Development of a clinical prediction model for cough variant asthma

Background: Cough variant asthma (CVA) is a common cause of chronic cough but remains underdiagnosed due to limited access to bronchial provocation test. Objective: To develop a clinical prediction model for CVA based on more accessible indicators. Design: A single-center retrospective cohort study. Methods: A retrospective cohort of patients with chronic cough from January 2024 to December 2024 was included. The patients were randomly divided into a training set and an internal validation set at a ratio of 7:3. Univariable and multivariable logistic regression analyses were used to identify independent predictors of CVA. A nomogram prediction model was constructed based on these factors. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: A total of 323 patients with chronic cough were included, with 226 assigned to the training set (45 with CVA, 181 with non-CVA) and 97 to the internal validation set (23 with CVA, 74 with non-CVA). Multivariable logistic regression analysis identified increased eosinophils in induced sputum, elevated peripheral blood eosinophil count (PBEC), raised FeNO 50 , and reduced maximal mid-expiratory flow (MMEF) as independent predictors of CVA (all p < 0.05). The nomogram model developed based on these indicators demonstrated strong discriminative ability, with area under the ROC curve (AUC) of 0.93 in the training set and 0.92 in the internal validation set. Calibration curves indicated favorable predictive performance of the model. DCA confirmed net clinical benefit. Conclusion: The CVA clinical prediction model based on induced sputum cytology, PBEC, FeNO 50 and MMEF shows excellent discrimination, accuracy, calibration, and clinical applicability.