目的：干眼治疗前泪液炎症因子水平对患者治疗效果的早期预测价值。

方法：前瞻性对照观察研究。纳入2022年11月至2024年3月我院收治的干眼患者120例240眼。于干眼治疗前采用ELISA法检测患者泪液中炎症因子白介素 - 4(IL-4)、白介素 - 6(IL-6)、白介素 - 8(IL-8)、白介素 - 10(IL-10)、白介素 - 12(IL-12)、白介素 - 13(IL-13)、白介素 - 15(IL-15)、白介素 - 18(IL-18)、白介素 - 1β(IL-1β)、干扰素 - γ(INF-γ)、肿瘤坏死因子 - α(TNF-α)、粒细胞集落刺激因子(G-CSF)、粒细胞 - 巨噬细胞集落刺激因子(GM-CSF)、单核细胞趋化蛋白 - 1(MCP-1)水平，按照《中国干眼专家共识：治疗(2020年)》中的方案给予治疗，分析影响患者疗效的相关因素。

结果：连续治疗4 wk后所有患者均完成随访。根据患者疗效分为显效组60例120眼和无效组60例120眼。显效组患者治疗前泪液IL-6、IL-10、IL-18、IL-1β、TNF-α水平均低于无效组(均P<0.05)； IL-6(OR=0.994)、IL-18(OR=0.998)、IL-1β(OR=0.933)、TNF-α(OR=0.998)与干眼患者治疗效果相关(均P<0.05)； 列线图模型的C-指数为0.971(95%CI： 0.950-0.993)，校正曲线与理想曲线走形接近； 列线图模型对于干眼患者早期疗效具有一定的预测价值(敏感性=96.67%，特异性=71.67%，cutoff=208，AUC=0.866，95%CI：0.794-0.952，P<0.001)。

结论：基于干眼患者治疗前炎症因子水平构建的列线图模型能够很好地预测患者的治疗效果。

METHODS:Prospective controlled observational study. A total of 120 patients with dry eye(240 eyes)admitted to our hospital from November 2022 to March 2024 were included. Before dry eye treatment, the levels of inflammatory factors, including interlukin-4(IL-4), IL-6, IL-8, IL-10, IL-12, IL-13, IL-15, IL-18, IL-1β, interferon-γ(IFN-γ), tumor necrosis factor-α(TNF-α), granulocyte-colony stimulating factor(G-CSF), granulocyte-macrophage colony-stimulating factor(GM-CSF), monocyte chemoattractant protein-1(MCP-1)in the tear fluid were detected by ELISA. According to the treatment protocol in the Chinese Expert Consensus on the Treatment of Dry Eye(2020), the patients were given treatments, and the related factors affecting the treatment outcomes of dry eye patients were analyzed.

RESULTS:After continuous treatment for 4 wk, all the patients completed follow-up, and they were divided into the markedly effective group(60 patients, 120 eyes)and the ineffective group(60 patients, 120 eyes)based on their therapeutic effects. The markedly effective group had significantly lower pre-treatment levels of IL-6, IL-10, IL-18, IL-1β, and TNF-α than the poor efficacy group(all P<0.05). IL-6(OR=0.994), IL-18(OR=0.998), IL-1β(OR=0.933), and TNF-α(OR=0.998)were independently associated with treatment efficacy(all P<0.05). The nomogram model yielded a C-index of 0.971(95% CI: 0.950-0.993), with calibration curves closely aligned to the ideal curve. The model demonstrated significant predictive value for early therapeutic efficacy(sensitivity=96.67%, specificity=71.67%, cutoff=208, AUC=0.866, 95% CI=0.794-0.952, P<0.001).

CONCLUSION:The nomogram model constructed based on the levels of inflammatory factors in dry eye patients before treatment can well predict the treatment effect of patients.