female, and the mean age was 70 years. The correlation between CIRAS and DAS28-CRP was low (Pearson correlation coefficient = 0.07, P = 0.24). The correlation between the calculated CIRAS and MD-HAQ physical function scores was also found to be low (Pearson correlation coefficient = 0.08, P = 0.17). The linear regression model containing additional claims-derived variables yielded model R(2) of 0.23, suggesting limited ability of this model to explain variation in DAS28-CRP.

CONCLUSIONS

In a cohort of Medicare-enrolled patients with established RA, CIRAS showed low correlation with DAS28-CRP as well as MD-HAQ physical function scores. Claims-based algorithms for disease activity should be rigorously tested in distinct populations in order to establish their generalizability before widespread adoption.

The role of rheumatoid arthritis (RA) flare and cumulative burden of RA severity in the risk of cardiovascular disease

OBJECTIVE

To examine the role of rheumatoid arthritis (RA) flare, remission and RA severity burden in cardiovascular disease (CVD).

METHODS

In a population-based cohort of patients with RA without CVD (age ≥30 years; 1987 American College of Rheumatology criteria met in 1988-2007), we performed medical record review at each clinical visit to estimate flare/remission status. The previously validated RA medical Records-Based Index of Severity (RARBIS) and Claims-Based Index of RA Severity (CIRAS) were applied. Age- and sex-matched non-RA subjects without CVD comprised the comparison cohort. Cox models were used to assess the association of RA activity/severity with CVD, adjusting for age, sex, calendar year of RA, CVD risk factors and antirheumatic medications.

RESULTS

Study included 525 patients with RA and 524 non-RA subjects. There was a significant increase in CVD risk in RA per time spent in each acute flare versus remission (HR 1.07 per 6-week flare, 95% CI 1.01 to 1.15). The CVD risk for patients with RA in remission was similar to the non-RA subjects (HR 0.90, 95% CI 0.51 to 1.59). Increased cumulative moving average of daily RARBIS (HR 1.16, 95% CI 1.03 to 1.30) and CIRAS (HR 1.38, 95% CI 1.12 to 1.70) was associated with CVD. CVD risk was higher in patients with RA who spent more time in medium (HR 1.08, 95% CI 0.98 to 1.20) and high CIRAS tertiles (HR 1.18, 95% CI 1.06 to 1.31) versus lower tertile.

CONCLUSIONS

Our findings show substantial detrimental role of exposure to RA flare and cumulative burden of RA disease severity in CVD risk in RA, suggesting important cardiovascular benefits associated with tight inflammation control and improved flare management in patients with RA.

Derivation of severity index for rheumatoid arthritis and its association with healthcare outcomes

OBJECTIVES

To develop a claims-based severity index for rheumatoid arthritis (RA) using the Veterans Health Administration (VHA) database.

METHODS

Adult patients with at least two RA diagnoses 2 months apart were identified between 10/1/2008-09/30/2009. Patients were required to have at least 12 months continuous health plan enrollment before and after the index date (first RA diagnosis date) for an overall study period of 10/1/2007-09/30/2010. A severity index for rheumatoid arthritis (SIFRA, a proprietary algorithm of SIMR, Inc. [STATinMED Research]) was developed by calculating a weighted sum of 34 RA-related indicators assessed by an expert Delphi panel of six rheumatologists, including laboratory, clinical, and functional status, extra-articular manifestations, surgical history, and medications, during a 1-year pre-index period. Separate SIFRA versions were derived for patients with and without laboratory information. Correlations between SIFRA and previously validated claims-based indexes for RA severity (CIRAS), and other traditional comorbidity indexes were calculated during the pre-index period. The relationship between SIFRA and follow-up healthcare outcomes was also examined using histograms.

RESULTS

The Spearman's rank correlations between SIFRA and CIRAS were 0.525 for SIFRA without and 0.539 with laboratory data. The correlations between SIFRA and the Charlson Comorbidity Index (CCI) (0.1503 without, 0.1135 with laboratory data), Elixhauser Index (ELIX) (0.105 without, 0.079 with laboratory data), and Chronic Disease Score (CDS) (0.255 without, 0.239 with laboratory data) were low. Histograms showed that patients in the upper tercile of SIFRA incurred $9123 more all-cause and $1326 more RA-related healthcare costs during the 1-year post-index period than patients in the lower tercile. Using SIFRA in combination with CCI, CDS, or ELIX significantly increased the percentage of variation explained in outcomes measures.

LIMITATIONS

Patients in the VHA database may not represent typical RA patients since the database generally contains older, economically disadvantaged men with a high disease burden. Validity of the score is indirectly based on disease activity score 28 (DAS28), which measures disease activity rather than severity.

CONCLUSIONS

SIFRA was found to have moderate correlations with the previously validated CIRAS score, and demonstrated evidence of being a significant determinant of total and RA-related healthcare costs for RA patients. This study suggests that SIFRA could be an important methodological tool to control for severity in RA-related outcomes research. The algorithm can be applied to any claims dataset.

Rheumatoid Arthritis Disease Severity Indices in Administrative Databases: A Systematic Review

Objective.

We aimed to systematically review rheumatoid arthritis (RA) disease severity indices for use in administrative healthcare databases. We also provide an overview of alternative methods to control for RA disease severity in administrative database research.

Methods.

We conducted a systematic review of studies that developed/validated an index for RA disease severity using variables in administrative databases, and compared the convergent validity/reliability of the index with a standard measure of RA severity.

Results.

After reviewing 539 articles, 2 studies were included. The claims-based index for RA severity (CIRAS) was developed in one study. Components of the CIRAS included tests for inflammatory markers, number of chemistry panels/platelet counts ordered, rheumatoid factor test, number of rehabilitation and rheumatology visits, and Felty’s syndrome. The CIRAS correlated moderately well with a previously validated RA medical records-based index of severity. The second study assessed whether current and lifetime treatment with disease-modifying antirheumatic drugs and/or biologics accurately predicted RA severity, as measured by the patient-reported Patient Activity Scale (PAS). Treatment variables did not fully distinguish patients in the highest and lowest quartiles of PAS scores (67.2% correctly classified).

Conclusion.

Two claims-based indices of RA severity were identified but have some limitations for routine use. A concerted effort from experts in the field is needed to define, develop, and validate a widely applicable measure of RA disease severity for administrative database research.

Using genetic and clinical data to understand response to disease-modifying anti-rheumatic drug therapy: data from the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study

The objective of this review is to report on the progress of the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) Registry data collection and summarize previous research in understanding therapeutic response to DMARDs using clinical and genetic data. The BRASS Registry, established in 2003, is a large, single-centre, prospective and observational cohort of 1100 RA patients. Patients with either new-onset or established RA disease are recruited from the practices of rheumatologists. Annual visits collect information on demographics, 28-joint DAS-CRP3 (DAS-28-CRP3), medication use, comorbidities and functional status (Modified Health Assessment Questionnaire, Short Form Health Survey 12). Two published studies have utilized BRASS to examine genetic predictors of treatment response. In a cross-sectional study, examining the association between candidate single nucleotide polymorphisms (SNPs) and disease activity in a subset of 120 RA patients on MTX monotherapy, the minor allele of ATIC rs4673993 was associated with low disease activity (P=0.01, DAS-28-CRP3≤3.2). In an international collaboration, 55 BRASS patients receiving anti-TNF therapy were genotyped for 31 SNPs associated with the risk of RA. With our collaborators, we discovered an SNP at the protein tyrosine phosphatase, receptor type, C (PTPRC) gene locus that was associated with EULAR 'good response'. With accurate data collection and the capacity to run genome-wide association studies and SNP analyses, the BRASS Registry has the ability to determine the contribution of genetic variants to disease onset and to assess their usefulness as biomarkers for treatment response and drug toxicity.

Evaluation of Selected Rheumatoid Arthritis Activity Scores for Office-based Assessment

Objective.

Patient-reported measures can quickly provide assessments of rheumatoid arthritis (RA) disease activity in the office setting and do not require a laboratory test or physician examination. The goal of our study was to establish the validity of patient-reported indices compared to the C-reactive protein-based Disease Activity Score (DAS28-CRP4).

Methods.

Baseline and 1-year followup DAS28-CRP4 data were obtained from 740 RA subjects and were compared to indices (MDHAQ, CDAI, RAPID, RADAI, GAS) according to cyclic citrullinated peptide (CCP) status and change at 1 year. Pairwise correlations were calculated for each index.

Results.

Among 740 subjects, mean age 57 years, disease duration 14 years, the CDAI (r = 0.84, Δ r = 0.80) and RAPID (r = 0.71, Δ r = 0.70) had the highest correlation with the DAS28-CRP4 scores at baseline and 1 year. These correlations were not influenced by CCP status, disease-modifying antirheumatic drug use, biologic use, or by disease duration.

Conclusion.

In RA, the CDAI and RAPID correlated well with the DAS28-CRP4. They may both be practical and informative in the care of patients in the office setting.

Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factor alpha therapy

OBJECTIVE

Anti-tumor necrosis factor alpha (anti-TNF) therapy is a mainstay of treatment in rheumatoid arthritis (RA). The aim of the present study was to test established RA genetic risk factors to determine whether the same alleles also influence the response to anti-TNF therapy.

METHODS

A total of 1,283 RA patients receiving etanercept, infliximab, or adalimumab therapy were studied from among an international collaborative consortium of 9 different RA cohorts. The primary end point compared RA patients with a good treatment response according to the European League Against Rheumatism (EULAR) response criteria (n = 505) with RA patients considered to be nonresponders (n = 316). The secondary end point was the change from baseline in the level of disease activity according to the Disease Activity Score in 28 joints (triangle upDAS28). Clinical factors such as age, sex, and concomitant medications were tested as possible correlates of treatment response. Thirty-one single-nucleotide polymorphisms (SNPs) associated with the risk of RA were genotyped and tested for any association with treatment response, using univariate and multivariate logistic regression models.

RESULTS

Of the 31 RA-associated risk alleles, a SNP at the PTPRC (also known as CD45) gene locus (rs10919563) was associated with the primary end point, a EULAR good response versus no response (odds ratio [OR] 0.55, P = 0.0001 in the multivariate model). Similar results were obtained using the secondary end point, the triangle upDAS28 (P = 0.0002). There was suggestive evidence of a stronger association in autoantibody-positive patients with RA (OR 0.55, 95% confidence interval [95% CI] 0.39-0.76) as compared with autoantibody-negative patients (OR 0.90, 95% CI 0.41-1.99).

CONCLUSION

Statistically significant associations were observed between the response to anti-TNF therapy and an RA risk allele at the PTPRC gene locus. Additional studies will be required to replicate this finding in additional patient collections.

Genetic variants at CD28, PRDM1 and CD2/CD58 are associated with rheumatoid arthritis risk

To discover novel RA risk loci, we systematically examined 370 SNPs from 179 independent loci with p<0.001 in a published meta-analysis of RA GWAS of 3,393 cases and 12,462 controls¹. We used GRAIL², a computational method that applies statistical text mining to PubMed abstracts, to score these 179 loci for functional relationships to genes in 16 established RA disease loci^1,3-11. We identified 22 loci with a significant degree of functional connectivity. We genotyped 22 representative SNPs in an independent set of 7,957 cases and 11,958 matched controls. Three validate convincingly: CD2/CD58 (rs11586238, p=1×10⁻⁶ replication, p=1×10⁻⁹ overall), and CD28 (rs1980422, p=5×10⁻⁶ replication, p=1×10⁻⁹ overall), PRDM1 (rs548234, p=1×10⁻⁵ replication, p=2×10⁻⁸ overall). An additional four replicate (p<0.0023): TAGAP (rs394581, p=0.0002 replication, p=4×10⁻⁷ overall), PTPRC (rs10919563, p=0.0003 replication, p=7×10⁻⁷ overall), TRAF6/RAG1 (rs540386, p=0.0008 replication, p=4×10⁻⁶ overall), and FCGR2A (rs12746613, p=0.0022 replication, p=2×10⁻⁵ overall). Many of these loci are also associated to other immunologic diseases.

De novo copy number variants identify new genes and loci in isolated sporadic tetralogy of Fallot

Tetralogy of Fallot (TOF), the most common severe congenital heart malformation, occurs sporadically, without other anomaly, and from unknown cause in 70% of cases. A genome-wide survey of 114 TOF patients and their unaffected parents identified 11 de novo copy number variants (CNVs) that were absent or extremely rare (<0.1%) in 2,265 controls. A second, independent TOF cohort (n = 398) was then examined for additional CNVs at these loci. In 1% (5/512, p = 0.0002, OR = 22.3) of non-syndromic sporadic TOF cases we identified CNVs at chromosome 1q21.1. Recurrent CNVs were also identified at 3p25.1, 7p21.3 and 22q11.2. CNVs in a single TOF case occurred at six loci, two that encode known (NOTCH1, JAG1) disease genes. Our data predicts that at least 10% (4.5–15.5, 95% CI) of sporadic, non-syndromic TOF reflects de novo CNVs and implicates mutations within these loci as etiologic in other cases of TOF.

Development of a health care utilisation data-based index for rheumatoid arthritis severity: a preliminary study

Introduction

Health care utilisation ('claims') databases contain information about millions of patients and are an important source of information for a variety of study types. However, they typically do not contain information about disease severity. The goal of the present study was to develop a health care claims index for rheumatoid arthritis (RA) severity using a previously developed medical records-based index for RA severity (RA medical records-based index of severity [RARBIS]).

Methods

The study population consisted of 120 patients from the Veteran's Administration (VA) Health System. We previously demonstrated the construct validity of the RARBIS and established its convergent validity with the Disease Activity Score (DAS28). Potential claims-based indicators were entered into a linear regression model as independent variables and the RARBIS as the dependent variable. The claims-based index for RA severity (CIRAS) was created using the coefficients from models with the highest coefficient of determination (R²) values selected by automated modelling procedures. To compare our claims-based index with our medical records-based index, we examined the correlation between the CIRAS and the RARBIS using Spearman non-parametric tests.

Results

The forward selection models yielded the highest model R² for both the RARBIS with medications (R² = 0.31) and the RARBIS without medications (R² = 0.26). Components of the CIRAS included tests for inflammatory markers, number of chemistry panels and platelet counts ordered, rheumatoid factor, the number of rehabilitation and rheumatology visits, and Felty's syndrome diagnosis. The CIRAS demonstrated moderate correlations with the RARBIS with medication and the RARBIS without medication sub-scales.

Conclusion

We developed the CIRAS that showed moderate correlations with a previously validated records-based index of severity. The CIRAS may serve as a potentially important tool in adjusting for RA severity in pharmacoepidemiology studies of RA treatment and complications using health care utilisation data.

Two independent alleles at 6q23 associated with risk of rheumatoid arthritis

To identify susceptibility alleles associated with rheumatoid arthritis, we genotyped 397 individuals with rheumatoid arthritis for 116,204 SNPs and carried out an association analysis in comparison to publicly available genotype data for 1,211 related individuals from the Framingham Heart Study. After evaluating and adjusting for technical and population biases, we identified a SNP at 6q23 (rs10499194, approximately 150 kb from TNFAIP3 and OLIG3) that was reproducibly associated with rheumatoid arthritis both in the genome-wide association (GWA) scan and in 5,541 additional case-control samples (P = 10(-3), GWA scan; P < 10(-6), replication; P = 10(-9), combined). In a concurrent study, the Wellcome Trust Case Control Consortium (WTCCC) has reported strong association of rheumatoid arthritis susceptibility to a different SNP located 3.8 kb from rs10499194 (rs6920220; P = 5 x 10(-6) in WTCCC). We show that these two SNP associations are statistically independent, are each reproducible in the comparison of our data and WTCCC data, and define risk and protective haplotypes for rheumatoid arthritis at 6q23.

The validity of a rheumatoid arthritis medical records-based index of severity compared with the DAS28

Outcome measures play an extremely important role in clinical trials and observational research. Outcome measures for rheumatoid arthritis cover a whole array of domains, ranging from measures describing the inflammatory process to measures describing the ultimate consequences of long-term disease, such as joint damage, physical function and quality of life. There is a scientific need to be able to quantify what is called the 'severity of rheumatoid arthritis', so that patients with rheumatoid arthritis can be clustered according to their propensity to develop an unfavourable outcome. It is a challenge to find an appropriate measure for severity. One attempt has been the development of the Rheumatoid Arthritis Medical Record-Based Index of Severity. This commentary elaborates on how such a measure of severity should be validated to determine whether it is appropriate for practical use.