Purpose Chronic rhinosinusitis with nasal polyps (CRSwNP) can be classified into eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (non-eCRSwNP) by tissue biopsy, which is difficult to perform preoperatively. Clinical biomarkers have predictive value for the classification of CRSwNP. We aimed to evaluate the application of artificial neural network (ANN) modeling in distinguishing different endotypes of CRSwNP based on clinical biomarkers. Methods Clinical parameters were collected from 109 CRSwNP patients, and their predictive ability was analyzed. ANN and logistic regression (LR) models were developed in the training group (72 patients) and further tested in the test group (37 patients). The output variable was the diagnosis of eCRSwNP, defined as tissue eosinophil count > 10 per high-power field. The receiver operating characteristics curve was used to assess model performance. Results A total of 15 clinical features from 60 healthy controls, 60 eCRSwNP and 49 non-eCRSwNP were selected as candidate predictors. Nasal nitric oxide levels, peripheral eosinophil absolute count, total immunoglobulin E, and ratio of bilateral computed tomography scores for the ethmoid sinus and maxillary sinus were identified as important features for modeling. Two ANN models based on 4 and 15 clinical features were developed to predict eCRSwNP, which showed better performance, with the area under the receiver operator characteristics significantly higher than those from the respective LR models (0.976 vs. 0.902, P = 0.048; 0.970 vs. 0.845, P = 0.011). All ANN models had better fits than single variable prediction models (all P < 0.05), and ANN model 1 had the best predictive performance among all models. Conclusions Machine learning models assist clinicians in predicting endotypes of nasal polyps before invasive detection. The ANN model has the potential to predict eCRSwNP with high sensitivity and specificity, and is superior to the LR model. ANNs are valuable for optimizing personalized patient management.
ObjectivesTo investigate the value of secretions Eosinophilic cationic protein (ECP) detection in the diagnosis of endotypes of Chronic rhinosinusitis (CRS) and its correlation with clinical symptoms, so as to provide guidance for the clinical application of EOS and ECP detection in secretions.MethodsPatients' nasal secretions and polyps (or middle turbinate for control) were collected and their EOS% and ECP levels were measured. Correlation analysis was performed for EOS% and ECP levels in secretions and tissues, respectively. The correlation between secretions EOS% and ECP and clinical symptom scores (symptomatic visual analog scale (VAS) scores, Lanza‐kennedy scores from nasal endoscopy and Lund‐Mackay scores from sinus CT) was further analyzed. Receiver operating characteristic curves were used to assess the predictive potential of EOS% and ECP in nasal secretions.ResultsEosinophilic chronic rhinosinusitis (ECRS) patients had higher concentrations of ECP in nasal secretions than healthy subjects and NECRS (non‐eosinophilic CRS) (p < 0.0001;0.0001); EOS% in nasal secretions was higher in ECRS than healthy subjects (p = 0.0055), but the differences between ECRS and NECRS were not statistically significant (p = 0.0999). Correlation analysis showed that tissue EOS% was correlated with ECP concentration and EOS% in nasal secretions (R = 0.5943;0.2815). There was a correlation between EOS% in secretions with a total LM score (R = 0.3131); ECP concentration in secretions with a total LK score (R = 0.3792). To diagnose ECRS, the highest area under the curve (0.8230) was determined for ECP in secretions; the highest area under the curve (0.6635) was determined for EOS% in secretions.ConclusionMeasurement of ECP in nasal secretions is useful for non‐invasive diagnosis of ECRS.Level of EvidenceLevel 3 Laryngoscope, 2023
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