Evaluating screening strategies for diabetic retinopathy

  • John Smith

Student thesis: Doctoral ThesisDoctor of Medicine


Programmes for diabetic retinopathy screening have increased the number of patients screened for sight threatening diabetic retinopathy (STDR) and permitted monitoring of people at risk of progression to referable diabetic retinopathy (RDR). A disparity between demand for screening and capacity to provide screening has arisen from the rapid increase in the prevalence of diabetes. Over 350,000 people have diabetes on the island of Ireland; most (~90%) have Type 2 diabetes (T2D). The current mode of universal annual DR screening is unsustainable. A possible solution would be to increase screening intervals for those at “low risk”. The means of reliable identification of those at low risk will require identification of the factors associated with disease progression to RDR in Ireland. In chapter 3 of this thesis, we identify the factors associated with progression to RDR in the form of weighted covariates. Chapter 4 herein describes a prospective external validation of pre-existent prediction models for stratifying risk of progression to RDR in people with T2D in Ireland. Inputting into these pre-existent prediction models developed in Iceland and Gloucester (England) involved taking a linear combination of the published coefficients from the models and the corresponding variables in our dataset at the last systemic visit before the index retinal screening. The accuracy of the models in predicting progression to RDR was compared with the outcomes observed in a prospective Irish cohort (n=939). Receiver operating characteristic (ROC) curves were used to assess models’ performance. Models developed in Iceland and Gloucester had an acceptable performance with an AUC of ~ 0.70 or above. This signifies that there would be a >70% probability that a randomly selected subject from the screening cohort who did in fact develop RDR would have been allocated to the higher risk score category by each of the models. This suggests relative safety in individualising intervals between screening episodes.
Date of AwardJul 2023
Original languageEnglish
Awarding Institution
  • Queen's University Belfast
SupervisorNoemi Lois (Supervisor), David Wright (Supervisor) & Peter Scanlon (Supervisor)


  • Screening
  • diabetic retinography
  • imaging technology
  • predication modelling

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