ArticleBMC ophthalmology2023
Dynamic nomogram prediction model for diabetic retinopathy in patients with type 2 diabetes mellitus.
Article in BMC ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk prediction models for diabetic retinopathy: a systematic review.Frontiers in endocrinology · 2025Pooled it
- A Novel Nomogram for Diabetic Retinopathy Prediction in Young and Middle-Aged Patients with Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Universal nomogram for predicting referable diabetic retinopathy: a validated model for community and ophthalmic outpatient populations using easily accessible indicators.Frontiers in endocrinology · 2025Article
- Development and Validation of a Nomogram-Based Risk Prediction Model for Diabetic Retinopathy in Elderly Adults with Type 2 Diabetes Mellitus.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- [Risk factors for ketoacidosis in children/adolescents with type 1 diabetes mellitus and establishment of a predictive model].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundTo develop a dynamic prediction model for diabetic retinopathy (DR) using systemic risk factors.
methodsThis retrospective study included type 2 diabetes mellitus (T2DM) patients discharged from the Second Affiliated Hospital of Kunming Medical University between May 2020 and February 2022. The early patients (80%) were used for the training set and the late ones (20%) for the validation set.
resultsFinally, 1257 patients (1049 [80%] in the training set and 208 [20%] in the validation set) were included; 360 (28.6%) of them had DR. The areas under the curves (AUCs) for the multivariate regression (MR), least absolute shrinkage and selection operator regression (LASSO), and backward elimination stepwise regression (BESR) models were 0.719, 0.727, and 0.728, respectively. The Delong test showed that the BESR model had a better predictive value than the MR (p = 0.04899) and LASSO (P = 0.04999) models. The DR nomogram risk model was established according to the BESR model, and it included disease duration, age at onset, treatment method, total cholesterol, urinary albumin to creatinine ratio (UACR), and urine sugar. The AUC, kappa coefficient, sensitivity, specificity, and compliance of the nomogram risk model in the validation set were 0.79, 0.48, 71.2%, 78.9%, and 76.4%, respectively.
conclusionsA relatively reliable DR nomogram risk model was established based on the BESR model.
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