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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

Manjula D. Nugawela, Sarega Gurudas, A. Toby Prevost, Rohini Mathur, John Robson, Thirunavukkarasu Sathish Orcid Logo, Jim Rafferty Orcid Logo, Ramachandran Rajalakshmi, Ranjit Mohan Anjana, Saravanan Jebarani, Viswanathan Mohan, David Owens Orcid Logo, Sobha Sivaprasad Orcid Logo

eClinicalMedicine, Volume: 51, Start page: 101578

Swansea University Authors: Jim Rafferty Orcid Logo, David Owens Orcid Logo

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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...

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Published in: eClinicalMedicine
ISSN: 2589-5370
Published: Elsevier BV 2022
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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 &#x2018;at-risk&#x2019; 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&#x2013;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&#xB7;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&#xB7;778 to 0&#xB7;832, and in the external validation datasets (C statistic 0&#xB7;685 &#x2013; 0&#xB7;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. 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spelling 2022-07-26T13:35:26.0343881 v2 60460 2022-07-12 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 52effe759a718bd36eb12cdd10fe1a09 0000-0002-1667-7265 Jim Rafferty Jim Rafferty true false 2fd4b7c3f82c6d3bd546eff61ff944e9 0000-0003-1002-1238 David Owens David Owens true false 2022-07-12 HDAT 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. Journal Article eClinicalMedicine 51 101578 Elsevier BV 2589-5370 Diabetic; Retinopathy; Predictive models; Performance; Diabetes; South Asians; India 1 9 2022 2022-09-01 10.1016/j.eclinm.2022.101578 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University 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. 2022-07-26T13:35:26.0343881 2022-07-12T13:59:09.5292778 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Manjula D. Nugawela 1 Sarega Gurudas 2 A. Toby Prevost 3 Rohini Mathur 4 John Robson 5 Thirunavukkarasu Sathish 0000-0002-2016-4964 6 Jim Rafferty 0000-0002-1667-7265 7 Ramachandran Rajalakshmi 8 Ranjit Mohan Anjana 9 Saravanan Jebarani 10 Viswanathan Mohan 11 David Owens 0000-0003-1002-1238 12 Sobha Sivaprasad 0000-0001-8952-0659 13 60460__24746__9058ff57e2d94b64bf7128e3fe5bf287.pdf 60460_VoR.pdf 2022-07-26T13:33:34.2944687 Output 1129013 application/pdf Version of Record true Copyright: 2022 The Author(s). This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
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
spellingShingle 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
Jim Rafferty
David Owens
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_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_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
author_id_str_mv 52effe759a718bd36eb12cdd10fe1a09
2fd4b7c3f82c6d3bd546eff61ff944e9
author_id_fullname_str_mv 52effe759a718bd36eb12cdd10fe1a09_***_Jim Rafferty
2fd4b7c3f82c6d3bd546eff61ff944e9_***_David Owens
author Jim Rafferty
David Owens
author2 Manjula D. Nugawela
Sarega Gurudas
A. Toby Prevost
Rohini Mathur
John Robson
Thirunavukkarasu Sathish
Jim Rafferty
Ramachandran Rajalakshmi
Ranjit Mohan Anjana
Saravanan Jebarani
Viswanathan Mohan
David Owens
Sobha Sivaprasad
format Journal article
container_title eClinicalMedicine
container_volume 51
container_start_page 101578
publishDate 2022
institution Swansea University
issn 2589-5370
doi_str_mv 10.1016/j.eclinm.2022.101578
publisher Elsevier BV
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
active_str 0
description 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.
published_date 2022-09-01T04:18:35Z
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