Development and validation of a noninvasive prediction model for identifying eosinophilic asthma.

BACKGROUND:Identification of eosinophilic asthma (EA) using sputum analysis is important for disease monitoring and individualized treatment. But it is laborious and technically demanding. We aimed to develop and validate an effective model to predict EA with multidimensional assessment (MDA). METHODS:The asthma patients who underwent a successful sputum induction cytological analysis were consecutively recruited from March 2014 to January 2021. The variables assessed by MDA were screened by least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a nomogram and an online web calculator. Validation was performed internally by a bootstrap sampling method and externally in the validation cohort. Diagnostic accuracy of the model in different asthma subgroups were also investigated. RESULTS:In total of 304 patients in the training cohort and 95 patients in the validation cohort were enrolled. Five variables were identified in the EA prediction model: gender, nasal polyp, blood eosinophils, blood basophils and FeNO. The C-index of the model was 0.86 (95% CI: 0.81-0.90) in the training cohort and 0.84 (95% CI: 0.72-0.89) in the validation cohort. The calibration curve showed good agreement between the prediction and actual observation. The decision curve analysis (DCA) also demonstrated that the EA prediction model was clinically beneficial. An online publicly available web calculator was constructed (https://asthmaresearcherlimin.shinyapps.io/DynNomapp/). CONCLUSION:We developed and validated a multivariable model based on MDA to help the diagnosis of EA, which has good diagnostic performance and clinical practicability. This practical tool may be a useful alternative for predicting EA in the clinic.