Influencing factors of olfactory impairment in OSA and construction of nomogram prediction model.
10.13201/j.issn.2096-7993.2025.09.008
- Author:
Yunhao ZHAO
1
;
Zhihong LYU
1
;
Qisheng GUO
1
;
Zongjian RONG
1
;
Xian LUO
1
Author Information
1. Department of Psychiatry，Nanfang Hospital，Southern Medical University，Guangzhou，510515，China.
- Publication Type:Journal Article
- Keywords:
cognitive function;
influencing factors;
nomogram;
obstructive sleep apnea;
olfactory impairment
- MeSH:
Humans;
Sleep Apnea, Obstructive/physiopathology*;
Nomograms;
Olfaction Disorders/etiology*;
Logistic Models;
Middle Aged;
Male;
Female;
ROC Curve;
Adult;
Aged
- From:
Journal of Clinical Otorhinolaryngology Head and Neck Surgery
2025;39(9):842-847
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the influencing factors of olfactory impairment in patients with obstructive sleep apnea（OSA） and establish a nomogram prediction model. Methods:A total of 100 OSA patients were enrolled. Snap&Sniff olfactory test was used to evaluate the olfactory identification function and olfactory threshold of the patients. According to the scoring criteria, either olfactory identification scores below 14 points or olfactory threshold scores below 3 points was defined as olfactory impairment. Multivariate logistic regression analysis was used to explore the influencing factors of olfactory impairment in OSA. The nomogram model was constructed by using the R 4.4.2 software package. ROC curve, calibration curve and decision curve were used to evaluate the predictive efficacy, consistency and clinical utility of the model. Results:A total of 55 of 100 OSA patients had olfactory impairment. The results of multivariate logistic regression analysis showed that age, ESS score, MoCA score, and apnea-hypopnea index（AHI） were the influencing factors of olfactory impairment in OSA. Based on the above parameters, a nomogram model was established. The ROC curve analysis showed that the AUC was 0.897（95%CI 0.834-0.961）, indicating that the model had good predictive ability. The calibration curve showed that the predicted probability of the model fits the actual probability well. Decision curve analysis showed that when the threshold probability was in the range of 0-0.9, the model had a high clinical net benefit rate. Conclusion:Age, ESS score, MoCA score and AHI are the influencing factors of olfactory impairment in patients with OSA. The nomogram model constructed based on the above factors has good predictive value, which is conducive to the clinical multi-angle understanding of OSA and the formulation of scientific prevention and treatment measures.
