Citations

Abstract Objectives The lack of uniform terminology and abbreviations for anti-synthetase syndrome has led to significant challenges in research and clinical practice. This study aimed to establish an international consensus on a standardized nomenclature and abbreviation among a diverse group of global myositis experts and patient representatives. Methods This qualitative project was approved by the International Myositis Assessment and Clinical Studies Group (IMACS). Based on the outcomes of a literature review and preliminary survey, list of terms for nomenclature and abbreviations was finalized. Subsequently, two rounds of the Delphi survey were conducted among myositis experts and patient participants to ascertain current practices and preferences. Using Nominal Group Technique (NGT), the project steering committee developed a consensus nomenclature and abbreviation. Results A total of 241 and 132 myositis experts from six continents, 35 countries and 9 specialities participated in round 1 and round 2 of the Delphi survey, respectively. The results indicated a preference for the terms anti-synthetase syndrome, antisynthetase syndrome and anti-tRNA synthetase syndrome. The abbreviations ASS, ASyS and ASSD were most used, though ASS received notable disapproval. The steering committee, including twenty-five myositis experts and three patient representatives from diverse backgrounds and geographical locations, reached a consensus on ‘anti-synthetase syndrome’ as the preferred nomenclature and ASyS as the preferred abbreviation, with 100% and 93.1% votes, respectively. Conclusion The terms anti-synthetase syndrome and ASyS should be adopted as the standard nomenclature and abbreviation in both research and clinical practice. This standardization is expected to increase consistency within the literature.

Abstract Objectives To establish a framework by which experts define disease subsets in systemic sclerosis associated interstitial lung disease (SSc-ILD). Methods A conceptual framework for subclinical, clinical and progressive ILD was provided to 83 experts, asking them to use the framework and classify actual SSc-ILD patients. Each patient profile was designed to be classified by at least four experts in terms of severity and risk of progression at baseline; progression was based on 1-year follow-up data. A consensus was reached if ≥75% of experts agreed. Experts provided information on which items were important in determining classification. Results Forty-four experts (53%) completed the survey. Consensus was achieved on the dimensions of severity (75%, 60 of 80 profiles), risk of progression (71%, 57 of 80 profiles) and progressive ILD (60%, 24 of 40 profiles). For profiles achieving consensus, most were classified as clinical ILD (92%), low risk (54%) and stable (71%). Severity and disease progression overlapped in terms of framework items that were most influential in classifying patients (forced vital capacity, extent of lung involvement on high resolution chest CT [HRCT]); risk of progression was influenced primarily by disease duration. Conclusions Using our proposed conceptual framework, international experts were able to achieve a consensus on classifying SSc-ILD patients along the dimensions of disease severity, risk of progression and progression over time. Experts rely on similar items when classifying disease severity and progression: a combination of spirometry and gas exchange and quantitative HRCT.

Objective To assess the efficacy and tolerability of tocilizumab in a multicenter, randomized, double‐blind, placebo‐controlled trial in refractory adult patients with dermatomyositis (DM) and polymyositis (PM). Methods Thirty‐six subjects with probable or definite DM/PM were enrolled in a 6‐month phase 2B clinical trial and randomized 1:1 to receive tocilizumab (8 mg/kg intravenously) or placebo every 4 weeks for 24 weeks. Eligible subjects had either a DM rash, a myositis‐associated autoantibody or an adjudicated PM diagnosis. Active disease was defined by at least three of six abnormal core set measures (CSMs), including a manual muscle testing (MMT)‐8 score of less than 136/150. If the MMT‐8 score was greater than 136, then a cutaneous score of 3 or more (10 cm visual analogue scale) was required along with three additional abnormal CSMs indicating disease activity. The primary endpoint compared the Total Improvement Score (TIS) between both arms from week 4 to 24. Secondary outcomes included time to meeting minimal TIS improvement, changes in CSMs, time to worsening, steroid‐sparing effect, proportion of subjects meeting more stringent improvement criteria, and safety outcomes. Results There was no significant difference ( P = 0.86) in the TIS over 24 weeks between tocilizumab and placebo arms. The secondary endpoints of time to improvement (minimal, moderate, or major), time to worsening, CSM changes, safety outcomes, and steroid‐sparing effect were also not significantly different between arms. Conclusion Tocilizumab was safe and well tolerated but did not meet the primary or secondary efficacy outcomes in refractory DM and PM in this 24‐week phase 2B study.

ObjectivePublished predictive models of disease outcomes in idiopathic inflammatory myopathies (IIMs) are sparse and of limited accuracy due to disease heterogeneity. Computational methods may address this heterogeneity by partitioning patients based on clinical and biological phenotype.MethodsTo identify new patient groups, we applied similarity network fusion (SNF) to clinical and biological data from 168 patients with myositis (64 adult polymyositis [PM], 65 adult dermatomyositis [DM], and 39 juvenile DM [JDM]) in the Rituximab in Myositis trial. We generated a sparse proof‐of‐concept bedside classifier using multinomial regression and identified characteristics that distinguished these groups. We conducted χ2 tests to link new patient groups with the myositis subtypes.ResultsSNF identified five patient groups in the discovery cohort that subdivided the myositis subtypes. The sparse multinomial regressor to predict patient group assignments (areas under the receiver operating characteristic curve = [0.78, 0.97]; areas under the precision‐recall curve = [0.55, 0.96]) found that autoantibody enrichment defined four of these groups: anti–Mi‐2, anti–signal recognition peptide (SRP), anti–nuclear matrix protein 2 (NXP2), and anti‐synthetase (Syn). Depletion of immunoglobulin M (IgM) defined the fifth group. Each group was associated with one subtype, with adult DM being associated with anti–Mi‐2 and anti‐Syn autoantibodies, JDM being associated with anti‐NXP2 autoantibodies, and adult PM being associated with IgM depletion and anti‐SRP autoantibodies. These associations enabled us to further resolve the current myositis subtypes.ConclusionUsing unsupervised machine learning, we identified clinically and biologically homogeneous groups of patients with IIMs, forming the basis of an integrated disease classification based on both clinical and biological phenotype, thus validating other approaches and what has been previously described.

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 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.