ABSTRACT Background Dermatomyositis (DM) can present initially with respiratory symptoms and may be misdiagnosed as infection or other pulmonary disorders, leading to delayed recognition and worse outcomes. We aimed to characterize this respiratory‐onset phenotype and to evaluate whether elbow Gottron's sign is associated with respiratory‐onset and clinically meaningful adverse outcomes. Methods We conducted a single‐center retrospective cohort study of 190 DM patients. Respiratory‐onset was defined as respiratory symptoms documented as the first presenting complaint (cough, sputum production, wheezing, chest tightness, dyspnea, and/or auscultatory Velcro crackles); imaging findings were not used to define onset phenotype. Outcomes included respiratory failure (primary), progressive ILD, and death. Multivariable logistic regression and ROC analyses were performed. Results The initial symptoms of DM patients with respiratory system manifestations are mostly cough, sputum, wheezing, chest tightness, fever, and Velcro rales. Laboratory tests show that erythrocyte sedimentation rate, lactate dehydrogenase, anti‐MDA5 antibody, anti‐RO‐52 antibody, carcinoembryonic antigen, and ferritin are all higher in this group compared to those with initial symptoms from other systems. ROC curve analysis demonstrated that elbow Gottron's sign alone provided modest but significant predictive value for respiratory failure (AUC = 0.668). Notably, a combined model incorporating both respiratory symptoms and elbow Gottron's sign showed significantly improved discriminative performance (AUC = 0.765). These findings support the role of elbow Gottron's sign as a clinically relevant marker for identifying dermatomyositis with respiratory onset and for stratifying the risk of respiratory failure in this patient subgroup. Conclusions Respiratory‐onset identifies a clinically meaningful high‐risk DM phenotype with substantially increased respiratory failure. Elbow Gottron's sign is a readily observable cutaneous clue that may facilitate earlier recognition and risk stratification.
Zhang et al. (Mon,) studied this question.
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