• Amber A van der Heijden, Giel Nijpels, Fariza Badloe, Heidi L Lovejoy, Linda M Peelen, Talitha L Feenstra, Moons Karel G M KGM Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands., Roderick C Slieker, Herings Ron M C RMC Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, Amsterdam, the Netherlands. , Elders Petra J M PJM Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, van der Boechors, and Joline W Beulens.
• Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, van der Boechorststraat 7, 1081 BT, Amsterdam, the Netherlands. a.vanderheijden@amsterdamumc.nl.
• Diabetologia. 2020 Jun 1; 63 (6): 1110-1119.

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.RegistrationPROSPERO registration ID CRD42018089122.

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