Newer EUSTAR-AI Scale Ably Predicts Short-term Risk of SSc Progression, Study Finds

Newer EUSTAR-AI Scale Ably Predicts Short-term Risk of SSc Progression, Study Finds

The European Scleroderma Trials and Research Group (EUSTAR) revised activity index scale, known as EUSTAR-AI, is the best tool for predicting the short-term risk of disease progression and severe organ involvement in scleroderma (SSc) patients diagnosed within the last five years, a study reports.

It had a better predictive value for the disease’s course in a patient over the short term, affecting treatment, than the European Scleroderma Study Group activity index (EScSG-AI), its researchers said. An earlier study, published in 2017, had established EUSTAR-AI’s superiority in measures of disease activity in SSc patients.

The study, “Revised European Scleroderma Trials and Research Group Activity Index is the best predictor of short-term severity accrual,” was published in the journal Annals of the Rheumatic Diseases.

The EUSTAR group’s revised activity index (AI) was a response to reports of weaknesses in the EScSG-AI scale, like multiple missing values and it being drawn from patient groups with long-standing disease. But the predictive value of EUSTAR-AI — relative to EScSG-AI — in predicting progression and likely organ involvement in recently diagnosed patients, whose treatment plans are being considered, is not established.

An international research team analyzed clinical data, from the EUSTAR database, covering 549 SSc patients diagnosed with the onset of a non-Raynaud sign or symptom within five years and a first (baseline) visit between 2003 and 2014. Researchers assessed variations in disease activity over two years, and investigated which factors could help to predict poorer outcomes.

Disease progression was established according to the Medsger severity scale. Particularly, progression was defined as an increase of one point or more in the severity score at a follow-up exam two years after the first visit. Disease progression was also assessed through changes in key organs that make up the Medsger scale, including the lungs, heart, skin, muscle, kidney, and peripheral blood vessels.

Most study patients (81%) were women, with a mean age of 51.9 when they joined the database. Among the total patient group were 179 people (32.6%) with the more serious and potentially life-threatening diffuse SSc subtype.

Analyses revealed that the adjusted mean EUSTAR-AI score could capture any increase in disease activity progression, similar to the adjusted mean EScSG-AI values. Still, the EUSTAR-AI scale was found to be better at predicting disease progression and the development of severe organ involvement — namely of the heart, vasculature, skin and lung — at the two-year follow-up in patients with either diffuse or limited SSc.

These findings have clear clinical implications, as this scale may help in identifying patients at short-term risk of disease progression and in need of prompt care and more specific management.

“A EUSTAR-AI predictive role of organ/system severity accrual in the short term has emerged, indicating that measuring this parameter can help the clinician in adjusting treatment in such time frame,” the researchers wrote.

“Identifying patients at risk … helps the clinician in managing patients with SSc, monitoring disease state with rapid adjustments in treatment to prevent irreversible organ damage,” they concluded.

Patricia holds a Ph.D. in Cell Biology from University Nova de Lisboa, and has served as an author on several research projects and fellowships, as well as major grant applications for European Agencies. She has also served as a PhD student research assistant at the Department of Microbiology & Immunology, Columbia University, New York.
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Patricia holds a Ph.D. in Cell Biology from University Nova de Lisboa, and has served as an author on several research projects and fellowships, as well as major grant applications for European Agencies. She has also served as a PhD student research assistant at the Department of Microbiology & Immunology, Columbia University, New York.
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