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, J. M. Rafferty, Ramachandran Rajalakshmi, Ranjit Mohan Anjana, Saravanan Jebarani, Viswanathan Mohan, David R. Owens, Sobha Sivaprasad

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Abstract

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.

Original languageEnglish
Article number101578
Number of pages13
JournaleClinicalMedicine
Volume51
DOIs
Publication statusPublished - 22 Jul 2022
Externally publishedYes

Bibliographical note

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Funder

SS reports personal fees from Novartis, personal fees from Bayer, grants from Boehringer Ingleheim, grants and personal fees from Allergan, personal fees from Oxurion, personal fees from Apellis, personal fees from Roche, outside the submitted work; ATP reports grants from UKRI to employer King's College London, during the conduct of the study; personal fees from Roche and a grant from Wellcome Trust, outside the submitted work. RM reports personal fees from AMGEN, outside the submitted work. None of the other authors declare that they have any competing interests related to the submitted work.

Funding Information:
This study was part of the ORNATE India project which was funded by the GCRF UKRI (MR/P207881/1). The research is supported by the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. AM is supported by the NIHR Applied Research Collaboration (ARC) NW London and is an NIHR Senior Investigator. The views expressed in this publication are those of the authors and not necessarily those of the NHS, NIHR or the Department of Health and Social Care. RM is supported by a Sir Henry Wellcome Postdoctoral fellowship from the Wellcome Trust (201375/Z/16/Z).

Keywords

  • Diabetes
  • Diabetic
  • India
  • Performance
  • Predictive models
  • Retinopathy
  • South Asians

ASJC Scopus subject areas

  • Medicine(all)

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