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Clustering CRSwNP Patients for Predicting Uncontrolled Outcomes Based on Clinical Features.

Ying Chen1,2,3,4,5, Jianwei Wang2,3,4,5, Yu Zhang2,3,4,5

  • 1Second Clinical Medicine College, Binzhou Medical University, Yantai, China.

Allergy, Asthma & Immunology Research
|October 4, 2025
PubMed
Summary

A new predictive model using CT scores, eosinophil counts, and age helps identify patients with uncontrolled chronic rhinosinusitis with nasal polyps (CRSwNP). This aids in tailoring treatment for better outcomes.

Keywords:
Rhinosinusitisblood eosinophil countsclinical outcomescluster analysisdecision treenasal polypspredictive procedure

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Area of Science:

  • Otolaryngology
  • Immunology
  • Medical Imaging

Background:

  • Chronic rhinosinusitis with nasal polyps (CRSwNP) presents significant heterogeneity.
  • Many CRSwNP patients exhibit poor response to current medical and surgical treatments, leading to uncontrolled disease.
  • Accurate identification of patients likely to have uncontrolled CRSwNP is challenging.

Purpose of the Study:

  • To develop an effective predictive procedure for identifying uncontrolled CRSwNP.
  • To utilize clinical features for predicting treatment outcomes in CRSwNP patients.

Main Methods:

  • Decision tree analysis was applied to clinical data from 952 adult CRSwNP patients.
  • Uncontrolled outcomes at follow-up were defined as the positive predictive event.
  • Key classification indicators identified were total computed tomography (CT) scores, age, and tissue and blood eosinophil counts.

Main Results:

  • A 6-cluster predictive model for CRSwNP uncontrolled rates was established, ranging from 2.75% to 38.27%.
  • Cluster analysis revealed distinct patient profiles based on CT scores, age, and eosinophil levels.
  • Post-surgical symptoms varied by cluster, with nasal congestion, rhinorrhea/postnasal drip, and olfactory dysfunction being prominent in specific groups.

Conclusions:

  • A decision tree model incorporating CT scores, eosinophil counts, and age effectively predicts uncontrolled CRSwNP.
  • This predictive procedure can guide clinicians in identifying patients requiring more intensive management strategies.
  • The findings support a more personalized approach to CRSwNP treatment based on specific clinical indicators.