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. Sub-classification 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 to <55% as “possible IIM”. Conclusions The 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.

Background The impact of COVID-19 on physical and mental health and employment after hospitalisation with acute disease is not well understood. The aim of this study was to determine the effects of COVID-19-related hospitalisation on health and employment, to identify factors associated with recovery, and to describe recovery phenotypes. MethodsThe Post-hospitalisation COVID-19 study (PHOSP-COVID) is a multicentre, long-term follow-up study of adults (aged ≥18 years) discharged from hospital in the UK with a clinical diagnosis of COVID-19, involving an assessment between 2 and 7 months after discharge, including detailed recording of symptoms, and physiological and biochemical testing. Multivariable logistic regression was done for the primary outcome of patient-perceived recovery, with age, sex, ethnicity, body-mass index, comorbidities, and severity of acute illness as covariates. A posthoc cluster analysis of outcomes for breathlessness, fatigue, mental health, cognitive impairment, and physical performance was done using the clustering large applications k-medoids approach. The study is registered on the ISRCTN Registry (ISRCTN10980107). Findings We report findings for 1077 patients discharged from hospital between March 5 and Nov 30, 2020, who underwent assessment at a median of 5•9 months (IQR 4•9-6•5) after discharge. Participants had a mean age of 58 years (SD 13); 384 (36%) were female, 710 (69%) were of white ethnicity, 288 (27%) had received mechanical ventilation, and 540 (50%) had at least two comorbidities. At follow-up, only 239 (29%) of 830 participants felt fully recovered, 158 (20%) of 806 had a new disability (assessed by the Washington Group Short Set on Functioning), and 124 (19%) of 641 experienced a health-related change in occupation. Factors associated with not recovering were female sex, middle age (40-59 years), two or more comorbidities, and more severe acute illness. The magnitude of the persistent health burden was substantial but only weakly associated with the severity of acute illness. Four clusters were identified with different severities of mental and physical health impairment (n=767): very severe (131 patients, 17%), severe (159, 21%), moderate along with cognitive impairment (127, 17%), and mild (350, 46%). Of the outcomes used in the cluster analysis, all were closely related except for cognitive impairment. Three (3%) of 113 patients in the very severe cluster, nine (7%) of 129 in the severe cluster, 36 (36%) of 99 in the moderate cluster, and 114 (43%) of 267 in the mild cluster reported feeling fully recovered. Persistently elevated serum C-reactive protein was positively associated with cluster severity.Interpretation We identified factors related to not recovering after hospital admission with COVID-19 at 6 months after discharge (eg, female sex, middle age, two or more comorbidities, and more acute severe illness), and four different recovery phenotypes. The severity of physical and mental health impairments were closely related, whereas cognitive health impairments w...

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. Sub-classification 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 to <55% as “possible IIM”. Conclusions The 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.

Objectives: There is a known association between myositis and cancer. The risk is greater in dermatomyositis (DM) than polymyositis (PM), although reliable methods to predict cancer risk in specific patients with myositis are not presently available. This study was undertaken to determine whether risk of developing cancer in myositis can be predicted by antibody profiling. Methods: A cross-sectional study of UK Caucasian adults with PM (n = 109), DM (n = 103) and connective tissue disease overlap (myositis/CTD-overlap, n = 70). Patients were tested for a comprehensive range of myositis-specific/associated autoantibodies. Sensitivity and specificity analyses were performed for the optimal identification of cancer risk. Results: Sixteen patients had cancer-associated myositis (CAM) (15 DM, 1 myositis/CTD-overlap). CAM patients were older at disease onset, and patients without myositis-specific/associated autoantibodies on ''routine'' laboratory testing (negative for anti-Jo-1, anti-PM-Scl, anti-U1-RNP, anti-U3-RNP, anti-Ku antibodies) had a significantly increased risk of CAM. Possession of the antibody against 155 kDa and 140 kDa protein specificities (anti-155/140 antibody) represented a significant risk factor for CAM, and was found exclusively in DM. A positive anti-155/140 antibody result proved highly specific, moderately sensitive, with high negative predictive value for CAM. A ''negative routine myositis antibody panel'' result was highly sensitive, with high negative predictive value for CAM. The combination of these two approaches was 94% sensitive, detecting 15 of 16 CAM, with 100% sensitivity and negative predictive value in DM. Conclusions: These results may help clinicians predict which patients with myositis are at greater risk of developing cancer, thus identifying those requiring aggressive diagnostic evaluation and intensive cancer surveillance at myositis onset and follow-up.