June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
A novel multivariate discriminant approach to predict sight threatening diabetic retinopathy (STDR) cases – data from the Liverpool Diabetic Eye Study
Author Affiliations & Notes
  • Marta Garcia-Finana
    Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
  • David M Hughes
    Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
  • Christopher P Cheyne
    Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom
  • Deborah M Broadbent
    St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, United Kingdom
  • Amu Wang
    Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
  • Mehrdad Mobayen-Rahni
    Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
    Medical Physics and Clinical Engineering, Royal Liverpool University Hospital, Liverpool, United Kingdom
  • Ayesh Alshukri
    Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
  • Irene M Stratton
    Gloucestershire Retinal Research Group, Cheltenham General Hospital, Cheltenham, United Kingdom
  • Anthony C Fisher
    Medical Physics and Clinical Engineering, Royal Liverpool University Hospital, Liverpool, United Kingdom
  • Jiten P Vora
    Diabetes and Endocrinology, Royal Liverpool University Hospital, Liverpool, United Kingdom
  • Simon P Harding
    St Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, United Kingdom
    Eye and Vision Science, University of Liverpool, Liverpool, United Kingdom
  • Footnotes
    Commercial Relationships   Marta Garcia-Finana, None; David Hughes, None; Christopher Cheyne, None; Deborah Broadbent, None; Amu Wang, None; Mehrdad Mobayen-Rahni, None; Ayesh Alshukri, None; Irene Stratton, None; Anthony Fisher, None; Jiten Vora, None; Simon Harding, None
  • Footnotes
    Support  MGF, DMH and SPH acknowledge support from the Medical Research Council (Research project MR/L010909/1). This abstract presents independent research funded by the National Institute for Health Research (RP-PG-1210-12016). The views expressed are those of the authors, not those of the NHS, NIHR or Department of Health
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 4288. doi:
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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.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To identify patients developing STDR over a one year period using the patients’ demographic, clinical and retinopathy data collected over time.

Methods : 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.

Results : 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).

Conclusions : 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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