ArticleScientific reports2023
Development and validation of a risk prediction model for diabetic retinopathy in type 2 diabetic patients.
Article in Scientific reports, 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, 16 citations in OpenAlex.
- Prediction model developed on the basis of meta-analysis in the field of medicine: a systematic survey and methodological summaries.BMC medical research methodology · 2025Pooled it
- Factors Influencing Non-Proliferative Retinopathy in Elderly Patients with T2DM: Metabolic-Behavioral Interaction Analysis.Risk management and healthcare policy · 2025Article
- Risk of Microvascular Complications in Newly Diagnosed Type 2 Diabetes Patients Using Automated Machine Learning Prediction Models.Journal of clinical medicine · 2024Article
- The causal effect of hypertension, intraocular pressure, and diabetic retinopathy: a Mendelian randomization study.Frontiers in endocrinology · 2024Article
- Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.Frontiers in endocrinology · 2024Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To establish a risk prediction model and make individualized assessment for the susceptible diabetic retinopathy (DR) population in type 2 diabetic mellitus (T2DM) patients. According to the retrieval strategy, inclusion and exclusion criteria, the relevant meta-analyses on DR risk factors were searched and evaluated. The pooled odds ratio (OR) or relative risk (RR) of each risk factor was obtained and calculated for β coefficients using logistic regression (LR) model. Besides, an electronic patient-reported outcome questionnaire was developed and 60 cases of DR and non-DR T2DM patients were investigated to validate the developed model. Receiver operating characteristic curve (ROC) was drawn to verify the prediction accuracy of the model. After retrieving, eight meta-analyses with a total of 15,654 cases and 12 risk factors associated with the onset of DR in T2DM, including weight loss surgery, myopia, lipid-lowing drugs, intensive glucose control, course of T2DM, glycated hemoglobin (HbA1c), fasting plasma glucose, hypertension, gender, insulin treatment, residence, and smoking were included for LR modeling. These factors, followed by the respective β coefficient was bariatric surgery (- 0.942), myopia (- 0.357), lipid-lowering drug follow-up < 3y (- 0.994), lipid-lowering drug follow-up > 3y (- 0.223), course of T2DM (0.174), HbA1c (0.372), fasting plasma glucose (0.223), insulin therapy (0.688), rural residence (0.199), smoking (- 0.083), hypertension (0.405), male (0.548), intensive glycemic control (- 0.400) with constant term α (- 0.949) in the constructed model. The area under receiver operating characteristic curve (AUC) of the model in the external validation was 0.912. An application was presented as an example of use. In conclusion, the risk prediction model of DR is developed, which makes individualized assessment for the susceptible DR population feasible and needs to be further verified with large sample size application.
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