A predictive model for acute exacerbations in adult patients with chronic spontaneous urticaria

Background Chronic spontaneous urticaria (CSU) is a common disorder of unknown etiology, characterized by recurrent, spontaneous episodes of pruritic wheals and/or angioedema lasting longer than six weeks. Acute exacerbations in CSU patients typically occur without identifiable triggers; their recurrent nature not only impairs quality of life but may also precipitate life-threatening complications. We aim to develop a predictive model for acute exacerbations in CSU patients to support clinical risk stratification and advance understanding of underlying disease mechanisms. Methods We extracted electronic medical records of patients newly diagnosed with CSU at two centers between January 2015 and December 2025. The observation indicator was acute exacerbation occurring within 30 days of the initial diagnosis. Univariable logistic regression was employed to rapidly identify variables with potential predictive value. LA S SO regression was subsequently applied to further select variables, which were then incorporated into a multivariable logistic regression to construct the predictive model. Results In this study, acute exacerbation occurred within 30 days of initial diagnosis in 137 patients (10.1%). The predictive model comprises eight variables: anxiety, depression, MT, CRP, TNF-α, D-dimer, F1 + 2, and FIB. The risk of acute exacerbation can be assessed by summing the scores corresponding to each parameter on the nomogram. The areas under the receiver operating characteristic curves were 0.900 in the training cohort and 0.869 in the validation cohort, with mean absolute errors of 0.011 and 0.017, respectively. Conclusion We developed and validated a clinical prediction model for the risk of acute exacerbation in patients with CSU, which holds promise as an objective, quantifiable tool for risk stratification to support individualized treatment strategies—thereby reducing the incidence of acute exacerbations and associated disease burden. Moreover, the key predictive variables identified in the model may provide novel insights into the underlying pathogenic mechanisms of CSU acute exacerbations.