目的：分析影响超声乳化吸除联合人工晶状体(IOL)植入术后发生前房渗出的因素。

方法：回顾性分析2021年6月至2025年6月在我院接受超声乳化吸除联合IOL植入术的白内障患者临床资料。根据术后是否出现前房渗出分为渗出组和未渗出组。收集两组患者一般资料、基础疾病情况、术前检查结果、手术相关因素、术中并发症以及术后情况。分析影响超声乳化吸除联合IOL植入术后发生前房渗出的因素。

结果：本研究共纳入接受超声乳化吸除联合IOL植入术的白内障患者患者840例840眼，根据术后是否出现前房渗出分为渗出组(42例42眼，其中男18例，女24例，平均年龄72.04±7.84岁)和未渗出组(798例798眼，其中男370例，女428例，平均年龄65.13±9.25岁)。单因素分析显示，两组间年龄、白内障病程、糖尿病、术后眼压升高、术后激素滴眼液使用持续时间及晶状体核分级比较均有差异(均P<0.05)。限制性立方样条分析显示，年龄、白内障病程、术后激素滴眼液使用持续时间与术后前房渗出风险的总体关联均有差异(均P<0.001)，非线性检验均无差异(P_{for non-linearity}>0.05)，二者呈线性关联。多因素 Logistic 回归分析表明，年龄(OR=1.096，95%CI：1.053-1.141)、白内障病程(OR=1.074，95%CI：1.042-1.107)、糖尿病(OR=2.630，95%CI：1.269-5.453)、术后眼压升高(OR=4.311，95%CI：1.851-10.041)、术后激素滴眼液使用持续时间(OR=2.082，95%CI：1.479-2.930)及晶状体核分级≥Ⅳ级(OR=2.635，95%CI：1.309-5.304)是影响术后发生前房渗出的危险因素(均P<0.05)。基于上述因素构建列线图预测模型，校准曲线提示模型预测概率与实际观测概率一致性良好； ROC曲线分析显示，模型预测术后前房渗出的AUC为0.858(95%CI：0.801-0.916)，灵敏度为0.810，特异度为0.803。

结论：超声乳化吸除联合IOL植入术后发生前房渗出受多因素影响，年龄、白内障病程、糖尿病、术后眼压升高、术后激素滴眼液使用持续时间及晶状体核硬度是其危险因素。基于上述因素构建的列线图模型对术后发生前房渗出风险具有较好的预测效能，可为临床早期识别高危人群、制定个体化干预策略提供参考依据。

METHODS: Clinical data from patients with cataract who underwent phacoemulsification combined with IOL implantation at the hospital between June 2021 and June 2025 were retrospectively analyzed. Patients were divided into an exudation group and a non-exudation group according to the presence or absence of postoperative anterior chamber exudation. Demographic characteristics, underlying diseases, preoperative examination findings, surgery-related factors, intraoperative complications, and postoperative conditions were collected and compared between the two groups. Factors associated with anterior chamber exudation after phacoemulsification combined with IOL implantation were analyzed.

RESULTS: A total of 840 cataract patients(840 eyes)who underwent phacoemulsification combined with IOL implantation were enrolled. According to the presence or absence of postoperative anterior chamber exudation, 42 patients(42 eyes)were assigned to the exudation group, including 18 men and 24 women, with a mean age of 72.04±7.84 y; the remaining 798 patients(798 eyes)were assigned to the non-exudation group, including 370 men and 428 women, with a mean age of 65.13±9.25 y. Univariate analysis showed significant between-group differences in age, duration of cataract, diabetes mellitus, postoperative elevation of intraocular pressure, duration of corticosteroid eye-drop use, and lens nuclear grade(all P<0.05). Restricted cubic spline analysis demonstrated significant overall associations of age, duration of cataract, and duration of corticosteroid eye-drop use with the risk of postoperative anterior chamber exudation(all P<0.001). No significant non-linear associations were observed(P_{for non-linearity} >0.05), indicating linear relationships. Multivariable Logistic regression analysis showed that age(OR=1.096, 95%CI: 1.053-1.141), duration of cataract(OR=1.074, 95%CI: 1.042-1.107), diabetes mellitus(OR=2.630, 95%CI: 1.269-5.453), postoperative elevation of intraocular pressure(OR=4.311, 95%CI: 1.851-10.041), duration of corticosteroid eye-drop use(OR=2.082, 95%CI: 1.479-2.930), and lens nuclear grade ≥IV(OR=2.635, 95%CI: 1.309-5.304)were risk factors for postoperative anterior chamber exudation(all P<0.05). A nomogram prediction model was developed based on these factors. The calibration curve showed good agreement between the predicted and observed probabilities. ROC curve analysis showed that the AUC of the model for predicting postoperative anterior chamber exudation was 0.858(95%CI: 0.801-0.916), with a sensitivity of 0.810 and a specificity of 0.803.

CONCLUSION: Anterior chamber exudation following phacoemulsification combined with IOL implantation is influenced by multiple factors. Age, duration of cataract, diabetes mellitus, postoperative elevation of intraocular pressure, longer duration of corticosteroid eye-drop use, and greater lens nuclear hardness are risk factors. The nomogram developed using these factors demonstrates good predictive performance for postoperative anterior chamber exudation and may facilitate the early identification of high-risk patients and the development of individualized intervention strategies.