角膜移植术后并发干眼症的危险因素分析及预测模型构建-现代医学

角膜移植术后并发干眼症的危险因素分析及预测模型构建

单位：南京医科大学附属南京医院/南京市第一医院眼科, 江苏南京 210006

分类号：R779.65

出版年·卷·期（页码）：2025·53·第五期（797-803）

摘要：

目的： 分析角膜移植术后并发干眼症的危险因素，基于此建立预测模型。方法： 选取2020年1月至2024年1月期间于我院行角膜移植术的331例患者，术后进行随访，根据术后1个月患者是否并发干眼症将其分为干眼症组与非干眼症组。比较两组临床因素差异，采用多因素Logistic回归分析筛选角膜移植术后并发干眼症的危险因素，根据结果构建预测模型。采用受试者工作特征(ROC)曲线以及Homer-Lemeshow检验评估模型效能。结果： 本研究纳入331例角膜移植术后患者，干眼症发生率为72.21%(239/331)。多因素Logistic回归分析结果显示，年龄、全身免疫性疾病史、糖尿病史、长时间处于干燥环境以及未服用维生素补充剂是角膜移植术后并发干眼症的危险因素(P＜0.05)。Homer-Lemeshow检验拟合优度(χ²=10.867, P=0.496)显示，模型校准度良好，该模型ROC曲线下面积(AUC)为0.815(95%CI 0.726~0.904)，灵敏度为 0.791，特异度为 0.817。结论： 年龄较大、全身免疫性疾病史、糖尿病史以及长期处于干燥环境未服用维生素补充剂是术后干眼症的危险因素，基于以上因素构建的预测模型具有良好的拟合度和预测效能。建议术后护理中加强对高危人群的干眼症筛查与管理，以改善术后生活质量并降低干眼症的发生风险。

Objective: To analyze risk factors of dry eye syndrome after corneal transplantation, and to establish a prediction model based on these. Methods: A total of 331 patients who underwent corneal transplantation in our hospital from January 2020 to January 2024 were included in this study. Postoperative follow-up was conducted, and the patients were divided into dry eye syndrome group and non-dry eye syndrome group based on whether they developed dry eye syndrome one month after surgery. Clinical factors between the two groups were compared, and multivariate Logistic regression analysis was used to identify risk factors for dry eye syndrome following corneal transplantation. A predictive model was constructed based on the results, and its performance was assessed using the receiver operating characteristic(ROC) curve and the Hosmer-Lemeshow test. Results: This study included 331 patients who underwent corneal transplantation, with the incidence of dry eye syndrome being 72.21%(239/331). Multivariate Logistic regression analysis revealed that age, history of systemic autoimmune diseases, history of diabetes, prolonged exposure to a dry environment, and lack of vitamin supplementation were independent risk factors for postoperative dry eye(P < 0.05). The Hosmer-Lemeshow test demonstrated good model calibration(χ²=10.867, P=0.496). The area under the ROC curve(AUC) was 0.815(95% CI 0.726-0.904), with the sensitivity of 0.791 and the specificity of 0.817. Conclusion: Elder age, a history of systemic autoimmune diseases, a history of diabetes, prolonged exposure to dry environments and lack of vitamin supplementation are risk factors for postoperative dry eye. The predictive model based on these factors exhibited good calibration and predictive performance. Enhanced screening and management of dry eye syndrome in highrisk patients during postoperative care is recommended to improve postoperative quality of life and reduce the incidence of dry eye.

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