Background: Type 2 CRSwNP is characterized by severe symptoms, multiple comorbidities, longer recovery course and high recurrence rate. A simple and cost-effective diagnostic model for CRSwNP endotype integrating clinical characteristics and histopathological features is urgently needed.
Objective: To establish a clinical diagnostic model of inflammatory endotype in CRSwNP based on the clinical characteristics, pathological characteristics, and cytokines profile in the polyp tissue of patients.
Methods: A total of 244 participants with CRSwNP were enrolled at 2 different centers in China and Belgium from 2018 to 2020. IL-5 level of nasal polyp tissue was used as gold standard. Clinical characteristics were used to establish diagnostic models. The area under the receiver operating curve (AUC) was used to evaluate the diagnostic performance. The study was approved by the ethics board of the First Affiliated Hospital of Sun Yat-sen University ([2020] 302), and written informed consent was obtained from all subjects before inclusion.
Results: In total, 134 patients from China (training set) and 110 patients from Belgium (validation set) were included. The logistic regression (LR) model in predicting inflammatory endotype of CRSwNP showed the AUC of 83%, which was better than the diagnostic performance of machine learning models (AUC of 61.14%-82.42%), and single clinical variables. We developed a simplified scoring system based on LR model which shows similar diagnostic performance to the LR model (P = 0.6633).
Conclusion: The LR model in this diagnostic study provided greater accuracy in prediction of inflammatory endotype of CRSwNP than those obtained from the machine learning model and single clinical variable. This indicates great potential for the use of diagnostic model to facilitate inflammatory endotype evaluation when tissue cytokines are unable to be measured.
Keywords: Biomarkers; Chronic rhinosinusitis; Diagnostic model; Inflammatory endotypes; Type-2 inflammation.
© 2023 The Authors.