Predictors for diabetic retinopathy progression-findings from nominal group technique and Evidence review

Predicting 1, 2 and 3 year emergent referable diabetic retinopathy and maculopathy using deep learning

BACKGROUND

Predicting diabetic retinopathy (DR) progression could enable individualised screening with prompt referral for high-risk individuals for sight-saving treatment, whilst reducing screening burden for low-risk individuals. We developed and validated deep learning systems (DLS) that predict 1, 2 and 3 year emergent referable DR and maculopathy using risk factor characteristics (tabular DLS), colour fundal photographs (image DLS) or both (multimodal DLS).

METHODS

From 162,339 development-set eyes from south-east London (UK) diabetic eye screening programme (DESP), 110,837 had eligible longitudinal data, with the remaining 51,502 used for pretraining. Internal and external (Birmingham DESP, UK) test datasets included 27,996, and 6928 eyes respectively.

RESULTS

Internal multimodal DLS emergent referable DR, maculopathy or either area-under-the receiver operating characteristic (AUROC) were 0.95 (95% CI: 0.92-0.98), 0.84 (0.82-0.86), 0.85 (0.83-0.87) for 1 year, 0.92 (0.87-0.96), 0.84 (0.82-0.87), 0.85 (0.82-0.87) for 2 years, and 0.85 (0.80-0.90), 0.79 (0.76-0.82), 0.79 (0.76-0.82) for 3 years. External multimodal DLS emergent referable DR, maculopathy or either AUROC were 0.93 (0.88-0.97), 0.85 (0.80-0.89), 0.85 (0.76-0.85) for 1 year, 0.93 (0.89-0.97), 0.79 (0.74-0.84), 0.80 (0.76-0.85) for 2 years, and 0.91 (0.84-0.98), 0.79 (0.74-0.83), 0.79 (0.74-0.84) for 3 years.

CONCLUSIONS

Multimodal and image DLS performance is significantly better than tabular DLS at all intervals. DLS accurately predict 1, 2 and 3 year emergent referable DR and referable maculopathy using colour fundal photographs, with additional risk factor characteristics conferring improvements in prognostic performance. Proposed DLS are a step towards individualised risk-based screening, whereby AI-assistance allows high-risk individuals to be closely monitored while reducing screening burden for low-risk individuals.

Diabetic retinopathy progression in patients under monitoring for treatment or vision loss: external validation and update of a multivariable prediction model

INTRODUCTION

The number of people with diabetes mellitus is increasing globally and consequently so too is diabetic retinopathy (DR). Most patients with diabetes are monitored through the diabetic eye screening programme (DESP) until they have signs of retinopathy and these changes progress, requiring referral into hospital eye services (HES). Here, they continue to be monitored until they require treatment. Due to current pressures on HES, delays can occur, leading to harm. There is a need to triage patients based on their individual risk. At present, patients are stratified according to retinopathy stage alone, yet other risk factors like glycated haemoglobin (HbA1c) may be useful. Therefore, a prediction model that combines multiple prognostic factors to predict progression will be useful for triage in this setting to improve care.We previously developed a Diabetic Retinopathy Progression model to Treatment or Vision Loss (DRPTVL-UK) using a large primary care database. The aim of the present study is to externally validate the DRPTVL-UK model in a secondary care setting, specifically in a population under care by HES. This study will also provide an opportunity to update the model by considering additional predictors not previously available.

METHODS AND ANALYSIS

We will use a retrospective cohort of 2400 patients with diabetes aged 12 years and over, referred from DESP to the NHS hospital trusts with referable DR between 2013 and 2016, with follow-up information recorded until December 2021.We will evaluate the external validity of the DRPTVL-UK model using measures of discrimination, calibration and net benefit. In addition, consensus meetings will be held to agree on acceptable risk thresholds for triage within the HES system.

ETHICS AND DISSEMINATION

This study was approved by REC (ref 22/SC/0425, 05/12/2022, Hampshire A Research Ethics Committee). The results of the study will be published in a peer-reviewed journal, presented at clinical conferences.

TRIAL REGISTRATION NUMBER

ISRCTN 10956293.

Visualizing the intellectual structure and recent research trends of diabetic retinopathy

AIM

To analyze the intellectual structure and recent research trends in diabetic retinopathy (DR) and unearth potential knowledge.

METHODS

English DR publication included in this study was exported from the Web of Science Core Collection, and Chinese DR publication was exported from China National Knowledge Infrastructure from the establishment time of the database to 2019. CiteSpace and Microsoft Excel were used to visually analyze DR research, including analysis of the number of publications, highly cited publication analysis, spatial distribution analysis, and keyword co-occurrence analysis.

RESULTS

A total of 23 795 English studies and 11 577 Chinese studies, including 2089 studies related to traditional Chinese medicine (TCM), were obtained. The data suggested the following: 1) The number of English and Chinese DR publications increased over time, and the growth rate of English publications was relatively fast. 2) The distribution of international scholars and institutions was close, while the distribution was scattered in China. Shanghai Jiao Tong University has the largest number of publications. Tien-Yin Wong was the core author with the largest number of publications. England and the United States are the core of international DR research cooperation. 3) Optical coherence tomography and risk factors are recent international research hot spots and trends. The difference is that TCM is a recent research trend under DR in China.

CONCLUSION

DR has drawn an increasing amount of attention worldwide. The focus of research in this field has shifted from tertiary type DR treatment to secondary prevention strategies which focus on the screening and monitoring of disease progression. The advantages of TCM in the prevention of DR have attracted attention, and it is worth incorporating this with Western medicine to address this challenge.