NHMRC Clinical Trials Centre, The University of Sydney, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia.
NHMRC Clinical Trials Centre, The University of Sydney, Australia; Centre for Public Health, Queens University, Belfast, Northern Ireland, United Kingdom.
Diabetes Res Clin Pract. 2022 Apr;186:109835. doi: 10.1016/j.diabres.2022.109835. Epub 2022 Mar 18.
To evaluate the risk algorithm by Aspelund et al. for predicting sight-threatening diabetic retinopathy (STDR) in Type 2 diabetes (T2D), and to develop a new STDR prediction model.
The Aspelund et al. algorithm was used to calculate STDR risk from baseline variables in 1012 participants in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) ophthalmological substudy, compared to on-trial STDR status, and receiver operating characteristic analysis performed. Using multivariable logistic regression, traditional risk factors and fenofibrate allocation as STDR predictors were evaluated, with bootstrap-based optimism-adjusted estimates of predictive performance calculated.
STDR developed in 28 participants. The Aspelund et al. algorithm predicted STDR at 2- and 5-years with area under the curve (AUC) 0.86 (95% CI 0.77-0.94) and 0.86 (0.81-0.92), respectively. In the second model STDR risk factors were any DR at baseline (OR 24.0 [95% CI 5.53-104]), HbA1c (OR 1.95 [1.43-2.64]) and male sex (OR 4.34 [1.32-14.3]), while fenofibrate (OR 0.13 [0.05-0.38]) was protective. This model had excellent discriminatory ability (AUC = 0.89).
The algorithm by Aspelund et al. predicts STDR well in the FIELD ophthalmology substudy. Logistic regression analysis found DR at baseline, male sex, and HbA1c were predictive of STDR and, fenofibrate was protective.
评估 Aspelund 等人用于预测 2 型糖尿病（T2D）患者发生威胁视力的糖尿病视网膜病变（STDR）风险的算法，并开发一种新的 STDR 预测模型。
在 Fenofibrate Intervention and Event Lowering in Diabetes（FIELD）眼科子研究的 1012 名参与者中，使用 Aspelund 等人的算法根据基线变量计算 STDR 风险，并与临床试验中的 STDR 状态进行比较，同时进行受试者工作特征（ROC）分析。使用多变量逻辑回归，评估将传统危险因素和非诺贝特（fenofibrate）分配作为 STDR 预测因子，并计算基于 bootstrap 的预测性能的校正估计。
28 名参与者发生了 STDR。Aspelund 等人的算法预测 2 年和 5 年的 STDR 的曲线下面积（AUC）分别为 0.86（95%CI 0.77-0.94）和 0.86（0.81-0.92）。在第二个模型中，STDR 的风险因素为基线时存在任何糖尿病视网膜病变（OR 24.0 [95%CI 5.53-104]）、糖化血红蛋白（HbA1c）（OR 1.95 [1.43-2.64]）和男性（OR 4.34 [1.32-14.3]），而非诺贝特（OR 0.13 [0.05-0.38]）具有保护作用。该模型具有出色的区分能力（AUC=0.89）。
Aspelund 等人的算法在 FIELD 眼科子研究中能够很好地预测 STDR。逻辑回归分析发现基线时存在糖尿病视网膜病变、男性和糖化血红蛋白是 STDR 的预测因素，而非诺贝特具有保护作用。
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