[LASSO regression based risk prediction model for postoperative control in chronic sinusitis with nasal polyps]

Area of Science:

• Otolaryngology and Rhinology
• Surgical Prognosis and Risk Stratification

Context:

• Chronic sinusitis with nasal polyps (CRSwNP) poses challenges for postoperative management and recurrence.
• Accurate prediction of treatment outcomes is crucial for optimizing patient care and therapeutic strategies.

Purpose:

• To establish and validate a risk prediction model for postoperative control of CRSwNP.
• To identify key clinical and pathological factors associated with CRSwNP recurrence post-endoscopic sinus surgery.

Summary:

• A retrospective analysis of 144 CRSwNP patients identified seven significant risk factors for recurrence: allergic rhinitis, olfactory dysfunction, E/M ratio, pathological eosinophilic percentage, stromal edema, basement membrane thickening, and goblet cell hyperplasia.
• A nomogram prediction model was developed using these factors, demonstrating high accuracy with a C-index and AUC of 0.937.
• The model effectively predicts the risk of CRSwNP recurrence, aiding in personalized treatment planning.

Impact:

• Provides a validated tool for clinicians to predict postoperative CRSwNP outcomes.
• Facilitates the development of tailored therapeutic plans to improve patient management and reduce recurrence rates.
• Enhances understanding of prognostic indicators in CRSwNP, guiding future research and clinical practice.