儿童白内障是全球范围内可治疗儿童盲症的主要原因之一。对于这些患儿而言，手术是恢复或保护视力的主要方法。然而，手术后的并发症，特别是青光眼相关不良事件(glaucoma-related adverse events, GRAEs)，常常成为导致儿童二次致盲的主要原因，这引起了眼科医疗领域的广泛关注。文章综述了儿童Ⅱ期人工晶状体植入术后GRAEs的影响因素，包括手术设计、眼部解剖特征、其他眼部发育异常和全身疾病等。手术设计中是否植入人工晶状体(intraocular lens，IOL)以及植入的时机和位置都对GRAEs的发生有显著影响。此外，眼部解剖特征如角膜直径、眼轴长度、前房深度、中央角膜厚度和术前晶状体厚度等，也是影响GRAEs发生的重要因素。同时，其他眼部发育异常和全身疾病，如先天性无虹膜、先天性风疹综合征等，也会增加儿童白内障术后青光眼的发生率。文章还总结了预测GRAEs的方法，并推荐使用Cox回归模型建立预测模型。这种模型可以有效地预测儿童Ⅱ期IOL植入术后在特定时间段内发展为GRAEs的概率，从而为早期识别GRAEs高危儿童提供了重要的借鉴。通过对GRAEs影响因素的深入分析和预测模型的建立，文章旨在帮助眼科医生更好地理解GRAEs的发生机制，并在手术前对患儿进行风险评估，从而选择最佳的手术方案和预防措施。这对于改善患儿的术后恢复、减少并发症、保护视功能具有重要的临床意义。

Pediatric cataract is one of the leading causes of treatable childhood blindness worldwide. For these children, surgery is the primary method to restore or preserve vision. However, postoperative complications, particularly glaucoma-related adverse events (GRAEs), often become the main reason for secondary blindness in children, attracting widespread concern in the field of ophthalmology. This study reviews the impact factors of glaucoma-related adverse events after secondary intraocular lens (IOL) implantation in children, including surgical design, ocular anatomical characteristics, other ocular developmental abnormalities, and systemic diseases. Whether to implant an IOL in the surgical design and the timing and positioning of the implantation have a significant impact on the occurrence of GRAEs. In addition, ocular anatomical characteristics, such as corneal diameter, axial length, anterior chamber depth, central corneal thickness, and preoperative lens thickness, are also important factors affecting the occurrence of GRAEs. At the same time, other ocular developmental abnormalities and systemic diseases, such as congenital aniridia and congenital rubella syndrome, also increase the incidence of glaucoma after pediatric cataract surgery. The article also summarizes methods for predicting GRAEs and recommends using the Cox regression model to establish a predictive model. This model can effectively predict the probability of children developing GRAEs after secondary IOL implantation within a specific time period, providing an important reference for the early identification of high-risk children for GRAEs. Through in-depth analysis of the impact factors of GRAEs and the establishment of predictive models, the article aims to help ophthalmologists better understand the mechanisms of GRAEs and assess the risks of children before surgery, thereby selecting the best surgical plan and preventive measures. This is of great clinical significance for improving postoperative recovery in children, reducing complications, and protecting visual function.

目的：建立并评估儿童Ⅱ期人工晶状体(intraocular lens,IOL)植入术后青光眼相关不良事件(glaucoma-related adverse events,GRAEs)的预测模型。方法：选取于中山大学中山眼科中心行Ⅱ期IOL植入术的无晶状体眼患儿205例(356眼)，并在术后对其随访3年。采用Cox比例风险模型确定GRAEs的预测因子，并建立列线图预测模型。采用随时间变化的受试者工作特征(receiver operating characteristic,ROC)曲线、决策曲线分析、Kaplan-Meier曲线评估模型性能，并通过Bootstrapping的C指数和校准图进行内部验证。结果：行Ⅱ期IOL植入术时年龄较大(HR=1.50, 95% CI: 1.03 ~2.19)、术后一过性高眼压(HR=9.06, 95% CI: 2.97~27.67)和IOL睫状沟植入术(HR=14.55, 95% CI: 2.11~100.57)是GRAEs的危险因素(均P<0.05)，并据此建立了两个列线图预测模型。在术后1、2、3年，模型1的ROC曲线下面积(area under curve,AUC)分别为0.747(95% CI: 0.776 ~0.935)、0.765 (95% CI: 0.804 ~0.936)和0.748 (95% CI: 0.736~0.918)，模型2的AUC分别为0.881 (95% CI: 0.836 ~0.926)、0.895 (95% CI: 0.852 ~0.938)和0.848 (95% CI: 0.752~0.945)。在内部验证和评价中，两种模型均表现出良好的性能和临床净效益。Kaplan-Meier曲线显示两个不同的风险组在两个模型中都能被显著且稳健地区分。此外，本研究也构建了在线风险计算器。结论：两种列线图均能灵敏、准确地识别Ⅱ期IOL植入术后GRAEs的高危患儿，有助对其进行早期识别和及时干预。

Aims: To establish and evaluate predictive models for glaucoma-related adverse events (GRAEs) following secondary intraocular lens (IOL) implantation in paediatric eyes. Methods: 205 children (356 aphakic eyes) receiving secondary IOL implantation at Zhongshan Ophthalmic Center with a 3-year follow-up were enrolled. Cox proportional hazard model was used to identify predictors of GRAEs and developed nomograms. Model performance was evaluated with time-dependent receiver operating characteristic (ROC) curves, decision curve analysis, Kaplan-Meier curves and validated internally through C-statistics and calibration plot of the bootstrap samples. Results: Older age at secondary IOL implantation (HR=1.5, 95% CI: 1.03 to 2.19), transient intraocular hypertension (HR=9.06, 95% CI: 2.97 to  27.67) and ciliary sulcus implantation (HR=14.55, 95% CI: 2.11 to 100.57) were identified as risk factors for GRAEs (all p<0.05). Two nomograms were established. At postoperatively 1, 2 and 3 years, model 1 achieved area under the ROC curves (AUCs) of 0.747 (95% CI: 0.776 to 0.935), 0.765 (95% CI: 0.804 to 0.936) and 0.748 (95% CI: 0.736 to 0.918), and the AUCs of model 2 were 0.881 (95% CI: 0.836 to 0.926), 0.895 (95% CI: 0.852 to 0.938) and 0.848 (95% CI: 0.752 to 0.945). Both models demonstrated fine clinical net benefit and performance in the interval validation. The Kaplan-Meier curves showing two distinct risk groups were well discriminated and robust in both models. An online risk calculator was constructed. Conclusions: Two nomograms could sensitively and accurately identify children at high risk of GRAEs after secondary IOL implantation to help early identification and timely intervention.