Rider, Lisa G.

Objective To describe the methodology used to develop new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIMs) and their major subgroups. Methods An international, multidisciplinary group of myositis experts produced a set of 93 potentially relevant variables to be tested for inclusion in the criteria. Rheumatology, dermatology, neurology and paediatric clinics worldwide collected data on 976 IIM cases (74% adults, 26% children) and 624 non-IIM comparator cases with mimicking conditions (82% adults, 18% children). The participating clinicians classified each case as IIM or non-IIM. Generally, the classification of any given patient was based on few variables, leaving remaining variables unmeasured. We investigated the strength of the association between all variables and between these and the disease status as determined by the physician. We considered three approaches: (1) a probability-score approach, (2) a sum-of-items approach criteria and (3) a classification-tree approach. Results The approaches yielded several candidate models that were scrutinised with respect to statistical performance and clinical relevance. The probability-score approach showed superior statistical performance and clinical practicability and was therefore preferred over the others. We developed a classification tree for subclassification of patients with IIM. A calculator for electronic devices, such as computers and smartphones, facilitates the use of the European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria. Conclusions The new EULAR/ACR classification criteria provide a patient’s probability of having IIM for use in clinical and research settings. The probability is based on a score obtained by summing the weights associated with a set of criteria items.

Objective To develop and validate new classification criteria for adult and juvenile idiopathic inflammatory myopathies (IIM) and their major subgroups. Methods Candidate variables were assembled from published criteria and expert opinion using consensus methodology. Data were collected from 47 rheumatology, dermatology, neurology, and pediatric clinics worldwide. Several statistical methods were utilized to derive the classification criteria. Results Based on data from 976 IIM patients (74% adults; 26% children) and 624 non‐IIM patients with mimicking conditions (82% adults; 18% children), new criteria were derived. Each item is assigned a weighted score. The total score corresponds to a probability of having IIM. Subclassification is performed using a classification tree. A probability cutoff of 55%, corresponding to a score of 5.5 (6.7 with muscle biopsy) “probable IIM,” had best sensitivity/specificity (87%/82% without biopsies, 93%/88% with biopsies) and is recommended as a minimum to classify a patient as having IIM. A probability of ≥90%, corresponding to a score of ≥7.5 (≥8.7 with muscle biopsy), corresponds to “definite IIM.” A probability of <50%, corresponding to a score of <5.3 (<6.5 with muscle biopsy), rules out IIM, leaving a probability of ≥50–<55% as “possible IIM.” Conclusion The European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria for IIM have been endorsed by international rheumatology, dermatology, neurology, and pediatric groups. They employ easily accessible and operationally defined elements, and have been partially validated. They allow classification of “definite,” “probable,” and “possible” IIM, in addition to the major subgroups of IIM, including juvenile IIM. They generally perform better than existing criteria.

ObjectiveTo develop response criteria for adult dermatomyositis (DM) and polymyositis (PM).MethodsExpert surveys, logistic regression, and conjoint analysis were used to develop 287 definitions using core set measures. Myositis experts rated greater improvement among multiple pairwise scenarios in conjoint analysis surveys, where different levels of improvement in 2 core set measures were presented. The PAPRIKA (Potentially All Pairwise Rankings of All Possible Alternatives) method determined the relative weights of core set measures and conjoint analysis definitions. The performance characteristics of the definitions were evaluated on patient profiles using expert consensus (gold standard) and were validated using data from a clinical trial. The nominal group technique was used to reach consensus.ResultsConsensus was reached for a conjoint analysis–based continuous model using absolute percent change in core set measures (physician, patient, and extramuscular global activity, muscle strength, Health Assessment Questionnaire, and muscle enzyme levels). A total improvement score (range 0–100), determined by summing scores for each core set measure, was based on improvement in and relative weight of each core set measure. Thresholds for minimal, moderate, and major improvement were ≥20, ≥40, and ≥60 points in the total improvement score. The same criteria were chosen for juvenile DM, with different improvement thresholds. Sensitivity and specificity in DM/PM patient cohorts were 85% and 92%, 90% and 96%, and 92% and 98% for minimal, moderate, and major improvement, respectively. Definitions were validated in the clinical trial analysis for differentiating the physician rating of improvement (P < 0.001).ConclusionThe response criteria for adult DM/PM consisted of the conjoint analysis model based on absolute percent change in 6 core set measures, with thresholds for minimal, moderate, and major improvement.

Objective To examine the influence of solar cycle and geomagnetic effects on the incidence of giant cell arteritis (GCA) and rheumatoid arthritis (RA). Methods We used data from patients with GCA (1950–2004) and RA (1955–2007) obtained from population-based cohorts. Yearly trends in age-adjusted and sex-adjusted incidence were correlated with the F10.7 index (solar radiation at 10.7 cm wavelength, a proxy for the solar extreme ultraviolet radiation) and AL index (a proxy for the westward auroral electrojet and a measure of geomagnetic activity). Fourier analysis was performed on AL, F10.7, and GCA and RA incidence rates. Results The correlation of GCA incidence with AL is highly significant: GCA incidence peaks 0–1 year after the AL reaches its minimum (ie, auroral electrojet reaches a maximum). The correlation of RA incidence with AL is also highly significant. RA incidence rates are lowest 5–7 years after AL reaches maximum. AL, GCA and RA incidence power spectra are similar: they have a main peak (periodicity) at about 10 years and a minor peak at 4–5 years. However, the RA incidence power spectrum main peak is broader (8–11 years), which partly explains the lower correlation between RA onset and AL. The auroral electrojets may be linked to the decline of RA incidence more strongly than the onset of RA. The incidences of RA and GCA are aligned in geomagnetic latitude. Conclusions AL and the incidences of GCA and RA all have a major periodicity of about 10 years and a secondary periodicity at 4–5 years. Geomagnetic activity may explain the temporal and spatial variations, including east-west skewness in geographic coordinates, in GCA and RA incidence, although the mechanism is unknown. The link with solar, geospace and atmospheric parameters need to be investigated. These novel findings warrant examination in other populations and with other autoimmune diseases.

Objective To identify the clinical and laboratory predictors of clinical improvement in a cohort of myositis patients treated with rituximab. Methods We analyzed data for 195 patients with myositis (75 with adult polymyositis [PM], 72 with adult dermatomyositis [DM], and 48 with juvenile DM) in the Rituximab in Myositis trial. Clinical improvement was defined as 20% improvement in at least 3 of the following 6 core set measures of disease activity: physician's and patient's/parent's global assessment of disease activity, manual muscle testing, physical function, muscle enzymes, and extramuscular disease activity. We analyzed the association of the following baseline variables with improvement: myositis clinical subgroup, demographics, myositis damage, clinical and laboratory parameters, core set measures, rituximab treatment, and myositis autoantibodies (antisynthetase, anti–Mi‐2, anti–signal recognition particle, anti–transcription intermediary factor 1γ [TIF‐1γ], anti‐MJ, other autoantibodies, and no autoantibodies). All measures were univariately assessed for association with improvement using time‐to‐event analyses. A multivariable time‐dependent proportional hazards model was used to evaluate the association of individual predictive factors with improvement. Results In the final multivariable model, the presence of an antisynthetase, primarily anti–Jo‐1 (hazard ratio [HR] 3.08, P < 0.01), anti–Mi‐2 (HR 2.5, P < 0.01), or other autoantibody (HR 1.4, P = 0.14) predicted a shorter time to improvement compared to the absence of autoantibodies. A lower physician's global assessment of damage (HR 2.32, P = 0.02) and juvenile DM (versus adult myositis) (HR 2.45, P = 0.01) also predicted improvement. Unlike autoantibody status, the predictive effect of physician's global assessment of damage and juvenile DM diminished by week 20. Rituximab treatment did not affect these associations. Conclusion Our findings indicate that the presence of antisynthetase and anti–Mi‐2 autoantibodies, juvenile DM subset, and lower disease damage strongly predict clinical improvement in patients with refractory myositis.

ObjectiveTo identify new genetic associations with juvenile and adult dermatomyositis (DM).MethodsWe performed a genome‐wide association study (GWAS) of adult and juvenile DM patients of European ancestry (n = 1,178) and controls (n = 4,724). To assess genetic overlap with other autoimmune disorders, we examined whether 141 single‐nucleotide polymorphisms (SNPs) outside the major histocompatibility complex (MHC) locus, and previously associated with autoimmune diseases, predispose to DM.ResultsCompared to controls, patients with DM had a strong signal in the MHC region consisting of GWAS‐level significance (P < 5 × 10–8) at 80 genotyped SNPs. An analysis of 141 non‐MHC SNPs previously associated with autoimmune diseases showed that 3 SNPs linked with 3 genes were associated with DM, with a false discovery rate (FDR) of <0.05. These genes were phospholipase C–like 1 (PLCL1; rs6738825, FDR = 0.00089), B lymphoid tyrosine kinase (BLK; rs2736340, FDR = 0.0031), and chemokine (C‐C motif) ligand 21 (CCL21; rs951005, FDR = 0.0076). None of these genes was previously reported to be associated with DM.ConclusionOur findings confirm the MHC as the major genetic region associated with DM and indicate that DM shares non‐MHC genetic features with other autoimmune diseases, suggesting the presence of additional novel risk loci. This first identification of autoimmune disease genetic predispositions shared with DM may lead to enhanced understanding of pathogenesis and novel diagnostic and therapeutic approaches.