International forum of allergy & rhinology · 2026
Prediction of Rhinologic Surgery Within 90 Days Using Item-Level SNOT-22 Responses: A Multisite Machine Learning Study of 35,170 Patients.
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Sramek Michael, Kumar Nitish, Patel Shrinath, Miglani Amar, Marino Michael, Lal Devyani
Аннотация
BACKGROUND: Optimizing access to cost-effective care in capacity-constrained health systems is a contemporary imperative. We evaluated whether machine learning (ML) models using patient-reported data could optimize access for patients requiring surgical care in rhinology clinics, without need for CT imaging. The outcome was defined as any rhinologic surgery within 90 days of initial evaluation.
METHODS: A de-identified electronic dataset from patients seen at five distinct sites within an integrated healthcare system between 2018 and 2025 was used to train models. Demographic data and 22-item Sinonasal Outcome Test (SNOT-22) responses were studied. Models were trained using stratified 5-fold cross-validation with an 80% development cohort and validated in a 20% held-out validation cohort. Hyperparameters were optimized with Optuna. The primary outcome was performance of any rhinologic surgical intervention within 90 days of initial SNOT-22.
RESULTS: Data from 35,170 patients were evaluated. Item-level responses outperformed use of total SNOT-22 score in the models. Among models, XGBoost demonstrated best discrimination with AUC of 0.70 (95% CI, 0.69-0.71), outperforming logistic regression (0.66), random forest (0.63), and TabNet (0.66) (all p < 0.001). At optimal threshold, XGBoost achieved 66% sensitivity, 64% specificity, 28% PPV, and 90% NPV. Top predictors for surgery were age, nasal blockage, facial pain or pressure, and decreased sense of smell or taste. Validation on the held-out cohort remained stable (AUC 0.70), with strong discrimination across sites despite surgical rates ranging from 11.5% to 27.4%.
CONCLUSIONS: Demographics and item-level SNOT-22 responses were successful in developing an ML model that demonstrated moderate discrimination and high NPV (90%) for performance of surgery within next 90 days. ML models balanced with human oversight may accelerate triage and optimize surgical yield for rhinology clinics.
METHODS: A de-identified electronic dataset from patients seen at five distinct sites within an integrated healthcare system between 2018 and 2025 was used to train models. Demographic data and 22-item Sinonasal Outcome Test (SNOT-22) responses were studied. Models were trained using stratified 5-fold cross-validation with an 80% development cohort and validated in a 20% held-out validation cohort. Hyperparameters were optimized with Optuna. The primary outcome was performance of any rhinologic surgical intervention within 90 days of initial SNOT-22.
RESULTS: Data from 35,170 patients were evaluated. Item-level responses outperformed use of total SNOT-22 score in the models. Among models, XGBoost demonstrated best discrimination with AUC of 0.70 (95% CI, 0.69-0.71), outperforming logistic regression (0.66), random forest (0.63), and TabNet (0.66) (all p < 0.001). At optimal threshold, XGBoost achieved 66% sensitivity, 64% specificity, 28% PPV, and 90% NPV. Top predictors for surgery were age, nasal blockage, facial pain or pressure, and decreased sense of smell or taste. Validation on the held-out cohort remained stable (AUC 0.70), with strong discrimination across sites despite surgical rates ranging from 11.5% to 27.4%.
CONCLUSIONS: Demographics and item-level SNOT-22 responses were successful in developing an ML model that demonstrated moderate discrimination and high NPV (90%) for performance of surgery within next 90 days. ML models balanced with human oversight may accelerate triage and optimize surgical yield for rhinology clinics.