AIMS: To evaluate our proposed multivariate approach to identify patients who will develop sight-threatening diabetic retinopathy (STDR) within a 1-year screen interval, and explore the impact of simple stratification rules on prediction. MATERIALS AND METHODS: A 7-year dataset (2009-2016) from people with diabetes (PWD) was analysed using a novel multivariate longitudinal discriminant approach. Level of diabetic retinopathy, assessed from routine digital screening photographs of both eyes, was jointly modelled using clinical data collected over time. Simple stratification rules based on retinopathy level were also applied and compared with the multivariate discriminant approach. RESULTS: Data from 13 103 PWD (49 520 screening episodes) were analysed. The multivariate approach accurately predicted whether patients developed STDR or not within 1 year from the time of prediction in 84.0% of patients (95% confidence interval [CI] 80.4-89.7), compared with 56.7% (95% CI 55.5-58.0) and 79.7% (95% CI 78.8-80.6) achieved by the two stratification rules. While the stratification rules detected up to 95.2% (95% CI 92.2-97.6) of the STDR cases (sensitivity) only 55.6% (95% CI 54.5-56.7) of patients who did not develop STDR were correctly identified (specificity), compared with 85.4% (95% CI 80.4-89.7%) and 84.0% (95% CI 80.7-87.6%), respectively, achieved by the multivariate risk model. CONCLUSIONS: Accurate prediction of progression to STDR in PWD can be achieved using a multivariate risk model whilst also maintaining desirable specificity. While simple stratification rules can achieve good levels of sensitivity, the present study indicates that their lower specificity (high false-positive rate) would therefore necessitate a greater frequency of eye examinations.

Department of Biostatistics Institute of Translational Medicine University of Liverpool Liverpool UK

Department of Eye and Vision Science Institute of Ageing and Chronic Disease University of Liverpool Liverpool UK

Department of Eye and Vision Science Institute of Ageing and Chronic Disease University of Liverpool Liverpool UK Department of Medical Physics and Clinical Engineering Royal Liverpool University Hospital Liverpool UK

Department of Eye and Vision Science Institute of Ageing and Chronic Disease University of Liverpool Liverpool UK St Paul's Eye Unit Royal Liverpool University Hospital Liverpool UK

Department of Probability and Mathematical Statistics Faculty of Mathematics and Physics Charles University Prague Czech Republic

Diabetes and Endocrinology Royal Liverpool University Hospital Liverpool UK

Gloucestershire Retinal Research Group Gloucestershire Hospitals NHS Foundation Trust Cheltenham General Hospital Cheltenham UK

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