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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

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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310126/
https://www.ncbi.nlm.nih.gov/pubmed/35898318
http://dx.doi.org/10.1016/j.eclinm.2022.101578
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author 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
author_facet 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
author_sort Nugawela, Manjula D.
collection PubMed
description 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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spelling pubmed-93101262022-07-26 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 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 eClinicalMedicine Articles 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. Elsevier 2022-07-22 /pmc/articles/PMC9310126/ /pubmed/35898318 http://dx.doi.org/10.1016/j.eclinm.2022.101578 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
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
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9310126/
https://www.ncbi.nlm.nih.gov/pubmed/35898318
http://dx.doi.org/10.1016/j.eclinm.2022.101578
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