Predictive Modeling of Surgically Recalcitrant Chronic Rhinosinusitis Using Integrated Feature Sets
Xindong Zheng1, Yifei Wu1, Yuankai Huo1,2,3
1Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
Introduction:
Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disease with variable treatment outcomes based on endotypes. Perioperative stratification of these patients remains a significant challenge. In the present study, we sought to use machine learning (ML) approaches in conjunction with clinical and inflammatory biomarker data to identify patients presenting for revision surgery as a proxy for recalcitrant disease and better understand which data types may be useful in classifying patients.
Methods:
Six hundred thirty-four CRS patients were included. All features were categorized into 5 sets: histopathologic markers, cytokine and chemokine markers, demographic factors, comorbidities, and medication history. Leave-one-out cross-validation was employed to train random forest (RF) classifiers separately according to each feature group.
Results:
There were 298 patients with CRS without nasal polyps (CRSsNP) and 336 patients with CRS with nasal polyps (CRSwNP). The training results of the RF model revealed that for CRSsNP patients, the combination of cytokine and chemokine markers possessed the highest predictive accuracy, while the classification accuracies of other feature groups were relatively lower. For CRSwNP patients, the combination of cytokine and chemokine markers along with eosinophil and neutrophil-related markers provided the highest classification performance. Considering all patients, the classification model obtained by jointly training multiple feature combinations demonstrated commendable performance.
Conclusion:
Integrating multiple clinical and inflammatory feature sets significantly enhances the predictive modeling of surgical history in CRS patients. These findings highlight the importance of multimodal data integration for improving classification performance and provide valuable insights for developing more accurate and clinically applicable ML models in CRS. The code has been made publicly available through https://github.com/hrlblab/CRS_prediction.
