AIMS: Diabetic retinopathy (DR) is a leading cause of vision impairment in type 2 diabetes mellitus (T2DM), with non-proliferative DR (NPDR) representing its most prevalent form. Early identification of high-risk individuals remains challenging due to the complexity and poor interpretability of existing machine learning models. This study aims to develop a clinically interpretable Logistic regression model for NPDR risk prediction using routinely available clinical indicators. METHODS: A retrospective cohort of 421 T2DM patients from a single centre was divided into training (n = 295) and validation (n = 126) sets. Demographic, glycemic (fasting glucose, HbA1c), renal (UACR) and ophthalmologic (macular oedema) data were collected. Univariate and multivariate Logistic regression with stepwise selection identified independent predictors. Model performance was evaluated using area under the ROC curve (AUC), sensitivity, specificity and Hosmer-Lemeshow goodness-of-fit. Internal validation was performed via bootstrapping (1000 replicates), and external validation used an independent cohort. RESULTS: Four independent predictors were identified: macular oedema (OR = 3.247), fasting glucose (OR = 2.194), HbA1c (OR = 2.799) and UACR (OR = 1.153). The model demonstrated excellent discrimination in the training set (AUC = 0.949, sensitivity = 86.4%, specificity = 95.5%) and good calibration (H-L test, p = 0.358). Bootstrap validation confirmed stability of HbA1c and UACR. External validation yielded an AUC of 0.918, with a positive predictive value of 91.1% and maintained calibration (p = 0.282). CONCLUSIONS: The constructed Logistic regression model accurately predicts NPDR risk using four readily available clinical variables, offering high discriminative power, interpretability and clinical utility for stratifying high-risk T2DM patients in primary care settings.
Zhang et al. (Fri,) studied this question.