Predictors of early treatment response to antihistamines and omalizumab in chronic spontaneous urticaria.

Chronic spontaneous urticaria (CSU) is a common immune-mediated skin disorder characterized by spontaneous wheals, angioedema, or both, persisting for more than six weeks. Its pathogenesis is multifactorial, involving mast cell and basophil activation, autoimmunity and dysregulation of inflammatory and coagulation pathway. Current treatment guidelines recommended a stepwise algorithm beginning with second-generation H1-antihistamines (sgAH1) at standard doses (which can be increased up to fourfold if needed) before progressing to omalizumab (OMA). Nevertheless, a considerable proportion of patients remain unresponsive, highlighting the need for reliable predictors of treatment response to enable personalized care. This narrative review summarizes the current evidence on demographic, clinical, serological, and cellular biomarkers that may predict outcomes with sgAH1and OMA. Favorable sgAH1 response has been associated with shorter disease duration, low baseline UAS7 scores, and absence of angioedema. In contrast, high disease activity, inducible urticaria, elevated CRP or IL-6 levels, and hematological features such as increased neutrophil-to-lymphocyte ratio, basopenia, eosinopenia, and markers of coagulation activation (e.g., D-dimer, fibrinogen) are linked to resistance. Regarding OMA, predictors of good response include high total IgE levels, elevated basophil FcεRI expression, and reduction in IL-31 and D-dimer during treatment. Poor response correlates with advanced age, high BMI, comorbid autoimmune diseases, low total IgE (<40-50 IU/ml), positivity for ANA or anti-TPO antibodies, and activation markers such as CD203c. Functional test like the autologous serum skin test (ASST), basophil activation test (BAT), and histamine release assays offer additional stratification value. Composite immunological signatures integrating multiple biomarkers hold promise for guiding therapeutic decisions and improving prediction accuracy. Implementing validated markers could enable earlier identification of difficult-to-treat patients, faster disease control and more targeted therapy, advancing precision medicine in CSU.