Revised European Scleroderma Trials and Research Group Activity Index is the best predictor of short-term severity accrual

医学 硬皮病(真菌) 内科学 索引(排版) 增加物 期限(时间) 重症监护医学 病理 计算机科学 量子力学 物理 会计 万维网 业务 接种 收益
作者
Serena Fasano,Antonella Riccardi,Valentina Messiniti,Paola Caramaschi,Edoardo Rosato,Britta Maurer,Vanessa Smith,Elise Siegert,Ellen De Langhe,Valeria Riccieri,Paolo Airó,Carina Mihai,Jérôme Avouac,Elisabetta Zanatta,Ulrich A. Walker,Florenzo Iannone,Paloma García de la Peña Lefebvre,Jörg H. W. Distler,Alessandra Vacca,Oliver Distler,Otylia Kowal‐Bielecka,Yannick Allanore,Gabriele Valentini
出处
期刊:Annals of the Rheumatic Diseases [BMJ]
卷期号:78 (12): 1681-1685 被引量:16
标识
DOI:10.1136/annrheumdis-2019-215787
摘要

Background The European Scleroderma Trials and Research Group (EUSTAR) recently developed a preliminarily revised activity index (AI) that performed better than the European Scleroderma Study Group Activity Index (EScSG-AI) in systemic sclerosis (SSc). Objective To assess the predictive value for short-term disease severity accrual of the EUSTAR-AI, as compared with those of the EScSG-AI and of known adverse prognostic factors. Methods Patients with SSc from the EUSTAR database with a disease duration from the onset of the first non-Raynaud sign/symptom ≤5 years and a baseline visit between 2003 and 2014 were first extracted. To capture the disease activity variations over time, EUSTAR-AI and EScSG-AI adjusted means were calculated. The primary outcome was disease progression defined as a Δ≥1 in the Medsger’s severity score and in distinct items at the 2-year follow-up visit. Logistic regression analysis was carried out to identify predictive factors. Results 549 patients were enrolled. At multivariate analysis, the EUSTAR-AI adjusted mean was the only predictor of any severity accrual and of that of lung and heart, skin and peripheral vascular disease over 2 years. Conclusion The adjusted mean EUSTAR-AI has the best predictive value for disease progression and development of severe organ involvement over time in SSc.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Microgan完成签到,获得积分10
刚刚
进击的小胳膊完成签到,获得积分10
1秒前
1秒前
科研通AI5应助徐徐采纳,获得80
1秒前
1秒前
Orange应助Joshua采纳,获得10
2秒前
3秒前
3秒前
4秒前
蒋时晏应助陶醉薯片采纳,获得30
4秒前
4秒前
执着的灯泡完成签到,获得积分10
4秒前
睡到自然醒完成签到 ,获得积分10
5秒前
5秒前
5秒前
5秒前
Musen完成签到,获得积分10
5秒前
科研通AI5应助叫滚滚采纳,获得10
5秒前
5秒前
123456发布了新的文献求助10
5秒前
大方安白发布了新的文献求助10
6秒前
Hello应助正直冰露采纳,获得10
6秒前
lyy完成签到 ,获得积分10
7秒前
沈随便发布了新的文献求助10
7秒前
7秒前
7秒前
8秒前
灵巧荆发布了新的文献求助10
8秒前
丘奇发布了新的文献求助10
8秒前
8秒前
8秒前
通~发布了新的文献求助10
9秒前
9秒前
搜集达人应助FloppyWow采纳,获得10
9秒前
Musen发布了新的文献求助10
9秒前
pluto应助金宝采纳,获得10
10秒前
ii完成签到 ,获得积分10
10秒前
温言发布了新的文献求助10
10秒前
CodeCraft应助务实盼海采纳,获得10
11秒前
orixero应助JUSTs0so采纳,获得10
11秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527742
求助须知:如何正确求助?哪些是违规求助? 3107867
关于积分的说明 9286956
捐赠科研通 2805612
什么是DOI,文献DOI怎么找? 1540026
邀请新用户注册赠送积分活动 716884
科研通“疑难数据库(出版商)”最低求助积分说明 709762