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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
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Jim Rafferty ,
Ramachandran Rajalakshmi,
Ranjit Mohan Anjana,
Saravanan Jebarani,
Viswanathan Mohan,
David Owens ,
Sobha Sivaprasad
eClinicalMedicine, Volume: 51, Start page: 101578
Swansea University Authors:
Jim Rafferty , David Owens
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Copyright: 2022 The Author(s). This is an open access article under the CC BY license
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DOI (Published version): 10.1016/j.eclinm.2022.101578
Abstract
BackgroundDelayed 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 a...
| Published in: | eClinicalMedicine |
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| ISSN: | 2589-5370 |
| Published: |
Elsevier BV
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa60460 |
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| Abstract: |
BackgroundDelayed 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.MethodsModels 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.FindingsA 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.InterpretationWe 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. |
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| Keywords: |
Diabetic; Retinopathy; Predictive models; Performance; Diabetes; South Asians; India |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
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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101578 |