Delay in diabetic retinopathy screening increases the rate of detection of referable diabetic retinopathy.

Diabet Med

Gloucestershire Diabetic Retinopathy Research Group, Cheltenham General Hospital, Cheltenham, UK; English NHS Diabetic Eye Screening Programme, Gloucester, UK.

Published: April 2014

Aims: To assess whether there is a relationship between delay in retinopathy screening after diagnosis of type 2 diabetes and level of retinopathy detected.

Methods: Patients were referred from 88 primary care practices to an English National Health Service diabetic eye screening programme. Data for screened patients were extracted from the primary care databases using semi-automated data collection algorithms supplemented by validation processes. The programme uses two-field mydriatic digital photographs graded by a quality assured team.

Results: Data were available for 8183 screened patients with diabetes newly diagnosed in 2005, 2006 or 2007. Only 163 with type 1 diabetes were identified and were insufficient for analysis. Data were available for 8020 with newly diagnosed type 2 diabetes. Of these, 3569 were screened within 6 months, 2361 between 6 and 11 months, 1058 between 12 and 17 months, 366 between 18 and 23 months, 428 between 24 and 35 months, and 238 at 3 years or more after diagnosis. There were 5416 (67.5%) graded with no retinopathy, 1629 (20.3%) with background retinopathy in one eye, 753 (9.4%) with background retinopathy in both eyes and 222 (2.8%) had referable diabetic retinopathy. There was a significant trend (P = 0.0004) relating time from diagnosis to screening detecting worsening retinopathy. Of those screened within 6 months of diagnosis, 2.3% had referable retinopathy and, 3 years or more after diagnosis, 4.2% had referable retinopathy.

Conclusions: The rate of detection of referable diabetic retinopathy is elevated in those who were not screened promptly after diagnosis of type 2 diabetes.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4232880PMC
http://dx.doi.org/10.1111/dme.12313DOI Listing

Publication Analysis

Top Keywords

diabetic retinopathy
16
type diabetes
16
referable diabetic
12
retinopathy
11
retinopathy screening
8
rate detection
8
detection referable
8
diagnosis type
8
primary care
8
screened patients
8

Similar Publications

Innovations in diabetic retinopathy screening in the UK.

Lancet Diabetes Endocrinol

January 2025

NIHR Moorfields Biomedical Research Centre, Medical Retina, Moorfields Eye Hospital, London, EC1V 2PD, UK. Electronic address:

View Article and Find Full Text PDF

To describe the demographic and clinical characteristics of patients with Charcot neuro-osteoarthropathy (CNO) and to examine for differences between participants with Type 1 diabetes mellitus (DM) (T1DM) and Type 2 diabetes mellitus (T2DM). Multicenter observational study in eight diabetic foot clinics in six countries between January 1, 1996, and December 31, 2022. Demographic, clinical, and laboratory parameters were obtained from the medical records.

View Article and Find Full Text PDF

Background: The prevalence of diabetes is escalating globally, underscoring the need for comprehensive evidence to inform health systems in effectively addressing this epidemic. The purpose of this study was to examine the patterns of countries' capacity to manage diabetes using latent class analysis (LCA) and to determine whether the patterns are associated with diabetes-related deaths and healthcare costs.

Methods: Eight indicators of country-level capacity were drawn from the World Health Organization Global Health Observatory dataset: the widespread availability of hemoglobin A1C (HbA1c) testing, existence of diabetes registry, national diabetes management guidelines, national strategy for diabetes care, blood glucose testing, diabetic retinopathy screening, sulfonylureas, and metformin in the public health sector.

View Article and Find Full Text PDF

Background/aims: Large language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building, has yet to be studied in detail.

Methods: We introduced two LLM-based approaches of systematic review: an LLM-enabled fully automated approach (LLM-FA) utilising three different GPT-4 plugins (Consensus GPT, Scholar GPT and GPT web browsing modes) and an LLM-facilitated semi-automated approach (LLM-SA) using GPT4's Application Programming Interface (API).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!