Unsupervised clustering analysis based on multidimensional features reveals distinct clinical characteristics and associated factors of different phenotypes in patients with chronic rhinosinusitis with nasal polyp
10.3760/cma.j.cn115330-20240131-00069
- VernacularTitle:基于多维特征的无监督聚类分析揭示慢性鼻窦炎伴鼻息肉不同内在型的临床特点及相关因素
- Author:
Jingyu HUANG
1
;
Yongge LUO
;
Hao LYU
;
Duo LIU
;
Yunfei WANG
;
Peiqiang LIU
;
Lu TAN
;
Rong XIANG
;
Wei ZHANG
;
Yu XU
Author Information
1. 武汉大学人民医院耳鼻咽喉头颈外科中心鼻科及变态反应科，武汉 430060
- Keywords:
Sinusitis;
Nasal polyp;
Hierarchical clustering analysis;
Recurrence;
Phenotype
- From:
Chinese Journal of Otorhinolaryngology Head and Neck Surgery
2024;59(6):590-601
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To utilize routinely available clinical parameters to uncover the clinical features of different clusters in patients with chronic rhinosinusitis with nasal polyp (CRSwNP) through unsupervised clustering analysis.Methods:The clinical data from 155 CRSwNP patients undergoing nasal endoscopic surgery at Renmin Hospital of Wuhan University from 2021 to 2023 were prospectively collected, including 112 males and 43 females, aged from 7 to 87 years. Unsupervised clustering analysis was conducted using various clinical parameters, including age, gender, smoking and drinking history, local eosinophil (EOS) and neutrophil (NEU) counts, comorbid allergic rhinitis (AR), comorbid asthma, recurrence status, serum-specific IgE, total IgE, cytokine levels, peripheral blood EOS count and percentage, Lund-Mackay CT score, the ratio of CT scores for the maxillary sinus and ethmoid sinus (E/M ratio), visual analogue scale (VAS) score, Lund-Kennedy endoscopic score, and other common clinical indicators to elucidate the clinical characteristics of each cluster. Statistical analysis was conducted using GraphPad Prism 9.5 software.Results:Hierarchical clustering analysis identified four main clusters (Cluster A1-A4), showcasing distinct characteristics such as mild nasal polyps with higher peripheral blood cytokines levels, nasal polyps accompanied by allergies and asthma, a subtype of nasal polyps with high recurrence rates dominated by neutrophils, and nasal polyps with high eosinophil levels. Further subset clustering revealed two clusters of mild polyps (Cluster B1-B2) featuring high cytokine expression and comorbid AR; and two clusters of severe polyps (Cluster B3-B4) presented with severe symptoms, high Lund-Mackay CT score, and high Lund-Kennedy endoscopic score. Variations between Cluster B3 and B4 included symptom complexity, the degree of eosinophil infiltration, and the probability of comorbid asthma. Further clustering analysis for eosinophilic nasal polyps revealed a cluster characterized by highly neutrophilic infiltration and recurrent nasal polyps. The comprehensive analysis of multi-index correlations demonstrated valuable insights into the relationships between common clinical parameters of nasal polyps, providing valuable information for a deeper understanding of the pathogenesis of CRSwNP.Conclusion:The clustering analysis in this study categorizes CRSwNP patients into different clusters based on clinical features and disease outcomes, providing a new perspective for more precise clinical treatment strategies.
