Lian Jinxiao, So Ching, McGhee Sarah Morag, Thach Thuan-Quoc, Lam Cindy Lo Kuen, Fung Colman Siu Cheung, Kwong Alfred Siu Kei, Chan Jonathan Cheuk Hung
School of Optometry, The Hong Kong Polytechnic University, Hong Kong.
School of Public Health, The University of Hong Kong, Hong Kong.
Diabetes Metab J. 2025 Mar;49(2):286-297. doi: 10.4093/dmj.2024.0142. Epub 2024 Oct 31.
The optimal screening interval for diabetic retinopathy (DR) remains controversial. This study aimed to develop a risk algorithm to predict the individual risk of referable sight-threatening diabetic retinopathy (STDR) in a mainly Chinese population and to provide evidence for risk-based screening intervals.
The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. The risk algorithm can be used to predict the individual risk of STDR within a specific time interval, or the time to reach a specific risk margin and thus to allocate a screening interval. The calibration performance was assessed by comparing the cumulative STDR events versus predicted risk over 2 years, and discrimination by using receiver operative characteristics (ROC) curve.
Duration of diabetes, glycosylated hemoglobin, systolic blood pressure, presence of chronic kidney disease, diabetes medication, and age were included in the risk algorithm. The validation of prediction performance showed that there was no significant difference between predicted and observed STDR risks in males (5.6% vs. 5.1%, P=0.724) or females (4.8% vs. 4.6%, P=0.099). The area under the receiver operating characteristic curve was 0.80 (95% confidence interval [CI], 0.78 to 0.81) for males and 0.81 (95% CI, 0.79 to 0.83) for females.
The risk algorithm has good prediction performance for referable STDR. Using a risk-based screening interval allows us to allocate screening visits disproportionally more to those at higher risk, while reducing the frequency of screening of lower risk people.
糖尿病视网膜病变（DR）的最佳筛查间隔仍存在争议。本研究旨在开发一种风险算法，以预测主要为中国人群中可转诊的威胁视力的糖尿病视网膜病变（STDR）的个体风险，并为基于风险的筛查间隔提供证据。
纳入2010年至2016年在香港接受系统性DR筛查的117418名受试者的回顾性队列数据，使用参数生存模型开发并验证风险算法。该风险算法可用于预测特定时间间隔内STDR的个体风险，或达到特定风险阈值的时间，从而分配筛查间隔。通过比较2年内累积的STDR事件与预测风险来评估校准性能，并使用受试者操作特征（ROC）曲线进行鉴别。
风险算法纳入了糖尿病病程、糖化血红蛋白、收缩压、慢性肾病的存在、糖尿病用药情况和年龄。预测性能验证显示，男性预测的和观察到的STDR风险之间无显著差异（5.6%对5.1%，P = 0.724），女性也无显著差异（4.8%对4.6%，P = 0.099）。男性受试者操作特征曲线下面积为0.80（95%置信区间[CI]，0.78至0.81），女性为0.81（95%CI，0.79至0.83）。
该风险算法对可转诊的STDR具有良好的预测性能。使用基于风险的筛查间隔使我们能够将筛查就诊不成比例地更多分配给高风险人群，同时减少低风险人群的筛查频率。
