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Endoscopic Septoplasty with Limited Two-line Resection: Minimally Invasive Surgery for Septal Deviation
Published on: June 20, 2018
Cluster Analysis of Patients With Empty Nose Syndrome and Its Relation to Surgical Outcome
Chia-Hsin Lu1, Yun-Shien Lee2,3, Pei-Wen Wu1,4,5
1School of Medicine, Chang Gung University, Taoyuan, Taiwan.
Objectives/Hypothesis:
Patients with empty nose syndrome (ENS) present heterogeneous symptom patterns and varied surgical outcomes. This study aimed to stratify patients with ENS based on preoperative 25-item Sinonasal Outcome Test (SNOT-25) subdomain scores using hierarchical clustering and to evaluate the association between symptom-based clusters and postoperative outcomes.
Study Design:
Retrospective case analysis.
Methods:
Patients with ENS who underwent submucosal Medpor implantation between 2015 and 2023 were retrospectively enrolled. Preoperative SNOT-25 domain scores were Z-standardized and clustered using hierarchical agglomerative clustering. Surgical outcomes were evaluated using the Empty Nose Syndrome 6-item Questionnaire (ENS6Q). Positive predictive values (PPVs) for persistent ENS symptoms, defined as an ENS6Q score of ≥ 11 at 6 months post-surgery, were calculated.
Results:
A total of 168 patients with ENS were enrolled. Thirty-nine (23.2%) exhibited persistent ENS symptoms 6 months after surgery. Four symptom clusters were identified based on SNOT-25 subdomain scores. Cluster 1 showed the highest burden across all symptoms (PPV = 0.39). Cluster 2 demonstrated a high sleep and psychological burden with a relatively lower rhinologic burden and had the highest PPV (0.43). Cluster 3 exhibited moderate rhinologic symptoms with a lower psychological burden (PPV = 0.19). Cluster 4 presented the mildest symptoms and the lowest PPV (0.08).
Conclusions:
A high psychological burden predicts a greater risk of persistent symptoms following reconstructive surgery. Stratifying ENS patients through comprehensive evaluations of patient characteristics may help clinicians more accurately predict therapeutic outcomes and provide better personalized treatment strategies.
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