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. (September 2022)

• Record Type: Journal Article
• Authors: Nugawela, Manjula D.; Gurudas, Sarega; Prevost, A. Toby; Mathur, Rohini; Robson, John; Sathish, Thirunavukkarasu; Rafferty, J.M.; Rajalakshmi, Ramachandran; Anjana, Ranjit Mohan; Jebarani, Saravanan; Mohan, Viswanathan; Owens, David R.; Sivaprasad, Sobha
• Is Part Of: EClinicalMedicine. Volume 51(2022)
• Publication Date: 2022-09
• Subjects: Diabetic -- Retinopathy -- Predictive models -- Performance -- Diabetes -- South Asians -- India; BMI Body mass index -- CCG Clinical Commissioning Group -- CI Confidence Interval -- CPRD Clinical Practice Research Datalink -- CVD Cardiovascular disease -- DR Diabetic Retinopathy -- GP General Practice -- HR Hazard ratio -- OR Odds ratio -- NHS National Health Service -- STDR Sight threatening diabetic retinopathy -- T2DM Type II diabetes mellitus -- UK United Kingdom; Medicine -- Research -- Periodicals; Medical policy -- Periodicals; Clinical Medicine; Health Policy; Public Health; Medical policy; Medicine -- Research; Periodical; Electronic journals; Periodicals; 613
• DOI: 10.1016/j.eclinm.2022.101578
• Languages: English
• ISSNs: 2589-5370

Abstract

Summary: 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.