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Predicting Surgical Outcomes in Chronic Rhinosinusitis From Preoperative Patient Data: A Machine Learning Approach.

Arun Raghavan1, Ethan Sage1, Mahdi Al-Ghezi1

  • 1Division of Rhinology and Endoscopic Skull Base Surgery, Department of Otolaryngology: Head and Neck Surgery, University of Washington, Seattle, Washington, USA.

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|November 20, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict outcomes for endoscopic sinus surgery (ESS) in chronic rhinosinusitis (CRS) patients. An Ensemble model accurately identifies patients likely to benefit from ESS, aiding clinical decisions.

Keywords:
artificial intelligencechronic rhinosinusitisclinical decision‐makingmachine learningminimal clinically important difference

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Area of Science:

  • Otolaryngology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Chronic rhinosinusitis (CRS) treatment with endoscopic sinus surgery (ESS) has variable outcomes.
  • Predicting ESS success preoperatively is crucial for patient management.

Purpose of the Study:

  • To evaluate machine learning models (MLMs) for predicting minimal clinically important difference (MCID) after ESS.
  • To identify preoperative factors influencing ESS outcomes in CRS patients.

Main Methods:

  • Trained and tested MLMs using preoperative data from 242 CRS patients undergoing primary ESS.
  • Included 59 preoperative predictors and optimized models using K-fold cross-validation.
  • Compared two MLMs (Ensemble, XGBoost) against logistic regression (LR).

Main Results:

  • The Ensemble MLM demonstrated superior discriminative performance (AUC 0.89) and accuracy (87.8%).
  • Key predictors included age, SNOT-22, PHQ-2 scores, nasal obstruction, and facial pain.
  • Ensemble and LR models achieved high specificity (93.3%), while XGBoost showed higher sensitivity (97.1%).

Conclusions:

  • The Ensemble MLM accurately predicts patients likely to achieve MCID after ESS.
  • This model can aid clinical decision-making for selecting appropriate candidates for ESS.
  • Further validation in multicenter cohorts is recommended for widespread clinical adoption.