Introduction: The use of predictors of response to a specific treatment in patients with chronic spontaneous urticaria (CSU) can improve disease management, help prevent unnecessary healthcare costs, and save time. In this study, we aimed to identify predictors of complete response to standard-dosed and higher than standard-dosed antihistamine treatments in patients with CSU. Methods: Medical records of 475 CSU patients, 120 of them <18 years old, from 3 different centers were analyzed. We used 15 machine learning (ML) models as well as traditional statistical methods to predict complete response to standard-dosed and higher than standard-dosed antihistamine treatment based on 17 clinical parameters. Results: CSU disease activity, which was assessed by urticaria activity score (UAS), was the only clinical parameter that predicted complete response to standard-dosed and higher than standard-dosed antihistamine treatment, with ML models and traditional statistics, for all age groups. Based on ROC analyses, optimal cut-off values of disease activity to predict complete response were UAS <3 and UAS <4 for standard-dosed (area under the ROC curve [AUC] = 0.69; p = 0.001) and higher than standard-dosed (AUC = 0.79; p = 0.001) antihistamine treatments, respectively. Also, ML models identified lower total IgE (<150 IU/mL) as a predictor of complete response to a standard-dosed antihistamine and lower CRP (<3.4 mg/mL) as a predictor of complete response to higher than standard-dose antihistamine treatment. Discussion: In this study, we showed that patients with UAS <3 are highly likely to have complete response to standard-dosed AH and those with a UAS <4 are highly likely to have complete response to higher than standard-dosed AH treatment. Low CSU disease activity is the only universal predictor of complete response to AH treatment with both ML models and traditional statistics for all age groups.
