ArticleEClinicalMedicine2022
Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings.
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- Risk prediction models for diabetic retinopathy: a systematic review.Frontiers in endocrinology · 2025Pooled it
- Predicting vision-threatening diabetic retinopathy in patients with type 2 diabetes mellitus: Systematic review, meta-analysis, and prospective validation study.Journal of global health · 2024Pooled it
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- From Science and Technology to Eye-Health Policy: A Review on Global and Asia-Pacific Strategies on the Prevention of Blindness from Retinal Diseases.Ophthalmology and therapy · 2026Review
- An interpretable machine learning model for detecting vision-threatening diabetic retinopathy among patients with diabetic retinopathy: a web-based cross-sectional study.Frontiers in endocrinology · 2026Article
- Hormonal metabolic burden in diabetic retinopathy: the diabetic retina as an endocrine-responsive target organ.Frontiers in endocrinology · 2026Review
- Article
- Diabetic retinal disease.Nature reviews. Disease primers · 2025Review
- Artificial intelligence technology in ophthalmology public health: current applications and future directions.Frontiers in cell and developmental biology · 2025Review
- Development and validation of prediction models for diabetic retinopathy in type 2 diabetes patients.PloS one · 2025Article
- Red blood cell count and its inverse association with diabetic retinopathy: Exploratory development of a risk assessment model in a retrospective cohort.Frontiers in endocrinology · 2025Article
- Reduced Aqueous Retinol-Binding Protein 3 Concentration Is Associated With Diabetic Macular Edema and Progression of Diabetic Retinopathy.Diabetes care · 2025Article
- Health transition and eye care policy planning for people with diabetic retinopathy in south Asia.The Lancet regional health. Southeast Asia · 2024Review
- Association of retinopathy with risk of all-cause and specific-cause mortality in the National Health and Nutrition Examination Survey, 2005 to 2008.Frontiers in public health · 2023Article
- Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study.Frontiers in endocrinology · 2022Article
Corrections and comments
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Authors and funding
13 authors at 8 institutions in 3 countries.
Funding
Abstract
Background: Delayed diagnosis and treatment of sight threatening diabetic retinopathy (STDR) is a common cause of visual impairment in people with Type 2 diabetes. Therefore, systematic regular retinal screening is recommended, but global coverage of such services is challenging. We aimed to develop and validate predictive models for STDR to identify 'at-risk' population for retinal screening. Methods: Models were developed using datasets obtained from general practices in inner London, United Kingdom (UK) on adults with type 2 Diabetes during the period 2007-2017. Three models were developed using Cox regression and model performance was assessed using C statistic, calibration slope and observed to expected ratio measures. Models were externally validated in cohorts from Wales, UK and India. Findings: A total of 40,334 people were included in the model development phase of which 1427 (3·54%) people developed STDR. Age, gender, diabetes duration, antidiabetic medication history, glycated haemoglobin (HbA1c), and history of retinopathy were included as predictors in the Model 1, Model 2 excluded retinopathy status, and Model 3 further excluded HbA1c. All three models attained strong discrimination performance in the model development dataset with C statistics ranging from 0·778 to 0·832, and in the external validation datasets (C statistic 0·685 - 0·823) with calibration slopes closer to 1 following re-calibration of the baseline survival. Interpretation: We have developed new risk prediction equations to identify those at risk of STDR in people with type 2 diabetes in any resource-setting so that they can be screened and treated early. Future testing, and piloting is required before implementation. Funding: This study was funded by the GCRF UKRI (MR/P207881/1) and supported by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.
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