Analysis of risk factors related to pterygium and establishment of prediction model in plateau area
10.3980/j.issn.1672-5123.2022.7.30
- VernacularTitle:高原地区翼状胬肉相关危险因素分析及预测模型的建立
- Author:
Xiao-Ying ZHANG
1
;
Xin YAN
1
;
Rui-Juan GUAN
1
;
Ling LI
1
Author Information
1. Department of Ophthalmology, Qinghai Provincial People's Hospital, Xining 810000, Qinghai Province, China
- Publication Type:Journal Article
- Keywords:
plateau area;
pterygium;
prevalence rate;
risk factors;
prediction model
- From:
International Eye Science
2022;22(7):1215-1219
- CountryChina
- Language:Chinese
-
Abstract:
AIM: To analyze the risk factors related to pterygium in plateau area and establish a prediction model.METHODS: Using the method of cluster random sampling, the long-term residents living in the plateau with an average altitude of 3 000m were selected to conduct a field survey of pterygium from June 2020 to June 2021. Single factor and multi-factor analysis were used to analyze the risk factors related to pterygium, and the R software was used to establish the prediction model.RESULTS: The actual number of people investigated in this study was 1 514, and the number of patients with pterygium was 210, the overall prevalence rate was 13.87%. The age >43 years old, plateau area residence history, sunshine time, gender, smoking history, drinking history, hypertension, diabetes and hyperlipidemia are risk factors for pterygium. Among them, the long-term sunshine was the most dangerous factor for pterygium(OR: 6.215, 95%CI: 4.008-9.636, P<0.001), followed by >43 years old(OR: 5.348, 95%CI: 2.06-13.88, P=0.001). The decision curve analysis(DCA)showed that when the Nomo score system was applied, the predicted probability of pterygium was completely consistent with the actual probability of pterygium.CONCLUSION: The risk factors of pterygium as follows, the age >43 years old, plateau area residence history, sunshine time, gender, smoking history, drinking history, hypertension, diabetes and hyperlipidemia. The Nomo scoring system prediction model can accurately predict pterygium and provide a theoretical basis for the intervention of pterygium in plateau areas.
