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 "≥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.

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

Iannone F.
Membro del Collaboration Group
;
2019

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 "≥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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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/277276
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