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OBJECTIVE: To provide guidance for clinical endotypes by constructing a risk-predictive model of eosinophilic chronic rhinosinusitis with nasal polyps (ECRSwNP). DESIGN: A cross-sectional study. SETTING: Single-centre trial at tertiary medical institutions. PARTICIPANTS: A cross-sectional study included 343 CRSwNP patients divided into ECRSwNP (n = 237) and non-ECRSwNP (n = 106) groups using surgical pathology. MAIN OUTCOME MEASURES: Single-factor and multivariate analysis were used to identify statistically significant variables for constructing a nomogram, including the history of AR, hyposmia score, ethmoid sinus score, BEP and BEC. The model's performance was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA). RESULTS: Allergic rhinitis, hyposmia score, ethmoid sinus score, peripheral blood eosinophil percentage (BEP) and eosinophil count (BEC) were retained for the construction nomogram of ECRSwNP. The nomogram exhibited a certain accuracy, with an AUC of 0.897 (95% CI: 0.864-0.930), good agreement in the calibration curve and a 0.891 C-index of internal validation. Moreover, the DCA with a threshold probability between 0.0167 and 1.00 indicated a higher net benefit and greater clinical utility. CONCLUSION: The construction of a predictive risk model of ECRSwNP based on easily accessible factors could assist clinicians in more conveniently defining endotypes to make optimal diagnoses and treatment choices.
RESUMEN
PURPOSE: To determine whether mitochondrial DNA copy number (mtDNA-CN) is associated with allergic rhinitis (AR), and further establish a nomogram model for the early diagnosis of AR. METHODS: We carried out a case-control study involving a total of 134 subjects, including 66 healthy controls and 68 AR patients. The mtDNA-CN in peripheral blood of all subjects was detected by real-time fluorescence quantitative polymerase chain reaction, and general information of patients was recorded. And, least absolute shrinkage selection operator (LASSO) regression was used to screen clinically significant variables, which were substituted into a logistic regression analysis to determine independent risk factors. Next, a nomogram model was developed for the risk prediction of AR. Then, internal validation was performed with the bootstrap resampling. Ultimately, the clinical benefit and validity of the nomogram were assessed by receiver operating characteristic (ROC) curve, bias-corrected curve, and decision curve analysis (DCA). RESULTS: MtDNA-CN and total IgE were determined as independent risk factors of AR. The final model achieved an area under the ROC curve (AUC) of 0.869, and the DCA curve demonstrated that the nomogram was clinically beneficial for practical application. CONCLUSION: An increase of the mtDNA-CN was linked to the occurrence risk of AR. The nomogram prediction model based on mtDNA-CN showed the potential clinical utility in improving risk prediction and providing new insights for exploring the pathogenesis of AR.