The Usefulness of Machine Learning Models to Predict Patient-Reported Outcome Measures in Chronic Rhinosinusitis With Nasal Polyps.

Background and Objective: The treatment available for chronic rhinosinusitis with nasal polyps (CRSwNP) has remained unsatisfactory. Patient-reported outcome measures (PROMs), capturing patient-perceived health status and well-being, are vehicles for measuring and improving the efficacy of care. This study aimed to establish machine learning (ML) models to predict PROMs in CRSwNP patients using minimally invasive and easily acquired clinical data. Methods: We collected commonly available clinical predictive data from 437 patients and established four separate ML models in the training set: a least absolute shrinkage and selection operator (LASSO)-based Logistic regression, a random forest (RF) regression, a gradient-boosted decision tree (GBDT), and a deep neural network (DNN). In the test and independent external validation sets, the predictive performance of these models was measured by calculating C statistics, expected prediction results, and decision curves. A feature-ranking analysis was performed using the ML algorithm. We then developed a predictive nomogram using LASSO-based Logistic regression. Results: The models performed well on an independent external validation set, with no statistically significant differences in generalization ability metrics between groups. LASSO regression identified key features of the predictive PROMs. A nomogram was created based on multivariate Logistic regression with LASSO regularization. Conclusions: All four ML models demonstrated similar performance in predicting PROMs in CRSwNP patients from which a clinical nomogram was developed. Early prediction of the subjective treatment response is crucial, as it influences clinician decisions and facilitates effective doctor-patient communication preoperatively; this could lead to more precise and personalized treatment for CRSwNP patients.