International Forum of Allergy & Rhinology最新文献

Key points: Environmental and occupational inhalational exposures are linked to chronic rhinosinusitis The Sinonasal Occupational Airborne Pollutant Exposure (SOAPE) survey assesses lifetime exposure to inhalational irritants CRS patients who underwent sinus surgery reported higher inhalational exposures.

Background: Nasal airway obstruction (NAO) is prevalent with substantial health and quality of life burdens. Nasal valve collapse (NVC) is one structural cause of NAO. Temperature-controlled radiofrequency (TCRF) nasal valve remodeling offers an alternative to invasive surgery. Clinical efficacy is established, but the impacts of TCRF on healthcare resource utilization (HRU) and cost in real-world settings remain underexplored.

Methods: Two cohorts with NAO were defined from a large general NAO population: the TCRF cohort with an isolated TCRF (index) procedure and a propensity-matched medically managed (MM) cohort without nasal procedures. HRU and costs were evaluated within a 24-month pre-/post-index period for both.

Results: A total of 10,206 TCRF and 50,766 MM patients were analyzed. Significant post-index reductions were observed for TCRF across all-cause Evaluation & Management (E&M) visits, ENT-related procedures, and sleep-related claim categories. A large reduction in mean daily post-index costs was seen for the TCRF cohort: $68.07 pre-index to $38.75 post-index (-43.1%). Mean daily costs went up in the MM cohort from $42.08 pre-index to $63.26 post-index (+50.4%), resulting in total cost savings of $21,418.26 for the TCRF cohort and a total cost increase of $15,471.99 in the MM cohort in the 24-month post-index period. TCRF cost reductions were driven by reductions in NAO-related HRU.

Conclusions: In this large, real-world analysis, TCRF treatment for NVC-related NAO demonstrated substantial reductions in HRU and total costs of care, demonstrating sustained savings over 2 years relative to MM patients.

Background: Endoscopic sinus surgery (ESS) fails to adequately address symptoms in some chronic rhinosinusitis (CRS) patients. This study aims to evaluate machine learning models (MLMs) in predicting ESS outcomes from preoperative data.

Methods: CRS patients who underwent primary ESS were used to train and test MLMs for the identification of the minimal clinically important difference (MCID) between pre- and post-operative total SNOT-22 scores. Fifty-nine preoperative predictors were included in the MLMs. Each model was optimized using K-fold cross validation. The performance of two MLMs and a conventional logistic regression (LR) model was evaluated.

Results: The study cohort constituted 242 patients. The Ensemble model achieved the highest discriminative performance (Ensemble area-under-the-curve [AUC] 0.89, 95% CI 0.87-0.91; LR AUC 0.85, 95% CI 0.83-0.87; XGBoost AUC 0.74, 95% CI 0.72-0.76, p < 0.05) and accuracy (Ensemble 87.8%; LR 81.6%; XGBoost 81.6%). The XGBoost model achieved the highest sensitivity (XGBoost 97.1%; LR 76.5%; Ensemble 85.3%) but poorer specificity (XGBoost 46.7%; LR 93.3%; Ensemble 93.3%). The most predictive factors in the Ensemble model were age, preoperative SNOT-22 and Patient Health Questionnaire 2 (PHQ-2) scores, nasal obstruction, and facial pain/pressure.

Conclusion: The Ensemble MLM achieved the highest overall accuracy and discriminative performance and matched LR for the highest specificity, suggesting that this model could be used in clinical decision-making to "rule in" patients likely to achieve MCID after ESS. This is the highest performance achieved in an MLM trained toward clinical outcomes in CRS. After validation in a multicenter cohort, the Ensemble MLM could become a useful adjunct in clinical decision-making for ESS.

Background: Chronic rhinitis (CR) is currently recognized as a syndrome that manifests in different phenotypes. We aimed to establish an artificial intelligence system (quantitative assessment of nasal inflammatory cytology, QANIC) on the basis of whole-slide images (WSIs) to enable quantitative assessment of nasal inflammatory cells.

Methods: During the development phase of QANIC, we screened nasal secretion smears from 145 CR patients for deep learning and obtaining a robust model. Subsequently, QANIC was applied to an internal cohort (N = 881) and an independent external validation cohort comprising two clinical centers (N = 234). Cluster analysis was employed to analyze two inflammatory variables (nasal and blood eosinophil [Eos] percentages) to investigate the clinical characteristics and inflammatory patterns of different clusters.

Results: Three clusters of inflammatory phenotypes were defined in CR patients: Cluster 1 (high nasal and high blood Eoss, accounted for 17.14% and 16.24% in the two cohorts, respectively), Cluster 2 (high nasal but low blood Eoss, 45.86% and 45.30%), and Cluster 3 (low nasal and low blood Eoss, 37.00% and 38.46%). Compared to Cluster 3, Clusters 1 and 2 demonstrated more severe clinical symptoms and nasal Type 2 inflammation, along with a diagnostic advantage in identifying seasonal allergic rhinitis.

Conclusions: The QANIC marks the first time deep learning has been combined with WSIs for nasal cytology diagnosis. Subtyping rhinitis patients based on nasal cytology play an important role in monitoring inflammation dynamics and individualizing treatment.

Key points: High-risk dysplasia and multifocal attachment independently predict inverted papilloma recurrence. Novel nomogram generates individualized 3-, 6-, and 9-year recurrence risk estimates. Risk-stratified surveillance may optimize postoperative monitoring intensity and intervals.