目的：分析超声乳化联合人工晶状体(IOL)植入术后患者发生后囊膜混浊的影响因素，基于此构建列线图预测模型。

方法：回顾性队列研究，采用便利抽样法。模型组选取2019年1月至2021年3月于我院连续收治的接受超声乳化联合IOL植入手术的白内障患者。外部验证组选取2021年4月至2022年3月于我院连续收治的接受超声乳化联合IOL植入手术的白内障患者。根据随访36 mo期间是否发生后囊膜混浊分为发生后囊膜混浊组和未发生后囊膜混浊组。比较模型组发生后囊膜混浊组和未发生后囊膜混浊组患者的临床资料，多因素Logistic回归分析影响白内障患者术后发生后囊膜混浊的因素，并在此基础上构建列线图预测模型。采用校准曲线对模型组的预测模型进行校准度验证，通过决策曲线评估模型的临床预测效能。

结果：本研究模型组共纳入白内障患者256例256眼，术后发生后囊膜混浊组47例47眼(18.4%)和未发生后囊膜混浊组209例209眼。患者的年龄、眼部手术史、合并糖尿病、合并青光眼、高度近视、手术时间、麻醉方式、植入IOL材料类型比较均有差异(均P<0.05)。多因素Logistic回归分析结果显示，有眼部手术史、年龄、亲水性IOL材料、合并青光眼、手术时间均是影响白内障患者术后发生后囊膜混浊的因素(均P<0.05)。外部验证组共纳入白内障患者112例112眼，术后发生后囊膜混浊组22例22眼和未发生后囊膜混浊组90例90眼。检验结果表明，预测患者白内障术后发生后囊膜混浊的概率与实际白内障患者术后发生后囊膜混浊的概率比较均无差异(χ²_A=3.214，P_A=0.920； χ²_B=10.979，P_B=0.203)，C-index分别为0.904(0.855-0.952)、0.908(0.846-0.970)。决策曲线分析表明，当模型组患者的预测风险阈值>0.04，外部验证组中患者预测风险阈值>0.02时，该预测模型可以为临床决策提供显著的净获益。ROC曲线结果显示，训练队列和外部验证队列白内障患者术后发生后囊膜混浊风险评分分别为0.904(0.861-0.937)和0.913(0.872-0.945)，说明该预测模型具有较好的区分度。

结论：年龄、有眼部手术史、合并青光眼、手术时间、亲水性IOL材料是白内障超声乳化联合IOL植入术后患者发生后囊膜混浊的影响因素，在此基础上构建的列线图预测模型为患者术后发生后囊膜混浊的防治提供重要的策略指导。

METHODS:This was a retrospective cohort study conducted using convenience sampling. The model group comprised cataract patients who underwent phacoemulsification combined with IOL implantation at the hospital from January 2019 to March 2021. The external validation group included the same cohort of patients treated between April 2021 and March 2022. Patients were categorized into those with PCO and those without PCO based on the occurrence of PCO during follow-up. Clinical characteristics were compared between the PCO-positive and PCO-negative subgroups within the model group. Multivariate Logistic regression analysis was performed to identify factors influencing postoperative PCO in cataract patients, followed by the construction of a nomogram prediction model. The calibration curve was used to validate the model's performance in the model group, and the decision curve was employed to assess its clinical predictive efficacy.

RESULTS:The study model cohort included 256 patients(256 eyes), comprising 47 cases(47 eyes)with postoperative PCO and 209 cases(209 eyes)without PCO. Significant differences were observed in patient age, surgical history, comorbidities(diabetes, glaucoma, high myopia), operative duration, anesthesia method, and type of IOL material(all P<0.05). Multivariate Logistic regression analysis revealed that surgical history, age, hydrophilic IOL material, comorbid glaucoma, and operative duration were all influencing factors for postoperative PCO in cataract patients(all P<0.05). The external validation cohort comprised 112 cataract patients(112 eyes), including 22 cases(22 eyes)with postoperative PCO and 90 cases(90 eyes)without PCO. Statistical analyses showed no significant difference between the predicted and actual postoperative PCO probabilities(χ²_A=3.214, P_A=0.920; χ²_B=10.979, P_B=0.203), with C-index values of 0.904(0.855-0.952)and 0.908(0.846-0.970), respectively. Decision curve analysis demonstrated that the predictive model provided significant net benefit for clinical decision-making when the predicted risk threshold exceeded 0.04 in the model cohort and 0.02 in the external validation cohort. The ROC curve results demonstrated that the risk scores for PCO postoperatively in cataract patients from the training cohort and external validation cohort were 0.904(0.861-0.937)and 0.913(0.872-0.945), respectively, indicating that the predictive model exhibits good discriminative power.

CONCLUSION:Age, history of ocular surgery, comorbid glaucoma, operative duration, and hydrophilic IOL material are influencing factors for PCO after phacoemulsification combined with IOL implantation. The nomogram prediction model constructed based on these factors provides valuable guidance for the prevention and management of PCO in clinical practice.