AI Summary & Key Takeaways
AI InsightThisreview will focus on soluble and imaging biomarkers that recently showed promising evidence foroutcome stratification in patients with SSc.
📌 3 Key Takeaways
Abstract
Systemic sclerosis (SSc) is a highly complex disease whose heterogeneity includes multiple aspectsof the condition, such as clinical presentation, progression, extent and type of organ involvement,and clinical outcomes. Thus far, these features remain not easily predictable both at the patient grouplevel and in a given patient with regard to age at onset and clinical course. The unpredictable clinicalcourse represents an obstacle to focusing potentially effective treatment in patients that need it themost. At the time of organ involvement and clinical diagnosis, most of the clinical manifestations areirreversible; therefore, predicting outcomes becomes crucial. This can explain the multiple attemptsto identify prognostic, predictive, and monitoring—both soluble and imaging—biomarkers over thepast years. They range from the currently most used biomarkers, the autoantibodies associated withdisease-specific clinical features and course, to the single recently proposed skin, lung, cardiac involvementbiomarkers and to the composite scores capturing multiple aspects of the disease. Thisreview will focus on soluble and imaging biomarkers that recently showed promising evidence foroutcome stratification in patients with SSc.