Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort
Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort
Background/Aims: to evaluate the performance of existing prediction models to determine risk of progression to referable diabetic retinopathy (RDR) using data from a prospective Irish cohort of people with type 2 diabetes (T2D).
Methods: a cohort of 939 people with T2D followed prospectively was used to test the performance of risk prediction models developed in Gloucester, UK, and Iceland. Observed risk of progression to RDR in the Irish cohort was compared with that derived from each of the prediction models evaluated. Receiver operating characteristic curves assessed models' performance.
Results: the cohort was followed for a total of 2929 person years during which 2906 screening episodes occurred. Among 939 individuals followed, there were 40 referrals (4%) for diabetic maculopathy, pre-proliferative DR and proliferative DR. The original Gloucester model, which includes results of two consecutive retinal screenings; a model incorporating, in addition, systemic biomarkers (HbA1c and serum cholesterol); and a model including results of one retinopathy screening, HbA1c, total cholesterol and duration of diabetes, had acceptable discriminatory power (area under the curve (AUC) of 0.69, 0.76 and 0.77, respectively). The Icelandic model, which combined retinopathy grading, duration and type of diabetes, HbA1c and systolic blood pressure, performed very similarly (AUC of 0.74).
Conclusion: in an Irish cohort of people with T2D, the prediction models tested had an acceptable performance identifying those at risk of progression to RDR. These risk models would be useful in establishing more personalised screening intervals for people with T2D.
eye (Globe), imaging, macula, retina, vision
1051-1056
Smith, John J.
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Wright, David M.
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Stratton, Irene M.
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Scanlon, Peter Henry
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Lois, Noemi
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Smith, John J.
8f980673-b037-4ae2-a9a2-84bf039beed0
Wright, David M.
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Stratton, Irene M.
772f25b9-23c0-4240-a3f6-1e76b03b172f
Scanlon, Peter Henry
4e3d2310-c79e-42db-ae29-7a7d6b278aa3
Lois, Noemi
bbac68dd-a228-47f1-91b8-b4b0340a2e7b
Smith, John J., Wright, David M., Stratton, Irene M., Scanlon, Peter Henry and Lois, Noemi
(2021)
Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort.
British Journal of Ophthalmology, 106 (8), .
(doi:10.1136/bjophthalmol-2020-318570).
Abstract
Background/Aims: to evaluate the performance of existing prediction models to determine risk of progression to referable diabetic retinopathy (RDR) using data from a prospective Irish cohort of people with type 2 diabetes (T2D).
Methods: a cohort of 939 people with T2D followed prospectively was used to test the performance of risk prediction models developed in Gloucester, UK, and Iceland. Observed risk of progression to RDR in the Irish cohort was compared with that derived from each of the prediction models evaluated. Receiver operating characteristic curves assessed models' performance.
Results: the cohort was followed for a total of 2929 person years during which 2906 screening episodes occurred. Among 939 individuals followed, there were 40 referrals (4%) for diabetic maculopathy, pre-proliferative DR and proliferative DR. The original Gloucester model, which includes results of two consecutive retinal screenings; a model incorporating, in addition, systemic biomarkers (HbA1c and serum cholesterol); and a model including results of one retinopathy screening, HbA1c, total cholesterol and duration of diabetes, had acceptable discriminatory power (area under the curve (AUC) of 0.69, 0.76 and 0.77, respectively). The Icelandic model, which combined retinopathy grading, duration and type of diabetes, HbA1c and systolic blood pressure, performed very similarly (AUC of 0.74).
Conclusion: in an Irish cohort of people with T2D, the prediction models tested had an acceptable performance identifying those at risk of progression to RDR. These risk models would be useful in establishing more personalised screening intervals for people with T2D.
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Accepted/In Press date: 1 February 2021
e-pub ahead of print date: 21 April 2021
Additional Information:
Funding Information:
Funding This study was partially funded under a Medical Research Council, Health Data Research UK Innovation Fellowship to DW (MR/S003770/1).
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Keywords:
eye (Globe), imaging, macula, retina, vision
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Local EPrints ID: 487196
URI: http://eprints.soton.ac.uk/id/eprint/487196
ISSN: 0007-1161
PURE UUID: 2680a6a1-058d-4d19-9d0e-553a317b58f6
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Date deposited: 16 Feb 2024 10:29
Last modified: 18 Mar 2024 04:01
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Author:
John J. Smith
Author:
Irene M. Stratton
Author:
Peter Henry Scanlon
Author:
Noemi Lois
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