Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China.
Department of Endocrinology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
J Transl Med. 2024 May 31;22(1):523. doi: 10.1186/s12967-024-05328-y.
Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario.
In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction.
The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively.
The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.
糖尿病性黄斑水肿（DME）是糖尿病（DM）患者视力损害的主要原因。由于缺乏快速便捷的方法，早期检测的目标尚未实现。因此，我们旨在开发和验证一种预测模型，使用易于获得的系统变量来识别 2 型糖尿病（T2DM）患者的 DME，该模型可应用于独立于眼科医生的场景。
在这项四中心、观察性研究中，共纳入 1994 例接受常规糖尿病视网膜病变筛查的 T2DM 患者，并收集他们的眼科和系统状况信息。采用逐步向前多变量逻辑回归识别 DME 的危险因素。机器学习和 MLR（多变量逻辑回归）均用于建立预测模型。使用 1300 名患者对预测模型进行训练，并前瞻性验证 104 名来自广东省人民医院（GDPH）的患者。使用来自珠江医院（ZJH）的 175 名患者、昆明医科大学第一附属医院（FAHKMU）的 115 名患者和江门市人民医院（PHJM）的 100 名患者作为外部验证集。使用接受者操作特征曲线下的面积（AUC）、准确性（ACC）、灵敏度和特异性来评估 DME 预测的性能。
DME 的风险与糖尿病病程、舒张压、红细胞压积、糖化血红蛋白和尿白蛋白/肌酐比值阶段显著相关。由于其性能优于使用所有变量的机器学习模型，因此选择基于这五个危险因素的 MLR 模型作为最终预测模型。内部验证的 AUC、ACC、灵敏度和特异性分别为 0.80、0.69、0.80 和 0.67，前瞻性验证分别为 0.82、0.54、1.00 和 0.48。在外部验证中，ZJH 的 AUC、ACC、灵敏度和特异性分别为 0.84、0.68、0.90 和 0.60，FAHKMU 分别为 0.89、0.77、1.00 和 0.72，PHJM 分别为 0.80、0.67、0.75 和 0.65。
MLR 模型是一种简单、快速、可靠的工具，可用于在无需专门眼科检查的情况下早期检测 T2DM 个体的 DME。
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