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


Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Rheumatology Clinic and Institute of Physical Medicine
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:1 December 2019
Deposited On:08 Nov 2019 11:54
Last Modified:13 Nov 2019 02:05
Publisher:BMJ Publishing Group
ISSN:0003-4967
OA Status:Closed
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1136/annrheumdis-2019-215787
PubMed ID:31422354

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