准分子激光原位角膜磨镶术(laser-assisted in situ keratomileusis,LASIK)是矫正屈光不正的重要角膜屈光手术方式之一。经过准分子激光切削的角膜，生物测量数据发生改变。对于此类患者，通过常规测量获得的参数数据以及使用常规计算公式确定的IOL屈光度将变得不再准确，由此将会导致术后较大的屈光误差，进而影响患者的视觉质量。本文报道一例46岁的男性白内障患者。该患者既往双眼屈光不正，曾接受过LASIK手术治疗。白内障术前角膜地形图检查发现该患者双眼存在角膜偏心切削，这为IOL屈光度的确定带来困难。手术医生通过角膜地形图判断角膜切削的居中性，在特定区域内选择角膜曲率K值，并采用Barrett True K公式计算出IOL屈光度。白内障术后患眼屈光误差相对较小，视力提高，视觉质量改善。
Laser-assisted in situ keratomileusis (LASIK) is a crucial corneal refractive surgery for correcting refractive errors. The cornea, after undergoing excimer laser ablation, undergoes changes in biometric measurements. For such patients, conventional measurements and IOL power calculations based on standard formulas may no longer be accurate, leading to significant postoperative refractive errors and subsequently impacting the patient's visual quality. This article presents a case of a 46-year-old male cataract patient who had a history of refractive errors in both eyes and had previously undergone LASIK surgery. Preoperative corneal topography revealed corneal eccentric ablation in both eyes, posing challenges in determining IOL power. The surgeon assessed the centration of corneal ablation using corneal topography, selected the keratometry value (K value) within specific corneal regions, and calculated the IOL power using the Barrett True K formula. Postoperatively, the cataract patient experienced relatively minor refractive errors, leading to improved vision and enhanced visual quality.
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.
