MULTIFACTORIAL ANALYSIS FOR PREDICTING CHRONIC RHINOSINUSITIS RECURRENCE AFTER FUNCTIONAL ENDOSCOPIC SURGERY USING MACHINE LEARNING COMBINATION METHODS - Naumenko, O.M.; Dieeva, Yu.V.; Hogunska, I.V.; Romaniuk, L.I.; Zyablitsev, S.V. (World of Medicine and Biology, 2025)
Chronic rhinosinusitis represents a major healthcare burden with recurrence rates after functional endoscopic sinus surgery ranging from 6 % to 65 %. Machine learning approaches offer superior predictive capabilities compared to traditional statistical methods. This retrospective study analyzed 426 CRS patients who underwent functional endoscopic sinus surgery during 2019–2025. Seven machine learning algorithms were applied, and an ensemble model was developed. A simplified clinical risk score was created based on the most significant predictors for routine clinical use. Recurrence occurred in 157 patients (36.9 %) during 32.8±9.4 months follow-up. The ensemble model demonstrated the highest performance with AUC=0.94 and accuracy =89.2 %. Key predictors included previous surgery (OR=3.24), olfactory cleft obstruction ≥2 points (OR=2.87), blood eosinophilia ≥6 % (OR=2.34), and Lund-Mackay index ≥16 (OR=1.89). The simplified clinical risk score showed acceptable predictive ability (AUC=0.89) for routine practice. A two-tier prediction approach combining high-accuracy ML models for specialized centers and simplified clinical scores for routine practice can optimize personalized treatment strategies.
Keywords: chronic rhinosinusitis; machine learning; prediction; recurrence; ensemble methods