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Marta Garcia-Finana, David M Hughes, Christopher P Cheyne, Deborah M Broadbent, Amu Wang, Mehrdad Mobayen-Rahni, Ayesh Alshukri, Irene M Stratton, Anthony C Fisher, Jiten P Vora, Simon P Harding; A novel multivariate discriminant approach to predict sight threatening diabetic retinopathy (STDR) cases – data from the Liverpool Diabetic Eye Study. Invest. Ophthalmol. Vis. Sci. 2017;58(8):4288. doi: https://doi.org/.
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© ARVO (1962-2015); The Authors (2016-present)
To identify patients developing STDR over a one year period using the patients’ demographic, clinical and retinopathy data collected over time.
Data from 13,775 people with diabetes registered in Liverpool between 2009 and 2016 were analysed using a novel multivariate discriminant approach. The dataset included: attendance to screening for DR, level of retinopathy assessed from digital photographs (41,350 screening episodes) as well as chronological primary care demographic and biochemical data.
Retinopathy grade at first screening visit was distributed as follows: 63.8% No DR, 25.8% mild non-proliferative (NP) DR in one eye and 10.4% mild NPDR in both eyes. Level of retinopathy showed a gradual increase in risk of STDR across the 3 DR stages: 0.1% for no DR in either eye, 2.1% for mild NP in one eye and 18.1% for mild NP in both eyes. Duration of diabetes and HbA1C (median, IQR) were respectively (4.8, 2.3-9.4) years and (65, 53-87) mmol/mol in the STDR group, and (1.8, 0.3-4.6) years and (50, 44-60) mmol/mol in the no STDR group. Higher rates (difference in %) of appointments missed (20.3%), type I diabetes (15.8%) and males (6.6%) were observed in the STDR group.Our predictive model achieved high levels of accuracy: 87.7% of all STDR cases (423 cases) and 83.2% of all no STDR cases were correctly predicted (sensitivity and specificity, respectively); the AUC of the ROC was 89.8% (95%CI: 89.5-90.1%). Cross-validation was conducted with 70% of the data for training and 30% for testing (process repeated 100 times).
Clinical measurements collected over time and baseline data can be jointly modelled to effectively identify patients who develop STDR within a year.
This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.
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