A58: Demographics, Clinical Features and Therapies of Patients with Juvenile Dermatomyositis Participating in a National Myositis Patient Registry
Notice bibliographique
Résumé
Background/Purpose: The myositis syndromes are rare systemic autoimmune diseases with poorly understood etiologies. We present the demographics, illness features and treatments of patients with juvenile dermatomyositis (JDM) who enrolled in a newly created national registry. Methods: Using a patient database from The Myositis Association and supplemental advertisements, a national registry of myositis patients was established. Between December 2010 and July 2012 questionnaires were mailed to 8847 myositis patients in the US and Canada. The questionnaire queried demographics, clinical features, environmental exposures, and quality of life. Descriptive statistics and multivariable logistic regression analysis were computed using GraphPad Prysm and SAS. Results: 1956 patients (22%) returned the questionnaire and consented to participate; 1806 who met probable or definite Bohan and Peter criteria for myositis were included (708 DM, 483 PM, 466 IBM, 139 JDM, 10 JPM); juvenile patients were diagnosed before age 18 years. Of the 139 JDM patients, the median age at diagnosis was 6.9 years and median disease duration at enrollment was 10.3 years. Most JDM patients were female (78%) and Non‐Hispanic Caucasian (88%), and the remainder were Hispanic (6.5%), Asian (2.9%), multiple races (2.2%), and Black (0.7%). Patients or their parents often completed a graduate degree (23%) or college degree (29%). JDM patients were primarily diagnosed by a pediatric rheumatologist (48%), with adult and pediatric dermatologists (22%), pediatric neurologists and primary care physicians (11% each) diagnosing most of the remaining patients; 67% were under the care of a pediatric rheumatologist. JDM patients frequently had skin rashes as a major manifestation (86%); arthritis (35%) and dysphagia (32%) were also common, whereas lung disease (12%) was less frequent. An additional autoimmune disease was present in 18% of JDM patients, with JIA (8%) and SLE and celiac disease (3% each) the most frequent. There were no recorded associated malignancies. 98% of JDM patients received prednisone therapy. Methotrexate was the most common steroid‐sparing agent (84%), followed by hydroxychloroquine (60%), IV pulse solumedrol (54%), IVIG (48%), cyclosporine (19%), rituximab and anti‐TNFs (10% each). Predictors of which agents were received varied among medications, but included age, year of JDM diagnosis, gender, and region of country. Pulse solumedrol and cyclosporine were more likely to be used in JDM patients with dysphagia, hydroxychloroquine in patients with skin rashes and less likely in those with fevers, and azathioprine was less likely in patients with arthritis. Of medications utilized, 45% of JDM patients reported responding best to IVIG, 39% responded best to prednisone, 33% to anti‐TNFs, and 29% to cyclophosphamide. Conclusion: A nationwide myositis registry has been established and includes a subsample of JDM patients that appears demographically and clinically representative of other US populations. This registry may help identify environmental exposures associated with myositis, elucidate factors associated with quality of life, and serve as an important resource for future clinical investigations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».