Longitudinal study of patients with anti-SAE antibody-positive dermatomyositis: a multicenter cohort study in China

医学 皮肌炎 队列研究 队列 间质性肺病 内科学 外科 胃肠病学
作者
Yingfang Zhang,Lei Liu,Xinwang Duan,Hui Pi,Lili Jiang,Jiang Li,Guochun Wang,Xiaoming Shu
出处
期刊:Rheumatology [Oxford University Press]
被引量:3
标识
DOI:10.1093/rheumatology/keae232
摘要

Abstract Objectives To describe the longitudinal study and long-term prognosis of a multicentre large inception cohort of patients with anti-SAE positive DM. Methods We retrospectively recruited patients with anti-SAE+DM in four tertiary referral centres from China between March 2005 and December 2022. Long-term survival analysis was performed on the enrolled patients. The Myositis Damage Index and Cutaneous Disease Area and Severity Index were used to evaluate the degree of different organ damage and the extent of skin rashes. Longitudinal CT patterns were analysed. Phenotypes were characterized using unsupervised cluster analysis. Results All-cause death occurred in 10.5% (4/38) of all patients, in which three patients succumbed to malignancies at 13, 18 and 36 months. Most patients had favourable long-term outcomes, 35.3% of them were in drug-free remission. Skin rashes showed significant improvement evaluated by Cutaneous Disease Area and Severity Index with time. However, damage to different systems was observed in 70.6% of the surviving patients using the Myositis Damage Index, which mainly consisted of skin damage, accounting for 47.1%. Nine patients with anti-SAE+DM-associated interstitial lung disease underwent repeat CT showed marked radiological improvement at 6 months or being stable after 12 months. In further, different characteristics and outcomes were also showed in three clusters identified by unsupervised analysis. Conclusions Anti-SAE+DM is characterized with a lower mortality rate and the development of malignancies being the primary cause of death. Patients who survived showed notable cutaneous damage, while the interstitial lung disease tends to stabilize. Clusters identified with unsupervised analysis could assist physicians in identifying a higher risk of mortality.
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