谷歌浏览器插件
订阅小程序
在清言上使用

Efficacy of Canakinumab in Patients with Still’s Disease Across Different Lines of Biologic Therapy: Real-Life Data from the International AIDA Network Registry for Still’s Disease

Frontiers in Medicine(2023)

引用 0|浏览14
暂无评分
摘要
IntroductionThe effectiveness of canakinumab may change according to the different times it is used after Still’s disease onset. This study aimed to investigate whether canakinumab (CAN) shows differences in short- and long-term therapeutic outcomes, according to its use as different lines of biologic treatment.MethodsPatients included in this study were retrospectively enrolled from the AutoInflammatory Disease Alliance (AIDA) International Registry dedicated to Still’s disease. Seventy-seven (51 females and 26 males) patients with Still’s disease were included in the present study. In total, 39 (50.6%) patients underwent CAN as a first-line biologic agent, and the remaining 38 (49.4%) patients were treated with CAN as a second-line biologic agent or subsequent biologic agent.ResultsNo statistically significant differences were found between patients treated with CAN as a first-line biologic agent and those previously treated with other biologic agents in terms of the frequency of complete response (p =0.62), partial response (p =0.61), treatment failure (p >0.99), and frequency of patients discontinuing CAN due to lack or loss of efficacy (p =0.2). Of all the patients, 18 (23.4%) patients experienced disease relapse during canakinumab treatment, 9 patients were treated with canakinumab as a first-line biologic agent, and nine patients were treated with a second-line or subsequent biologic agent. No differences were found in the frequency of glucocorticoid use (p =0.34), daily glucocorticoid dosage (p =0.47), or concomitant methotrexate dosage (p =0.43) at the last assessment during CAN treatment.ConclusionCanakinumab has proved to be effective in patients with Still’s disease, regardless of its line of biologic treatment.
更多
查看译文
关键词
AOSD,AutoInflammatory diseases,rare diseases,personalized medicine,treatment
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要