How to quickly determine whether patients with chronic cough need corticosteroid treatment--construction of a predictive model for corticosteroid-responsive cough in chronic cough.

Patients with chronic cough need to undergo a wide range of tests and rely on empirical medication to determine the underlying cause. Corticosteroid-responsive cough (CRC) accounts for the majority of the causes of chronic cough, and the diagnostic process is relatively complicated. To develop a predictive model for diagnosis of CRC, which is based on readily available clinical information. A retrospective dataset of 304 cases was used for training, and a prospective dataset of 131 cases was used for temporal single-center validation. Candidate predictors were screened via univariate analysis and refined using LASSO regression. A final multivariable model was constructed through stepwise logistic regression and presented as a clinical nomogram. The model's performance was rigorously evaluated in terms of discrimination (AUC-ROC), calibration (calibration curve), and clinical utility (decision curve analysis). The robustness of the model predictors was further supported by their consistency with the key variables identified by an optimal machine learning model. Of 20 candidate variables based on patient basic information and test results, 7 variables were selected as optimal predictors to establish a CRC prediction model for chronic cough, including reflux symptoms, history of allergic diseases, blood eosinophils, IgE, CRP, FEV1/FVC, and FeNO50. The model demonstrated good discrimination and calibration in both the training set (AUC = 0.900 [0.865,0.935], MAE = 0.017) and the validation set (AUC = 0.840 [0.771,0.909], MAE = 0.046). The results of decision curve analysis revealed that using our models to predict CRC in chronic cough would add more benefit than either the treat-all scheme or the treat-none scheme. The variable selection for the nomogram was further validated by the high degree of agreement with the feature importance rankings derived from the top-performing machine learning model (Random Forest). Our study has developed a nomogram model, along with a simplified version, based on easily accessible clinical information and pulmonary function tests. The model demonstrates strong predictive performance and good calibration, providing clinicians with a rapid aid for initiating targeted treatment.