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Prediction models for postoperative uncontrolled chronic rhinosinusitis in daily practice
Xiaoyao Tao1, Fenghong Chen1, Yueqi Sun1
1Otorhinolaryngology Hospital, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
The Laryngoscope
|October 9, 2018
Summary
This study developed prediction models for uncontrolled chronic rhinosinusitis (CRS) after surgery. Key predictors include tissue eosinophilia and high CT scores, aiding in prognosis assessment.
Area of Science:
- Otolaryngology
- Surgical Outcomes
- Inflammatory Airway Diseases
Background:
- Chronic rhinosinusitis (CRS) management guidelines lack postoperative control assessment data.
- No established prediction models exist for identifying patients at risk of uncontrolled CRS post-surgery.
Purpose of the Study:
- To develop and validate predictive models for postoperative uncontrolled CRS.
- To identify independent risk factors associated with treatment failure.
Main Methods:
- Retrospective analysis of 136 patients who underwent endoscopic sinus surgery.
- Logistic regression and ROC curve analysis to identify risk factors and create models.
- Development of pathological and clinical prediction models.
Main Results:
- Nearly 30% of patients experienced uncontrolled CRS post-surgery.
- Independent risk factors identified: tissue eosinophil ratio >0.206, blood eosinophil ratio >0.025, Lund-Mackay score ≥15, and ethmoid/maxillary CT score ratio.
- Pathological and clinical models were generated to categorize CRS severity.
Conclusions:
- Simplified prediction models for uncontrolled CRS were developed.
- These models can assist otolaryngologists in predicting surgical prognosis.
- Early identification of at-risk patients can potentially improve management strategies.
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