SynthesisDiabetologia2020
Prediction models for development of retinopathy in people with type 2 diabetes: systematic review and external validation in a Dutch primary care setting.
Synthesis in Diabetologia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Evaluation of Machine Learning Methods Developed for Prediction of Diabetes Complications: A Systematic Review.Journal of diabetes science and technology · 2023Pooled it
- A narrative review of what cohorts have taught us and how they have laid the foundation for much of our understanding of type 2 diabetes.Diabetologia · 2026Review
- Predicting acute and chronic diabetes complications among adults newly diagnosed with diabetes: development and validation of dynamic risk prediction models.Nature communications · 2026Article
- Cohort Profile: The North East London Diabetes Study (NELDS).International journal of epidemiology · 2026Article
- Plasma proteomic signatures of early retinal neurodegeneration in diabetes: A multi-cohort study.PLoS medicine · 2026Observational
- Machine Learning Analysis of Retrospective Data From 503 Hospitalized Older Patients With Type 2 Diabetes to Identify Factors Associated With Cognitive Impairment.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- A clinically interpretable machine learning model for early detection of diabetic retinopathy in multiple community health centers.Frontiers in endocrinology · 2026Article
- Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- Proteome atlas for mechanistic discovery and risk prediction of diabetic retinopathy.Nature communications · 2025Article
- RetinoDeep: Leveraging Deep Learning Models for Advanced Retinopathy Diagnostics.Sensors (Basel, Switzerland) · 2025Article
- From Ulcer to Amputation: A Systematic Review of Prognostic Models for Diabetic Foot Ulcer Amputation.Risk management and healthcare policy · 2025Review
- A Pilot Study on Predicting Ocular Hypertension in Chinese Patients Undergoing Femtosecond Laser-Assisted Penetrating Keratoplasty.International journal of medical sciences · 2025Article
- Retinal age gap as a predictive biomarker for future risk of clinically significant diabetic retinopathy.Acta diabetologica · 2024Article
- Cardiovascular risk in diabetes mellitus: epidemiology, assessment and prevention.Nature reviews. Cardiology · 2023Review
- Article
- A Prediction Model for Sight-Threatening Diabetic Retinopathy Based on Plasma Adipokines among Patients with Mild Diabetic Retinopathy.Journal of diabetes research · 2023Article
- A Nomogram for Predicting Vision-Threatening Diabetic Retinopathy Among Mild Diabetic Retinopathy Patients: A Case-Control and Prospective Study of Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Precision prognostics for the development of complications in diabetes.Diabetologia · 2022Review
- 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.EClinicalMedicine · 2022Article
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 1 country.
Funding
Abstract
aims/hypothesisThe aims of this study were to identify all published prognostic models predicting retinopathy risk applicable to people with type 2 diabetes, to assess their quality and accuracy, and to validate their predictive accuracy in a head-to-head comparison using an independent type 2 diabetes cohort.
methodsA systematic search was performed in PubMed and Embase in December 2019. Studies that met the following criteria were included: (1) the model was applicable in type 2 diabetes; (2) the outcome was retinopathy; and (3) follow-up was more than 1 year. Screening, data extraction (using the checklist for critical appraisal and data extraction for systemic reviews of prediction modelling studies [CHARMS]) and risk of bias assessment (by prediction model risk of bias assessment tool [PROBAST]) were performed independently by two reviewers. Selected models were externally validated in the large Hoorn Diabetes Care System (DCS) cohort in the Netherlands. Retinopathy risk was calculated using baseline data and compared with retinopathy incidence over 5 years. Calibration after intercept adjustment and discrimination (Harrell's C statistic) were assessed.
resultsTwelve studies were included in the systematic review, reporting on 16 models. Outcomes ranged from referable retinopathy to blindness. Discrimination was reported in seven studies with C statistics ranging from 0.55 (95% CI 0.54, 0.56) to 0.84 (95% CI 0.78, 0.88). Five studies reported on calibration. Eight models could be compared head-to-head in the DCS cohort (N = 10,715). Most of the models underestimated retinopathy risk. Validating the models against different severities of retinopathy, C statistics ranged from 0.51 (95% CI 0.49, 0.53) to 0.89 (95% CI 0.88, 0.91). CONCLUSIONS/
interpretationSeveral prognostic models can accurately predict retinopathy risk in a population-based type 2 diabetes cohort. Most of the models include easy-to-measure predictors enhancing their applicability. Tailoring retinopathy screening frequency based on accurate risk predictions may increase the efficiency and cost-effectiveness of diabetic retinopathy care. REGISTRATION: PROSPERO registration ID CRD42018089122.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.