Construction and validation of risk prediction models for different subtypes of retinal vein occlusion

IF 3.4

Advances in ophthalmology practice and research Pub Date : 2025-03-17 DOI:10.1016/j.aopr.2025.03.003

Chunlan Liang , Lian Liu , Wenjuan Yu , Qi Shi , Jiang Zheng , Jun Lyu , Jingxiang Zhong

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Abstract

Purpose

While prognostic models for retinal vein occlusion (RVO) exist, subtype-specific risk prediction tools for central retinal vein occlusion (CRVO) and branch retinal vein occlusion (BRVO) remain limited. This study aimed to construct and validate distinct CRVO and BRVO risk stratification nomograms.

Methods

We retrospectively analyzed electronic medical records from a tertiary hospital in Guangzhou (January 2010–November 2024). Non-RVO controls were matched 1:4 (CRVO) and 1:2 (BRVO) by sex and year of admission. The final cohorts included 630 patients (126 CRVO cases and 504 controls) and 813 patients (271 BRVO cases and 542 controls). Predictors encompassed clinical histories and laboratory indices. Multivariate regression identified independent risk factors, and model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

Results

The CRVO-nom and BRVO-nom highlighted significant predictors, including the neutrophil-to-lymphocyte ratio (NLR). Additional risk factors for CRVO included high-density lipoprotein cholesterol (HDL-C), platelet distribution width (PDW), history of diabetes, cerebral infarction, and coronary artery disease (CAD). For BRVO, significant predictors included a history of hypertension, age, and body mass index (BMI). The AUC for CRVO-nom was 0.80 (95% CI: 0.73–0.87) in the training set and 0.77 (95% CI: 0.65–0.86) in the validation set, while BRVO-nom yielded an AUC of 0.95 (95 %CI: 0.91–0.97) in the training set and 0.95 (95% CI: 0.89–0.98) in the validation set.

Conclusions

CRVO and BRVO exhibit distinct risk profiles. The developed nomograms—CRVO-nom and BRVO-nom—provide subtype-specific risk stratification with robust discrimination and clinical applicability. An online Shiny calculator facilitates real-time risk estimation, enabling targeted prevention for high-risk populations.

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不同亚型视网膜静脉闭塞风险预测模型的构建与验证

目的虽然存在视网膜静脉闭塞（RVO）的预后模型，但针对视网膜中央静脉闭塞（CRVO）和视网膜分支静脉闭塞（BRVO）的亚型特异性风险预测工具仍然有限。本研究旨在构建和验证不同的CRVO和BRVO风险分层图。方法回顾性分析广州市某三级医院2010年1月- 2024年11月的电子病历。非rvo对照组按性别和入院年份按1:4 （CRVO）和1:2 （BRVO）匹配。最终的队列包括630例患者（126例CRVO病例和504例对照）和813例患者（271例BRVO病例和542例对照）。预测因子包括临床病史和实验室指标。多变量回归确定了独立的危险因素，并使用受试者工作特征曲线下面积（AUC）、校准图和决策曲线分析（DCA）来评估模型的性能。结果CRVO-nom和BRVO-nom突出了显著的预测因子，包括中性粒细胞与淋巴细胞比率（NLR）。CRVO的其他危险因素包括高密度脂蛋白胆固醇（HDL-C）、血小板分布宽度（PDW）、糖尿病史、脑梗死和冠状动脉疾病（CAD）。对于BRVO，重要的预测因素包括高血压史、年龄和体重指数（BMI）。CRVO-nom在训练集中的AUC为0.80 (95% CI: 0.73-0.87)，在验证集中的AUC为0.77 (95% CI: 0.65-0.86)，而BRVO-nom在训练集中的AUC为0.95 (95% CI: 0.91-0.97)，在验证集中的AUC为0.95 （95% CI: 0.89-0.98）。结论scvo和BRVO具有明显的风险特征。开发的标准图- crvo -nom和brvo -nom -提供了具有强大区分和临床适用性的亚型特异性风险分层。在线Shiny计算器有助于实时风险评估，使高风险人群能够有针对性地预防。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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