Individualised risk assessment for diabetic retinopathy and optimisation of screening intervals: a scientific approach to reducing healthcare costs

Comparison of diabetic retinopathy screening between hospital-based multidisciplinary and general practice-based settings: insights from a regional study in Italy

PURPOSE

To compare diabetic retinopathy screening among patients with type 1 or type 2 diabetes under care in two distinct setups: hospital-based multidisciplinary and general practice-based.

MATERIALS AND METHODS

In this retrospective observational case series, we collected data from a total of 133 diabetic patients: subjects from the hospital-based multidisciplinary setting were referred by the diabetologist and screened by an ophthalmologist using the Optomed Aurora IQ fundus camera. These patients were compared with those who underwent DR screening arranged through a general practice-based setting.

RESULTS

The proportion of patients treated with insulin was higher in the hospital-based multidisciplinary group, both considering the totality patients and those affected by type 2 diabetes (71.6% vs. 32.2%; p < 0.001, and 58.8% vs. 31.0%; p = 0.004 respectively). Patients from the hospital-based multidisciplinary group had a longer mean diabetes duration (19.6 vs 14.9 years, p < 0.001), underwent DR screening more frequently in the previous three years (2.9 vs 1.4, p < 0.001), the mean time between two DR screenings was shorter (14.6 vs 77.9 weeks, p < 0.001), and DR was detected more frequently (32,4% vs 13.5%; p = 0.011).

CONCLUSION

We were able to demonstrate that patients screened in the multidisciplinary center, which had characteristics predisposing to a higher risk of DR, were more likely to be diagnosed with DR on time, with a higher mean number of DR screenings and a shorted interval between diabetic and ophthalmological assessments.

Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up

(1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92-0.94) for any DR and 0.90 (95% CI, 0.89-0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient.

Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study

BACKGROUND

The Diabetic Retinopathy Extended Screening Study (DRESS) aims to develop and validate a new DR/diabetic macular edema (DME) risk stratification model in patients with Type 2 diabetes (DM) to identify low-risk groups who can be safely assigned to biennial or triennial screening intervals. We describe the study methodology, participants' baseline characteristics, and preliminary DR progression rates at the first annual follow-up.

METHODS

DRESS is a 3-year ongoing longitudinal study of patients with T2DM and no or mild non-proliferative DR (NPDR, non-referable) who underwent teleophthalmic screening under the Singapore integrated Diabetic Retinopathy Programme (SiDRP) at four SingHealth Polyclinics. Patients with referable DR/DME (> mild NPDR) or ungradable fundus images were excluded. Sociodemographic, lifestyle, medical and clinical information was obtained from medical records and interviewer-administered questionnaires at baseline. These data are extracted from medical records at 12, 24 and 36 months post-enrollment. Baseline descriptive characteristics stratified by DR severity at baseline and rates of progression to referable DR at 12-month follow-up were calculated.

RESULTS

Of 5,840 eligible patients, 78.3% (n = 4,570, median [interquartile range [IQR] age 61.0 [55-67] years; 54.7% male; 68.0% Chinese) completed the baseline assessment. At baseline, 97.4% and 2.6% had none and mild NPDR (worse eye), respectively. Most participants had hypertension (79.2%) and dyslipidemia (92.8%); and almost half were obese (43.4%, BMI ≥ 27.5 kg/m2). Participants without DR (vs mild DR) reported shorter DM duration, and had lower haemoglobin A1c, triglycerides and urine albumin/creatinine ratio (all p < 0.05). To date, we have extracted 41.8% (n = 1909) of the 12-month follow-up data. Of these, 99.7% (n = 1,904) did not progress to referable DR. Those who progressed to referable DR status (0.3%) had no DR at baseline.

CONCLUSIONS

In our prospective study of patients with T2DM and non-referable DR attending polyclinics, we found extremely low annual DR progression rates. These preliminary results suggest that extending screening intervals beyond 12 months may be viable and safe for most participants, although our 3-year follow up data are needed to substantiate this claim and develop the risk stratification model to identify low-risk patients with T2DM who can be assigned biennial or triennial screening intervals.

A deep learning system for predicting time to progression of diabetic retinopathy

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

A systematic review of diabetic retinopathy screening intervals

The current evidence on whether annual diabetic retinopathy (DR) screening intervals can be extended was reviewed. A systematic review protocol was followed (PROSPERO ID: CRD42022359590). Original longitudinal articles that specifically assessed DR screening intervals were in English and collected data after 2000 were included. Two reviewers independently conducted the search and reviewed the articles for quality and relevant information. The heterogeneity of the data meant that a meta-analysis was not appropriate. Twelve publications were included. Studies were of good quality and many used data from DR screening programs. Studies fit into three categories; those that assessed specific DR screening intervals, those that determined optimal DR screening intervals and those that developed/assessed DR screening risk equations. For those with type 2 diabetes, extending screening intervals to 3- to 4-yearly in those with no baseline DR appeared safe. DR risk equations considered clinical factors and allocated those at lower risk of DR progression screening intervals of up to five years. Those with baseline DR or type 1 diabetes appeared to have a higher risk of progression to STDR and needed more frequent screening. DR screening intervals can be extended to 3-5 yearly in certain circumstances. These include patients with type 2 diabetes and no current DR, and those who have optimal management of other risk factors such as glucose and blood pressure.

A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence

Diabetic retinopathy (DR) is vision impairment and a life-threatening condition for diabetic patients. Especially type II diabetic people have higher chances of getting retinal problems. Hence, early prediction of DR is necessary for preventing the diabetic patients from vision impairment. The main aim of this feasibility study is to identify the most critical risk features that could lead to diabetic retinopathy. This study investigated type II diabetic patients' socio-analytical, diabetes, behavioral, and clinical risk factors. We conducted a self-individual questionnaire session for all participants. Our questionnaire asked about the reliability of results, feeling comfortable during the screening test, willingness to participate in future screenings, overall perspective, and satisfaction with the DR screening test. We proposed a random forest model for predicting the prevalence of DR risk among diabetics. Further explanations of the model were conducted using more robust SHAP eXplainable Artificial Intelligence (XAI) tools. The SHAP method makes it possible to understand how input variables interact with their representative output records, as well as how input variables are ranked. In addition, various descriptive and inferential statistical analyses were performed on the data and evaluated the significant relationship between the factors discussed above via hypothesis testing. This feasibility study involved 172 type II diabetic patients (73 males and 99 females). Therefore, we found that 81 (47.09%) out of 172 participants had referable DR. The average age of the patients was determined as 55.08, with a standard deviation of ± 9.770 (ranging from 40 to 79). Type II patients were affected by mild, moderate, severe, and advanced proliferative diabetic retinopathy (PDR) stages with 23.83%, 13.95%, 5.81%, and 3.48%, respectively, of the total samples. The developed RF model obtained high accuracy of 94.9% using clinical dataset. Our results showed that the formation of tiny microminiature lesions was noticeable in type II diabetic patients with aged people, abnormal blood glucose levels, and prolonged diabetes duration.

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.

Behavioural Change Techniques in Health Coaching-Based Interventions for Type 2 Diabetes: A Systematic Review and Meta-Analysis

BACKGROUND

Given the high rates globally of Type 2 Diabetes Mellitus (T2DM), there is a clear need to target health behaviours through person-centred interventions. Health coaching is one strategy that has been widely recognised as a tool to foster positive behaviour change. However, it has been used inconsistently and has produced mixed results. This systematic review sought to explore the use of behaviour change techniques (BCTs) in health coaching interventions and identify which BCTs are linked with increased effectiveness in relation to HbA1C reductions.

METHODS

In line with the PICO framework, the review focused on people with T2DM, who received health coaching and were compared with a usual care or active control group on HbA1c levels. Studies were systematically identified through different databases including Medline, Web of science, and PsycINFO searches for relevant randomised controlled trials (RCTs) in papers published between January 1950 and April 2022. The Cochrane collaboration tool was used to evaluate the quality of the studies. Included papers were screened on the reported use of BCTs based on the BCT taxonomy. The effect sizes obtained in included interventions were assessed by using Cohen's d and meta-analysis was used to estimate sample-weighted average effect sizes (Hedges' g).

RESULTS

Twenty RCTs with a total sample size of 3222 were identified. Random effects meta-analysis estimated a small-sized statistically significant effect of health coaching interventions on HbA1c reduction (g₊ = 0.29, 95% CI: 0.18 to 0.40). A clinically significant HbA1c decrease of ≥5 mmol/mol was seen in eight studies. Twenty-three unique BCTs were identified in the reported interventions, with a mean of 4.5 (SD = 2.4) BCTs used in each study. Of these, Goal setting (behaviour) and Problem solving were the most frequently identified BCTs. The number of BCTs used was not related to intervention effectiveness. In addition, there was little evidence to link the use of specific BCTs to larger reductions in HbA1c across the studies included in the review; instead, the use of Credible source and Social reward in interventions were associated with smaller reductions in HbA1c.

CONCLUSION

A relatively small number of BCTs have been used in RCTs of health coaching interventions for T2DM. Inadequate, imprecise descriptions of interventions and the lack of theory were the main limitations of the studies included in this review. Moreover, other possible BCTs directly related to the theoretical underpinnings of health coaching were absent. It is recommended that key BCTs are identified at an early stage of intervention development, although further research is needed to examine the most effective BCTs to use in health coaching interventions.

TRIAL REGISTRATION

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021228567 .

Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage

Background and Objectives: The present study evaluated the detection of diabetic retinopathy (DR) using an automated fundus camera focusing exclusively on retinal hemorrhage (RH) using a deep convolutional neural network, which is a machine-learning technology. Materials and Methods: This investigation was conducted via a prospective and observational study. The study included 89 fundus ophthalmoscopy images. Seventy images passed an image quality review and were graded as showing no apparent DR (n = 51), mild nonproliferative DR (NPDR; n = 16), moderate NPDR (n = 1), severe NPDR (n = 1), and proliferative DR (n = 1) by three retinal experts according to the International Clinical Diabetic Retinopathy Severity scale. The RH numbers and areas were automatically detected and the results of two tests—the detection of mild-or-worse NPDR and the detection of moderate-or-worse NPDR—were examined. Results: The detection of mild-or-worse DR showed a sensitivity of 0.812 (95% confidence interval: 0.680–0.945), specificity of 0.888, and area under the curve (AUC) of 0.884, whereas the detection of moderate-or-worse DR showed a sensitivity of 1.0, specificity of 1.0, and AUC of 1.0. Conclusions: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention.

Assessing the Feasibility and Acceptability of Health Coaching as a New Diabetes Management Approach for the People with Type 2 Diabetes in Saudi Arabia: A Protocol for a Mixed Methods Feasibility Study

BACKGROUND

Over recent years, the Middle East, and especially Saudi Arabia, has faced multiple changes, including structural-demographic and economic shifts. This has led to massive changes in the population's lifestyle, including more unhealthy diets and increases in physical inactivity. As a result, accelerating rates of chronic diseases, including type 2 diabetes mellitus (T2DM) are a major public health concern. Current diabetes care in Saudi Arabia focuses on increasing the awareness of patients through various approaches, mainly based on health education, which is found to be suboptimal and ineffective for improving long-term outcomes. This study aims to assess the feasibility and acceptability of using a client-centred approach called health coaching that supports, enables, and engages T2DM patients to take the central role of controlling their own conditions by developing new crucial skills.

METHODS

A mixed methods randomised controlled feasibility study of health coaching will be used. Participants (n = 30) are adults with T2DM with poorly controlled diabetes (A1C ≥7) who can read and write in Arabic. Eligible participants are randomly allocated to either an intervention or control group for 12 weeks. COM-B model and Behaviour Change Technique Taxonomy version 1 (BCTTv1) guide the intervention curriculum. Predetermined progression criteria will be used to determine whether to proceed to a larger trial or not. Outcomes will be measured at baseline and 3 months. The study's primary aim is to assess the process of eligibility, recruitment, retention and completion rates, acceptability and suitability of intervention and the time to complete each procedure. The preliminary efficacy of health coaching is the secondary outcome that includes different measurements, such as HbA1c, blood pressure, body mass index (BMI), waist circumference, weight, patients' self-efficacy, and diabetes self-management.

DISCUSSION

This is the first study to explore the feasibility, acceptability, and preliminary efficacy of health coaching that used the Capability, Opportunity, Motivation, Behaviour (COM-B) model and BCTTv1 as guidance to develop the intervention for adults with T2DM in Saudi Arabia. The findings of this study will be used to inform the larger RCT trial if it is shown to be feasible and acceptable.

American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update

OBJECTIVE

The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers.

METHODS

The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development.

RESULTS

This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes.

CONCLUSIONS

This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.

External Validation and Clinical Applicability of Two Optical Coherence Tomography-Based Risk Calculators for Detecting Glaucoma

Purpose

To clinically validate the diagnostic ability of two optical coherence tomography (OCT)-based glaucoma diagnostic calculators (GDCs).

Methods

We conducted a retrospective, consecutive sampling of 76 patients with primary open-angle glaucoma, 107 glaucoma suspects, and 67 controls. Demographics, reliable visual field testing, and macular and optic disc OCT were collected. The reference diagnosis was compared against the probability of having glaucoma obtained from two GDCs derived from multivariate logistic regressions using quantitative and qualitative (GDC1) or only quantitative (GDC2) OCT data. The discrimination (area under the curve [AUC]) and calibration (calibration plots) were compared for both calculators and the best OCT parameters.

Results

GDC2 was able to identify 46.9% more suspects and 14.7% more glaucomatous eyes than GDC1. Both GDCs obtained the highest discriminative ability in glaucomatous eyes (GDC1 AUC = 0.949; GDC2 = 0.943 vs inferior peripapillary retinal nerve fiber layer [pRNFL] = 0.931; P = 0.43). The discriminating ability was not as good for glaucoma suspects, but the GDCs were not inferior to pRNFL (GDC 1 AUC = 0.739; GDC2 = 0.730; inferior pRNFL = 0.760; P = 0.54) and GDC2 was still able to correctly identify up to 30.8% more cases than the conventional OCT classification. Calibration showed risk underestimation for both groups and calculators, but it was better in GDC2 and in patients with glaucoma.

Conclusions

OCT-based calculators showed an excellent diagnostic performance in glaucomatous eyes. GDC2 was able to identify approximately 30% more cases than the conventional pRNFL inferior OCT classification in both groups, suggesting a potential role of these composite scores in clinical practice.

Translational Relevance

These OCT-based calculators may improve glaucoma diagnosis in clinical care.

Improving the screening of risk factors in diabetic retinopathy

INTRODUCTION

In 2002, Diabetic Retinopathy was reported as the leading cause of blindness in the working age group. The introduction of systematic screening programs in the UK has reduced visual loss and blindness due to diabetic retinopathy, but it does still occur with catastrophic consequences for the individual.

AREAS COVERED

The author conducted an ongoing search for articles relating to diabetic retinopathy since 2000 utilizing Zetoc Alert with keywords and contents page lists from relevant journals. This review covers the risk factors for loss of vision due to diabetic retinopathy and discusses ways in which the awareness of these risk factors can be used to further reduce visual loss. Some risk factors such as glycemic and B/P control are well known from landmark trials. This review has included these factors but concentrated more on the evidence behind those risk factors that are not so clearly defined or so well known.

EXPERT OPINION

The major risk factors are well known, but one continues to find that people with diabetes lose vision in situations in which a better awareness of the risks by both the individual with diabetes and the health workers involved may have prevented the visual loss.

Retinopathy risk calculators in the prediction of sight-threatening diabetic retinopathy in type 2 diabetes: A FIELD substudy

AIMS

To evaluate the risk algorithm by Aspelund et al. for predicting sight-threatening diabetic retinopathy (STDR) in Type 2 diabetes (T2D), and to develop a new STDR prediction model.

METHODS

The Aspelund et al. algorithm was used to calculate STDR risk from baseline variables in 1012 participants in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) ophthalmological substudy, compared to on-trial STDR status, and receiver operating characteristic analysis performed. Using multivariable logistic regression, traditional risk factors and fenofibrate allocation as STDR predictors were evaluated, with bootstrap-based optimism-adjusted estimates of predictive performance calculated.

RESULTS

STDR developed in 28 participants. The Aspelund et al. algorithm predicted STDR at 2- and 5-years with area under the curve (AUC) 0.86 (95% CI 0.77-0.94) and 0.86 (0.81-0.92), respectively. In the second model STDR risk factors were any DR at baseline (OR 24.0 [95% CI 5.53-104]), HbA1c (OR 1.95 [1.43-2.64]) and male sex (OR 4.34 [1.32-14.3]), while fenofibrate (OR 0.13 [0.05-0.38]) was protective. This model had excellent discriminatory ability (AUC = 0.89).

CONCLUSIONS

The algorithm by Aspelund et al. predicts STDR well in the FIELD ophthalmology substudy. Logistic regression analysis found DR at baseline, male sex, and HbA1c were predictive of STDR and, fenofibrate was protective.

Real-world outcomes of a clinical decision support system for diabetic retinopathy in Spain

Objective

The aim of present study was to evaluate our clinical decision support system (CDSS) for predicting risk of diabetic retinopathy (DR). We selected randomly a real population of patients with type 2 diabetes (T2DM) who were attending our screening programme.

Methods and analysis

The sample size was 602 patients with T2DM randomly selected from those who attended the DR screening programme. The algorithm developed uses nine risk factors: current age, sex, body mass index (BMI), duration and treatment of diabetes mellitus (DM), arterial hypertension, Glicated hemoglobine (HbA1c), urine–albumin ratio and glomerular filtration.

Results

The mean current age of 67.03±10.91, and 272 were male (53.2%), and DM duration was 10.12±6.4 years, 222 had DR (35.8%). The CDSS was employed for 1 year. The prediction algorithm that the CDSS uses included nine risk factors: current age, sex, BMI, DM duration and treatment, arterial hypertension, HbA1c, urine–albumin ratio and glomerular filtration. The area under the curve (AUC) for predicting the presence of any DR achieved a value of 0.9884, the sensitivity of 98.21%, specificity of 99.21%, positive predictive value of 98.65%, negative predictive value of 98.95%, α error of 0.0079 and β error of 0.0179.

Conclusion

Our CDSS for predicting DR was successful when applied to a real population.

Telescreening satisfaction: disparities between individuals with diabetic retinopathy and community health center staff

Background

The success of telescreening and the management of diabetic retinopathy (DR) in communities depends on stakeholder satisfaction, including both individuals with diabetes and community health center (CHC) staff. In this study, we investigated the satisfaction of both individuals with vision-threatening DR (VTDR) and CHC staff within the Shanghai Eye Disease Study (SEDS) comprehensive system for managing diabetic eye diseases at the primary care level.

Methods

The cross-sectional survey of patients receiving the service included 3,817 respondents with VTDR and focused on their satisfaction with the SEDS system, including the telescreening process, speed of feedback, interpretation of results, increased awareness of related diseases, and eye care services. The survey of the providers included 234 CHC staff respondents and focused on their satisfaction and the main barriers encountered during the implementation of the system. Sociodemographic characteristics and perceived barriers related to satisfaction were identified by conducting univariate and multivariate logistic regression analyses.

Results

The overall satisfaction of service recipients was 96.0%, and 75.8% of them were willing to undergo future telescreening for DR. The convenience of telescreening, organization of telescreening, and improvement in related disease awareness significantly correlated with satisfaction. Only 48.3% of the providers were satisfied with the SEDS system. The most frequently mentioned barriers to the development of the system were the inadequate levels of staffing (particularly technical staff), insufficient funding, and incomplete information transmission systems.

Conclusions

Disparities between high patient satisfaction and low provider satisfaction with the SEDS system were mainly related to the current weak level of ophthalmic expertise in the CHCs and the low awareness of screening for diabetic eye diseases among both patients and providers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-07500-w.

Telemedicine in diabetic retinopathy screening in India

With ever-growing prevalence of diabetes mellitus and its most common microvascular complication diabetic retinopathy (DR) in Indian population, screening for DR early for prevention of development of vision-threatening stages of the disease is becoming increasingly important. Most of the programs in India for DR screening are opportunistic and a universal screening program does not exist. Globally, telemedicine programs have demonstrated accuracy in classification of DR into referable disease, as well as into stages, with accuracies reaching that of human graders, in a cost-effective manner and with sufficient patient satisfaction. In this major review, we have summarized the global experience of telemedicine in DR screening and the way ahead toward planning a national integrated DR screening program based on telemedicine.

Complex interventions to implement a diabetic retinopathy care pathway in the public health system in Kerala: the Nayanamritham study protocol

INTRODUCTION

Using a type 2 hybrid effectiveness-implementation design, we aim to pilot a diabetic retinopathy (DR) care pathway in the public health system in Kerala to understand how it can be scaled up to and sustained in the whole state.

METHODS AND ANALYSIS

Currently, there is no systematic DR screening programme in Kerala. Our intervention is a teleophthalmology pathway for people with diabetes in the non-communicable disease registers in 16 family health centres. The planned implementation strategy of the pathway will be developed based on the discrete Expert Recommendations for Implementing Change taxonomy. We will use both quantitative data from a cross-sectional study and qualitative data obtained from structured interviews, surveys and group discussions with stakeholders to report the effectiveness of the DR care pathway and evaluation of the implementation strategy.We will use logistic regression models to assess crude associations DR and sight-threatening diabetic retinopathy and fractional polynomials to account for the form of continuous covariates to predict uptake of DR screening. The primary effectiveness outcome is the proportion of patients in the non-communicable disease register with diabetes screened for DR over 12 months. Other outcomes include cost-effectiveness, safety, efficiency, patient satisfaction, timeliness and equity. The outcomes of evaluation of the implementation strategies include acceptability, feasibility, adoption, appropriateness, fidelity, penetration, costs and sustainability. Addition of more family health centres during the staggered initial phase of the programme will be considered as a sign of acceptability and feasibility. In the long term, the state-wide adoption of the DR care pathway will be considered as a successful outcome of the Nayanamritham study.

ETHICS AND DISSEMINATION

The study was approved by Indian Medical Research Council (2018-0551) dated 13 March 2019. Study findings will be disseminated through scientific publications and the report will inform adoption of the DR care pathway by Kerala state in future.

TRIAL REGISTRATION NUMBER

ISRCTN28942696.

Vamos E, Molina G, Sivaprasad S. Associations between attainment of incentivized primary care indicators and incident sight-threatening diabetic retinopathy in England: A population-based historical cohort study

AIM

To examine the impact of attainment of primary care diabetes clinical indicators on progression to sight-threatening diabetic retinopathy (STDR) among those with mild non-proliferative diabetic retinopathy (NPDR).

MATERIALS AND METHODS

An historical cohort study of 18,978 adults (43.63% female) diagnosed with type 2 diabetes before 1 April 2010 and mild NPDR before 1 April 2011 was conducted. The data were obtained from the UK Clinical Practice Research Datalink during 2010-2017, provided by 330 primary care practices in England. Exposures included attainment of the Quality and Outcomes Framework HbA1c (≤59 mmol/mol [≤7.5%]), blood pressure (≤140/80 mmHg) and cholesterol (≤5 mmol/L) indicators in the financial year 2010-2011, as well as the number of National Diabetes Audit processes completed in 2010-2011. The outcome was time to incident STDR. Nearest neighbour propensity score matching was undertaken, and univariable and multivariable Cox proportional hazards models were then fitted using the matched samples. Concordance statistics were calculated for each model.

RESULTS

A total of 1037 (5.5%) STDR diagnoses were observed over a mean follow-up of 3.6 (SD 2.0) years. HbA1c, blood pressure and cholesterol indicator attainment were associated with lower rates of STDR (adjusted hazard ratios [95% CI] 0.64 [0.55-0.74; p < .001], 0.83 [0.72-0.94; p = .005] and 0.80 [0.66-0.96; p = .015], respectively).

CONCLUSIONS

Our findings provide support for meeting appropriate indicators for the management of type 2 diabetes in primary care to bring a range of benefits, including improved health outcomes-such as a reduction in the risk of STDR-for people with type 2 diabetes.

Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort

Background /Aims

To evaluate the performance of existing prediction models to determine risk of progression to referable diabetic retinopathy (RDR) using data from a prospective Irish cohort of people with type 2 diabetes (T2D).

Methods

A cohort of 939 people with T2D followed prospectively was used to test the performance of risk prediction models developed in Gloucester, UK, and Iceland. Observed risk of progression to RDR in the Irish cohort was compared with that derived from each of the prediction models evaluated. Receiver operating characteristic curves assessed models’ performance.

Results

The cohort was followed for a total of 2929 person years during which 2906 screening episodes occurred. Among 939 individuals followed, there were 40 referrals (4%) for diabetic maculopathy, pre-proliferative DR and proliferative DR. The original Gloucester model, which includes results of two consecutive retinal screenings; a model incorporating, in addition, systemic biomarkers (HbA1c and serum cholesterol); and a model including results of one retinopathy screening, HbA1c, total cholesterol and duration of diabetes, had acceptable discriminatory power (area under the curve (AUC) of 0.69, 0.76 and 0.77, respectively). The Icelandic model, which combined retinopathy grading, duration and type of diabetes, HbA1c and systolic blood pressure, performed very similarly (AUC of 0.74).

Conclusion

In an Irish cohort of people with T2D, the prediction models tested had an acceptable performance identifying those at risk of progression to RDR. These risk models would be useful in establishing more personalised screening intervals for people with T2D.

Biomarkers for Progression in Diabetic Retinopathy: Expanding Personalized Medicine through Integration of AI with Electronic Health Records

The goal of personalized diabetes eye care is to accurately predict in real-time the risk of diabetic retinopathy (DR) progression and visual loss. The use of electronic health records (EHR) provides a platform for artificial intelligence (AI) algorithms that predict DR progression to be incorporated into clinical decision-making. By implementing an algorithm on data points from each patient, their risk for retinopathy progression and visual loss can be modeled, allowing them to receive timely treatment. Data can guide algorithms to create models for disease and treatment that may pave the way for more personalized care. Currently, there exist numerous challenges that need to be addressed before reliably building and deploying AI algorithms, including issues with data quality, privacy, intellectual property, and informed consent.

Screening for Diabetic Retinopathy with Extended Intervals, Safe and Without Compromising Adherence: A Retrospective Cohort Study

INTRODUCTION

Screening for diabetic retinopathy (DR) prevents blindness through the early detection of sight-threatening retinal microvascular lesions that respond to timely local treatment. However, the provision of easy and regular access to DR screening programs is currently being challenged by the increasing prevalence of diabetes. One proposed solution is to extend the screening interval for patients at low risk for progression of retinopathy. To date, most providers of screening programs have hesitated to implement a strategy of extended intervals due to the lack of data on whether adherence and safety are compromised when retinal examinations occur less frequently. In the study reported here, we investigated adherence to the screening program and progression of retinopathy in patients with type 2 diabetes participating in a DR screening program with extended intervals.

METHODS

This was a retrospective study that included 1000 patients with type 2 diabetes mellitus who attended a screening program for DR. The patients were consecutively placed into a low-risk patient cohort with no retinopathy or into an intermediate-risk patient cohort with mild retinopathy (each cohort n = 500). Screening intervals were 36 months for the low-risk cohort and 18 months for the intermediate-risk cohort.

RESULTS

The 1000 subjects enrolled in the study had a median age of 68 (interquartile range 12) years and 60.4% were men. At the follow-up screening visit, data on 102 subjects were not included in the analysis of adherence rate due to death, severe systemic illness, other concurrent eye disease or migration. Among the 898 remaining subjects, adherence to the screening program was 93.7% (413/443) in the 36-month group and 98.3% (449/455) in the 18-month group (p < 0.0001). Non-adherence decreased with increasing age (odds ratio 0.92, 95% confidence interval 0.888-0.954, p = 0.0005). At follow-up, 65 subjects showed progression of retinopathy; none had worse than moderate retinopathy. Risk factors for DR and treatment for hyperglycemia, hypertension and hyperlipidemia were compared among subjects in the low-risk cohort: non-adherent subjects did not differ from their adherent counterparts without progression of DR, but the former had a shorter duration of diabetes and higher diastolic blood pressure than adherent subjects with progression of DR (4.5 vs. 7.5 years, p = 0.007; and 80 vs. 75 mmHg, p = 0.02, respectively).

CONCLUSION

The results suggest that screening DR at extended intervals can be achieved with high adherence rates without compromising patient safety. However, younger subjects and those at higher risk of progression may require extra attention.

Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders

OBJECTIVE

To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR.

METHODS

We randomly selected patients with diabetes screened twice, two years apart within a nationwide screening program. The reference standard was established via adjudication by retina specialists. Each patient's color fundus photographs were graded, and a patient was considered as having sight-threatening DR (STDR) if the worse eye had severe nonproliferative DR, proliferative DR, or diabetic macular edema. We compared DR screening via two modalities: DL and HG. For each modality, we simulated treatment referral by excluding patients with detected STDR from the second screening using that modality.

RESULTS

There were 5,738 patients (12.3% STDR) in the first screening. DL and HG captured different numbers of STDR cases, and after simulated referral and excluding ungradable cases, 4,148 and 4,263 patients remained in the second screening, respectively. The STDR prevalence at the second screening was 5.1% and 6.8% for DL- and HG-based screening, respectively. Along with the prevalence decrease, the sensitivity for both modalities decreased from the first to the second screening (DL: from 95% to 90%, p = 0.008; HG: from 74% to 57%, p < 0.001). At both the first and second screenings, the rate of false negatives for the DL was a fifth that of HG (0.5-0.6% vs. 2.9-3.2%).

CONCLUSION

On 2-year longitudinal follow-up of a DR screening cohort, STDR prevalence decreased for both DL- and HG-based screening. Follow-up screenings in longitudinal DR screening can be more difficult and induce lower sensitivity for both DL and HG, though the false negative rate was substantially lower for DL. Our data may be useful for health-economics analyses of longitudinal screening settings.

A personalised screening strategy for diabetic retinopathy: a cost-effectiveness perspective

AIMS/HYPOTHESIS

In this study we examined the cost-effectiveness of three different screening strategies for diabetic retinopathy: using a personalised adaptive model, annual screening (fixed intervals), and the current Dutch guideline (stratified based on previous retinopathy grade).

METHODS

For each individual, optimal diabetic retinopathy screening intervals were determined, using a validated risk prediction model. Observational data (1998-2017) from the Hoorn Diabetes Care System cohort of people with type 2 diabetes were used (n = 5514). The missing values of retinopathy grades were imputed using two scenarios of slow and fast sight-threatening retinopathy (STR) progression. By comparing the model-based screening intervals to observed time to develop STR, the number of delayed STR diagnoses was determined. Costs were calculated using the healthcare perspective and the societal perspective. Finally, outcomes and costs were compared for the different screening strategies.

RESULTS

For the fast STR progression scenario, personalised screening resulted in 11.6% more delayed STR diagnoses and €11.4 less costs per patient compared to annual screening from a healthcare perspective. The personalised screening model performed better in terms of timely diagnosis of STR (8.8% less delayed STR diagnosis) but it was slightly more expensive (€1.8 per patient from a healthcare perspective) than the Dutch guideline strategy.

CONCLUSIONS/INTERPRETATION

The personalised diabetic retinopathy screening model is more cost-effective than the Dutch guideline screening strategy. Although the personalised screening strategy was less effective, in terms of timely diagnosis of STR patients, than annual screening, the number of delayed STR diagnoses is low and the cost saving is considerable. With around one million people with type 2 diabetes in the Netherlands, implementing this personalised model could save €11.4 million per year compared with annual screening, at the cost of 658 delayed STR diagnoses with a maximum delayed time to diagnosis of 48 months.

Systemic risk factors contribute differently to the development of proliferative diabetic retinopathy and clinically significant macular oedema

AIMS/HYPOTHESIS

The purpose of screening for diabetic retinopathy is to detect either of the two sight-threatening complications: proliferative diabetic retinopathy (PDR) or clinically significant diabetic macular oedema (DME). The aim of the study was to evaluate whether systemic risk factors affect the risk of developing these two complications differently.

METHODS

Survival analysis with death as a competing risk was used to describe the effect of sex, age and time of onset of diabetes, systolic (SBP) and diastolic (DBP) BPs, and the weighted exposure and CV of HbA_1c for the development of PDR and DME from all 2773 patients treated for diabetic retinopathy in a defined population from the Aarhus area, Denmark, between 1 July 1994 and 1 July 2019.

RESULTS

Increasing HbA_1c above normal increased the risk of developing both PDR and DME (p < 0.04), and values below normal increased the risk of developing PDR (p < 0.013). Increasing DBP increased the risk of developing both PDR and DME (p < 0.0001), whereas increasing SBP increased the risk of developing DME (p < 0.0001), but not PDR (p > 0.08). The risk of developing PDR increased with decreasing age of onset of diabetes (p < 0.0001), whereas the risk of developing DME was maximal for a known onset of diabetes at about 30 years of age and decreased significantly for both lower and higher ages of onset (p < 0.0001). The risk of developing both PDR and DME was lower in women than in men (p < 0.004) and was reduced with lower variability of repeated HbA_1c measurements (p < 0.0001).

CONCLUSIONS/INTERPRETATION

Systemic risk factors such as metabolic regulation, arterial BP and the age of onset of diabetes contribute differently to the development of PDR and DME. The overall risk of developing treatment-requiring diabetic retinopathy should be calculated from the risks of reaching each of the two complications separately.

Cost-effectiveness of biennial screening for diabetes related retinopathy in people with type 1 and type 2 diabetes compared to annual screening

OBJECTIVE

Examine the health and economic impact of extending screening intervals in people with Type 2 diabetes (T2DM) and Type 1 diabetes (T1DM) without diabetes-related retinopathy (DR).

SETTING

Diabetic Eye Screening Wales (DESW).

STUDY DESIGN

Retrospective observational study with cost-utility analysis (CUA) and Decremental Cost-Effectiveness Ratios (DCER) study.

INTERVENTION

Biennial screening versus usual care (annual screening).

INPUTS

Anonymised data from DESW were linked to primary care data for people with two prior screening events with no DR. Transition probabilities for progression to DR were estimated based on a subset of 26,812 and 1232 people with T2DM and T1DM, respectively. DCER above £20,000 per QALY was considered cost-effective.

RESULTS

The base case analysis DCER results of £71,243 and £23,446 per QALY for T2DM and T1DM respectively at a 3.5% discount rate and £56,822 and £14,221 respectively when discounted at 1.5%. Diabetes management represented by the mean HbA_1c was 7.5% for those with T2DM and 8.7% for T1DM.

SENSITIVITY ANALYSIS

Extending screening to biennial based on HbA_1c, being the strongest predictor of progression of DR, at three levels of HbA_1c 6.5%, 8.0% and 9.5% lost one QALY saving the NHS £106,075; £58,653 and £31,626 respectively for T2DM and £94,696, £37,646 and £11,089 respectively for T1DM. In addition, extending screening to biennial based on the duration of diabetes > 6 years for T2DM per QALY lost, saving the NHS £54,106 and for 6-12 and > 12 years for T1DM saving £83,856, £23,446 and £13,340 respectively.

CONCLUSIONS

Base case and sensitivity analyses indicate biennial screening to be cost-effective for T2DM irrespective of HbA_1c and duration of diabetes. However, the uncertainty around the DCER indicates that annual screening should be maintained for those with T1DM especially when the HbA_1c exceeds 80 mmol/mol (9.5%) and duration of diabetes is greater than 12 years.

The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence

As a third of people with diabetes mellitus (DM) will suffer the microvascular complications of diabetic retinopathy (DR) and therapeutic options can effectively prevent visual impairment, systematic screening has substantially reduced disease burden in developed countries. In an effort to tackle the rising incidence of DM, screening programmes have modernized in synchrony with technical and infrastructural advancements. Patient evaluation has shifted from face-to-face ophthalmologist-based review delivered through community grassroots to asynchronous store-and-forward modern telemedicine platforms commissioned on a nationwide scale. First pioneered with primitive 35-mm slide film retinal photography, the last decade has seen an emergence of high resolution and widefield imaging devices, which may reveal extents of DR indiscernible to the clinician but with implications of potential earlier identification. Similar progress has been seen in image analysis approaches - automated image analysis of retinal photographs of DR has evolved from qualitative feature detection to rules-based algorithms to autonomous artificial intelligence-powered classification. Such models have, relatively rapidly, been validated and are now receiving approval from health regulation authorities with deployment into the clinical sphere. In this review, we chart the evolution of global DR screening programmes since their inception highlighting major milestones in healthcare infrastructure, telemedicine approaches and imaging devices that have shaped the robust and effective frameworks recognised today. We also provide an outlook for the future of DR screening in the context of recent technological advancements with respect to their limitations in current times.

Fundamental principles of an effective diabetic retinopathy screening program

BACKGROUND

Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults worldwide. Early detection and treatment are necessary to forestall vision loss from DR.

METHODS

A working group of ophthalmic and diabetes experts was established to develop a consensus on the key principles of an effective DR screening program. Recommendations are based on analysis of a structured literature review.

RESULTS

The recommendations for implementing an effective DR screening program are: (1) Examination methods must be suitable for the screening region, and DR classification/grading systems must be systematic and uniformly applied. Two-field retinal imaging is sufficient for DR screening and is preferable to seven-field imaging, and referable DR should be well defined and reliably identifiable by qualified screening staff; (2) in many countries/regions, screening can and should take place outside the ophthalmology clinic; (3) screening staff should be accredited and show evidence of ongoing training; (4) screening programs should adhere to relevant national quality assurance standards; (5) studies that use uniform definitions of risk to determine optimum risk-based screening intervals are required; (6) technology infrastructure should be in place to ensure that high-quality images can be stored securely to protect patient information; (7) although screening for diabetic macular edema (DME) in conjunction with DR evaluations may have merit, there is currently insufficient evidence to support implementation of programs solely for DME screening.

CONCLUSION

Use of these recommendations may yield more effective DR screening programs that reduce the risk of vision loss worldwide.

Prediction models for development of retinopathy in people with type 2 diabetes: systematic review and external validation in a Dutch primary care setting

AIMS/HYPOTHESIS

The aims of this study were to identify all published prognostic models predicting retinopathy risk applicable to people with type 2 diabetes, to assess their quality and accuracy, and to validate their predictive accuracy in a head-to-head comparison using an independent type 2 diabetes cohort.

METHODS

A systematic search was performed in PubMed and Embase in December 2019. Studies that met the following criteria were included: (1) the model was applicable in type 2 diabetes; (2) the outcome was retinopathy; and (3) follow-up was more than 1 year. Screening, data extraction (using the checklist for critical appraisal and data extraction for systemic reviews of prediction modelling studies [CHARMS]) and risk of bias assessment (by prediction model risk of bias assessment tool [PROBAST]) were performed independently by two reviewers. Selected models were externally validated in the large Hoorn Diabetes Care System (DCS) cohort in the Netherlands. Retinopathy risk was calculated using baseline data and compared with retinopathy incidence over 5 years. Calibration after intercept adjustment and discrimination (Harrell's C statistic) were assessed.

RESULTS

Twelve studies were included in the systematic review, reporting on 16 models. Outcomes ranged from referable retinopathy to blindness. Discrimination was reported in seven studies with C statistics ranging from 0.55 (95% CI 0.54, 0.56) to 0.84 (95% CI 0.78, 0.88). Five studies reported on calibration. Eight models could be compared head-to-head in the DCS cohort (N = 10,715). Most of the models underestimated retinopathy risk. Validating the models against different severities of retinopathy, C statistics ranged from 0.51 (95% CI 0.49, 0.53) to 0.89 (95% CI 0.88, 0.91).

CONCLUSIONS/INTERPRETATION

Several prognostic models can accurately predict retinopathy risk in a population-based type 2 diabetes cohort. Most of the models include easy-to-measure predictors enhancing their applicability. Tailoring retinopathy screening frequency based on accurate risk predictions may increase the efficiency and cost-effectiveness of diabetic retinopathy care.

REGISTRATION

PROSPERO registration ID CRD42018089122.

Diabetic eye screening with variable screening intervals based on individual risk factors is safe and effective in ophthalmic practice

PURPOSE

To test in a 'real world' diabetic eye-screening programme, a computer-based personal risk evaluation for progression to sight-threatening diabetic retinopathy. Screening intervals were individualized, and clinical outcomes, safety and cost-effectiveness documented.

METHODS

The RETINARISK algorithm was used in an ophthalmology clinic in Norway. The diabetes cohort was divided on voluntary basis into two groups: one with variable screening intervals based on their personal risk profile and the other group with conventional fixed interval diabetic eye screening. Compliance, clinical outcomes, safety and health economics were evaluated. A total of 843 diabetic patients participated in the program 2014-2019. A total of 63 had type 1 and 780 type 2 diabetes. A total of 671 patients had no diabetic retinopathy at baseline and 171 had retinopathy.

RESULTS

A total of 444 (53%) diabetic patients were included in the personal risk profile program and 399 in the fixed interval group. The RETINARISK algorithm calculated 563 screening intervals for the variable interval group, which was 23 ± 16 months (mean ± SD), compared to 14 ± 5 months for the group with fixed screening intervals. Due to selection bias, the two groups could not be directly compared. We did not experience any delay in detecting diabetic retinal changes when using the personal risk profile program.

CONCLUSION

The RETINARISK algorithm was safe and effective in a diabetic screening program in an ophthalmology clinic over 5 years. The use of the program reduces the mean frequency of screening visits and liberates valuable time in ophthalmic practice to be used on high-risk diabetic patients or other patient groups.

Screening for diabetic retinopathy: new perspectives and challenges

Although the prevalence of all stages of diabetic retinopathy has been declining since 1980 in populations with improved diabetes control, the crude prevalence of visual impairment and blindness caused by diabetic retinopathy worldwide increased between 1990 and 2015, largely because of the increasing prevalence of type 2 diabetes, particularly in low-income and middle-income countries. Screening for diabetic retinopathy is essential to detect referable cases that need timely full ophthalmic examination and treatment to avoid permanent visual loss. In the past few years, personalised screening intervals that take into account several risk factors have been proposed, with good cost-effectiveness ratios. However, resources for nationwide screening programmes are scarce in many countries. New technologies, such as scanning confocal ophthalmology with ultrawide field imaging and handheld mobile devices, teleophthalmology for remote grading, and artificial intelligence for automated detection and classification of diabetic retinopathy, are changing screening strategies and improving cost-effectiveness. Additionally, emerging evidence suggests that retinal imaging could be useful for identifying individuals at risk of cardiovascular disease or cognitive impairment, which could expand the role of diabetic retinopathy screening beyond the prevention of sight-threatening disease.

Applications of Artificial Intelligence for the Detection, Management, and Treatment of Diabetic Retinopathy

Rates of diabetic retinopathy (DR) and diabetic macular edema (DME), a common ocular complication of diabetes mellitus, are increasing worldwide. There is a substantial burden concerning the detection and management of this condition, particularly in low-resource settings, due to limitations such as the time, cost, and labor associated with current screening and treatment methods. Artificial intelligence (AI) is a modality of pattern recognition that has the potential to combat these limitations in a reliable and cost-effective way. This review explores the various applications of AI on the screening, management, and treatment of DR and DME. AI applications for detecting referable DR and DME have been the most thoroughly researched applications for this condition. While some studies exist using AI to stratify DR patients based on the risk of progression, predict treatment outcomes to anti-VEGF therapy, and explore the utilization of AI for clinical trials to develop new treatments for DR, further validation studies on larger datasets are warranted.

Innovations in personalised diabetes care and risk management

Type 2 diabetes is associated with an increased risk of developing macro and microvascular complications. Nevertheless, there is substantial heterogeneity between people with type 2 diabetes in their risk of developing such complications. Personalised medicine for people with type 2 diabetes may aid in efficient and tailored diabetes care for those at increased risk of developing such complications. Recently, progress has been made in the development of personalised diabetes care in several areas. Particularly for the risk prediction of cardiovascular disease, retinopathy and nephropathy, innovative methods have been developed for prediction and tailored monitoring or treatment to prevent such complications. For other complications or subpopulations of people with type 2 diabetes, such as the frail elderly, efforts are currently ongoing to develop such methods. In this review, we discuss the recent developments in innovations of personalised diabetes care for different complications and subpopulations of people with type 2 diabetes, their performance and modes of application in clinical practice.

Update on Screening for Sight-Threatening Diabetic Retinopathy

PURPOSE

The aim of this article was to describe recent advances in the use of new technology in diabetic retinopathy screening by looking at studies that assessed the effectiveness and cost-effectiveness of these technologies.

METHODS

The author conducts an ongoing search for articles relating to screening or management of diabetic retinopathy utilising Zetoc with keywords and contents page lists from relevant journals.

RESULTS

The areas discussed in this article are reference standards, alternatives to digital photography, area of retina covered by the screening method, size of the device and hand-held cameras, mydriasis versus non-mydriasis or a combination, measurement of distance visual acuity, grading of images, use of automated grading analysis and cost-effectiveness of the new technologies.

CONCLUSIONS

There have been many recent advances in technology that may be adopted in the future by screening programmes for sight-threatening diabetic retinopathy but each device will need to demonstrate effectiveness and cost-effectiveness before more widespread adoption.

The unmet need for better risk stratification of non-proliferative diabetic retinopathy

Diabetic retinopathy is a common microvascular complication of diabetes and remains one of the leading causes of preventable blindness in working-age people. Non-proliferative diabetic retinopathy is the earliest stage of diabetic retinopathy and is typically asymptomatic. Currently, the severity of diabetic retinopathy is assessed using semi-quantitative grading systems based on the presence or absence of retinal lesions. These methods are well validated, but do not predict those at high risk of rapid progression to sight-threatening diabetic retinopathy; therefore, new approaches for identifying these people are a current unmet need. We evaluated published data reporting the lesion characteristics associated with different progression profiles in people with non-proliferative diabetic retinopathy. Based on these findings, we propose that additional assessments of features of non-proliferative diabetic retinopathy lesions may help to stratify people based on the likelihood of rapid progression. In addition to the current classification, the following measurements should be considered: the shape and size of lesions; whether lesions are angiogenic in origin; the location of lesions, including predominantly peripheral lesions; and lesion turnover and dynamics. For lesions commonly seen in hypertensive retinopathy, a detailed assessment of potential concomitant diseases is also recommended. We believe that natural history studies of these changes will help characterize these non-proliferative diabetic retinopathy progression profiles and advance our understanding of the pathogenesis of diabetic retinopathy in order to individualize management of people with diabetic retinopathy.

Validation of a model for the prediction of retinopathy in persons with type 1 diabetes

BACKGROUND/AIM

To validate a previously developed model for prediction of diabetic retinopathy (DR) for personalised retinopathy screening in persons with type 1 diabetes.

METHODS

Retrospective medical data of persons with type 1 diabetes treated in an academic hospital setting were used for analysis. Sight-threatening retinopathy (STR) was defined as the presence of severe non-proliferative DR, proliferative DR or macular oedema. The presence and grade of retinopathy, onset of diabetes, systolic blood pressure, and levels of haemoglobin A_1c were used to calculate an individual risk estimate and personalised screening interval. In persons with STR, the occurrence was compared with the calculated date of screening. The model's predictive performance was measured using calibration and discrimination techniques.

RESULTS

Of the 268 persons included in our study, 24 (9.0%) developed STR during a mean follow-up of 4.6 years. All incidences of STR occurred after the calculated screening date. By applying the model, the mean calculated screening interval was 30.5 months, which is a reduction in screening frequency of 61% compared with annual screening and 21% compared with biennial screening. The discriminatory ability was good (Harrell's C-statistic=0.82, 95% CI 0.74 to 0.90), and calibration showed an overestimation of risk in persons who were assigned to a higher risk for STR.

CONCLUSION

This validation study suggests that a screening programme based on the previously developed prediction model is safe and efficient. The use of a personalised screening frequency could improve cost-effectiveness of diabetic eye care.

Prognostic prediction models for diabetic retinopathy progression: a systematic review

With the increasing incidence of diabetic retinopathy and its improved detection, there is increased demand for diabetic retinopathy treatment services. Prognostic prediction models have been used to optimise services but these were intended for early detection of sight-threatening retinopathy and are mostly used in diabetic retinopathy screening services. We wanted to look into the predictive ability and applicability of the existing models for the higher-risk patients referred into hospitals. We searched MEDLINE, EMBASE, COCHRANE CENTRAL, conference abstracts and reference lists of included publications for studies of any design using search terms related to diabetes, diabetic retinopathy and prognostic models. Search results were screened for relevance to the review question. Included studies had data extracted on model characteristics, predictive ability and validation. They were assessed for quality using criteria specified by PROBAST and CHARMS checklists, independently by two reviewers. Twenty-two articles reporting on 14 prognostic models (including four updates) met the selection criteria. Eleven models had internal validation, eight had external validation and one had neither. Discriminative ability with c-statistics ranged from 0.57 to 0.91. Studies ranged from low to high risk of bias, mostly due to the need for external validation or missing data. Participants, outcomes, predictors handling and modelling methods varied. Most models focussed on lower-risk patients, the majority had high risk of bias and doubtful applicability, but three models had some applicability for higher-risk patients. However, these models will also need updating and external validation in multiple hospital settings before being implemented into clinical practice.

Evidence-based Danish guidelines for screening of diabetic retinopathy

PURPOSE

Diabetic retinopathy (DR) is among the leading causes of visual loss in the working-age population. It is generally accepted that screening of DR is cost-effective and can detect DR before it becomes sight-threatening to allow timely treatment.

METHODS

A group of retinal specialists was formed by the Danish Ophthalmological Society with the aim to formulate contemporary evidence-based guidelines for screening of DR in order to implement these in the Danish screening system.

RESULTS

We hereby present evidence for DR-screening regarding (1) classification of DR, (2) examination techniques, (3) screening intervals and (4) automated screening. It is our recommendation that the International Clinical Retinopathy Disease Severity Scale should be used to classify DR. As a minimum, mydriatic two-field disc- and macular-centred images are required. In the case of suspected clinically significant diabetic macular oedema, supplementary optical coherence tomography can increase the diagnostic accuracy. There is solid evidence to support a flexible, individualized screening regimen. In particular, it is possible to prolong screening intervals to 24-48 months for patients with no or mild nonproliferative diabetic retinopathy (NPDR), but it is also possible to use extended intervals of 12-24 months for patients with moderate NPDR given that these are well-regulated regarding glycaemic control (HbA1c ≤ 53 mmol/mol) and blood pressure (≤130/80 mmHg). Automated screening of DR is encouraging but is not ready for implementation at present.

CONCLUSION

Danish evidenced-based guidelines for screening of DR support high-quality imaging and allow flexible, individualized screening intervals with a potential for extension to patients with low risk of DR progression.

Interventions to increase attendance for diabetic retinopathy screening

BACKGROUND

Despite evidence supporting the effectiveness of diabetic retinopathy screening (DRS) in reducing the risk of sight loss, attendance for screening is consistently below recommended levels.

OBJECTIVES

The primary objective of the review was to assess the effectiveness of quality improvement (QI) interventions that seek to increase attendance for DRS in people with type 1 and type 2 diabetes.Secondary objectives were:To use validated taxonomies of QI intervention strategies and behaviour change techniques (BCTs) to code the description of interventions in the included studies and determine whether interventions that include particular QI strategies or component BCTs are more effective in increasing screening attendance;To explore heterogeneity in effect size within and between studies to identify potential explanatory factors for variability in effect size;To explore differential effects in subgroups to provide information on how equity of screening attendance could be improved;To critically appraise and summarise current evidence on the resource use, costs and cost effectiveness.

SEARCH METHODS

We searched the Cochrane Library, MEDLINE, Embase, PsycINFO, Web of Science, ProQuest Family Health, OpenGrey, the ISRCTN, ClinicalTrials.gov, and the WHO ICTRP to identify randomised controlled trials (RCTs) that were designed to improve attendance for DRS or were evaluating general quality improvement (QI) strategies for diabetes care and reported the effect of the intervention on DRS attendance. We searched the resources on 13 February 2017. We did not use any date or language restrictions in the searches.

SELECTION CRITERIA

We included RCTs that compared any QI intervention to usual care or a more intensive (stepped) intervention versus a less intensive intervention.

DATA COLLECTION AND ANALYSIS

We coded the QI strategy using a modification of the taxonomy developed by Cochrane Effective Practice and Organisation of Care (EPOC) and BCTs using the BCT Taxonomy version 1 (BCTTv1). We used Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital (PROGRESS) elements to describe the characteristics of participants in the included studies that could have an impact on equity of access to health services.Two review authors independently extracted data. One review author entered the data into Review Manager 5 and a second review author checked them. Two review authors independently assessed risks of bias in the included studies and extracted data. We rated certainty of evidence using GRADE.

MAIN RESULTS

We included 66 RCTs conducted predominantly (62%) in the USA. Overall we judged the trials to be at low or unclear risk of bias. QI strategies were multifaceted and targeted patients, healthcare professionals or healthcare systems. Fifty-six studies (329,164 participants) compared intervention versus usual care (median duration of follow-up 12 months). Overall, DRS attendance increased by 12% (risk difference (RD) 0.12, 95% confidence interval (CI) 0.10 to 0.14; low-certainty evidence) compared with usual care, with substantial heterogeneity in effect size. Both DRS-targeted (RD 0.17, 95% CI 0.11 to 0.22) and general QI interventions (RD 0.12, 95% CI 0.09 to 0.15) were effective, particularly where baseline DRS attendance was low. All BCT combinations were associated with significant improvements, particularly in those with poor attendance. We found higher effect estimates in subgroup analyses for the BCTs 'goal setting (outcome)' (RD 0.26, 95% CI 0.16 to 0.36) and 'feedback on outcomes of behaviour' (RD 0.22, 95% CI 0.15 to 0.29) in interventions targeting patients, and 'restructuring the social environment' (RD 0.19, 95% CI 0.12 to 0.26) and 'credible source' (RD 0.16, 95% CI 0.08 to 0.24) in interventions targeting healthcare professionals.Ten studies (23,715 participants) compared a more intensive (stepped) intervention versus a less intensive intervention. In these studies DRS attendance increased by 5% (RD 0.05, 95% CI 0.02 to 0.09; moderate-certainty evidence).Fourteen studies reporting any QI intervention compared to usual care included economic outcomes. However, only five of these were full economic evaluations. Overall, we found that there is insufficient evidence to draw robust conclusions about the relative cost effectiveness of the interventions compared to each other or against usual care.With the exception of gender and ethnicity, the characteristics of participants were poorly described in terms of PROGRESS elements. Seventeen studies (25.8%) were conducted in disadvantaged populations. No studies were carried out in low- or middle-income countries.

AUTHORS' CONCLUSIONS

The results of this review provide evidence that QI interventions targeting patients, healthcare professionals or the healthcare system are associated with meaningful improvements in DRS attendance compared to usual care. There was no statistically significant difference between interventions specifically aimed at DRS and those which were part of a general QI strategy for improving diabetes care. This is a significant finding, due to the additional benefits of general QI interventions in terms of improving glycaemic control, vascular risk management and screening for other microvascular complications. It is likely that further (but smaller) improvements in DRS attendance can also be achieved by increasing the intensity of a particular QI component or adding further components.

Screening Intervals for Diabetic Retinopathy and Implications for Care

PURPOSE OF REVIEW

The purpose of this study is to review the evidence that lower risk groups who could safely be screened less frequently for sight-threatening diabetic retinopathy (DR) than annually.

RECENT FINDINGS

Data have demonstrated that people with no DR in either eye are at a low risk of progression to sight-threatening DR over a 2-year period (event rate 4.8 per 1000 person years), irrespective of whether the screening method is one-field non-mydriatic or two-field mydriatic digital photography. Low risk has been defined as no retinopathy on two consecutive screening episodes or no retinopathy on one screening episode combined with risk factor data. The risk of an extension to 2 years is less than 5 per 1000 person years in a population with a national screening programme, and the general standard of diabetes care is relatively good, whether low risk is defined as no retinopathy on two consecutive screening episodes or no retinopathy on one screening episode combined with other risk factor data. The definition used in different populations is likely to depend on the availability of data.

Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy

AIMS/HYPOTHESIS

We aimed to examine prospectively the association between a range of retinal vascular geometric variables measured from retinal photographs and the 6 year incidence and progression of diabetic retinopathy.

METHODS

We conducted a prospective, population-based cohort study of Asian Malay individuals aged 40-80 years at baseline (n = 3280) who returned for a 6 year follow-up. Retinal vascular geometric variables (tortuosity, branching, fractal dimension, calibre) were measured from baseline retinal photographs using a computer-assisted program (Singapore I Vessel Assessment). Diabetic retinopathy was graded from baseline and follow-up photographs using the modified Airlie House classification system. Incidence of diabetic retinopathy was defined as a severity of ≥15 at follow-up among those without diabetic retinopathy at baseline. Incidence of referable diabetic retinopathy was defined as moderate or severe non-proliferative diabetic retinopathy, proliferative diabetic retinopathy or diabetic macular oedema at follow-up in participants who had had no or mild non-proliferative diabetic retinopathy at baseline. Progression of diabetic retinopathy was defined as an increase in severity of ≥2 steps at follow-up. Log-binomial models with an expectation-maximisation algorithm were used to estimate RR adjusting for age, sex, diabetes duration, HbA_1c level, BP, BMI, estimated GFR and total and HDL-cholesterol at baseline.

RESULTS

A total of 427 individuals with diabetes participated in the baseline and 6 year follow-up examinations. Of these, 19.2%, 7.57% and 19.2% developed incidence of diabetic retinopathy, incidence of referable diabetic retinopathy and diabetic retinopathy progression, respectively. After multivariate adjustment, greater arteriolar simple tortuosity (mean RR [95% CI], 1.34 [1.04, 1.74]), larger venular branching angle (RR 1.26 [1.00, 1.59]) and larger venular branching coefficient (RR 1.26 [1.03, 1.56]) were associated with incidence of diabetic retinopathy. Greater arteriolar simple tortuosity (RR 1.82 [1.32, 2.52]), larger venular branching coefficient (RR 1.46 [1.03, 2.07]), higher arteriolar fractal dimension (RR 1.59 [1.08, 2.36]) and larger arteriolar calibre (RR 1.83 [1.15, 2.90]) were associated with incidence of referable diabetic retinopathy. Greater arteriolar simple tortuosity (RR 1.34 [1.12, 1.61]) was associated with diabetic retinopathy progression. Addition of retinal vascular variables improved discrimination (C-statistic 0.796 vs 0.733, p = 0.031) and overall reclassification (net reclassification improvement 18.8%, p = 0.025) of any diabetic retinopathy risk beyond established risk factors.

CONCLUSIONS/INTERPRETATION

Retinal vascular geometry measured from fundus photographs predicted the incidence and progression of diabetic retinopathy in adults with diabetes, beyond established risk factors.

The English National Screening Programme for diabetic retinopathy 2003-2016

The aim of the English NHS Diabetic Eye Screening Programme is to reduce the risk of sight loss amongst people with diabetes by the prompt identification and effective treatment if necessary of sight-threatening diabetic retinopathy, at the appropriate stage during the disease process. In order to achieve the delivery of evidence-based, population-based screening programmes, it was recognised that certain key components were required. It is necessary to identify the eligible population in order to deliver the programme to the maximum number of people with diabetes. The programme is delivered and supported by suitably trained, competent, and qualified, clinical and non-clinical staff who participate in recognised ongoing Continuous Professional Development and Quality Assurance schemes. There is an appropriate referral route for those with screen-positive disease for ophthalmology treatment and for assessment of the retinal status in those with poor-quality images. Appropriate assessment of control of their diabetes is also important in those who are screen positive. Audit and internal and external quality assurance schemes are embedded in the service. In England, two-field mydriatic digital photographic screening is offered annually to all people with diabetes aged 12 years and over. The programme commenced in 2003 and reached population coverage across the whole of England by 2008. Increasing uptake has been achieved and the current annual uptake of the programme in 2015-16 is 82.8% when 2.59 million people with diabetes were offered screening and 2.14 million were screened. The benefit of the programme is that, in England, diabetic retinopathy/maculopathy is no longer the leading cause of certifiable blindness in the working age group.

Clinical worthlessness of genetic prediction of common forms of diabetes mellitus and related chronic complications: A position statement of the Italian Society of Diabetology

AIM

We are currently facing several attempts aimed at marketing genetic data for predicting multifactorial diseases, among which diabetes mellitus is one of the more prevalent. The present document primarily aims at providing to practicing physicians a summary of available data regarding the role of genetic information in predicting diabetes and its chronic complications.

DATA SYNTHESIS

Firstly, general information about characteristics and performance of risk prediction tools will be presented in order to help clinicians to get acquainted with basic methodological information related to the subject at issue. Then, as far as type 1 diabetes is concerned, available data indicate that genetic information and counseling may be useful only in families with many affected individuals. However, since no disease prevention is possible, the utility of predicting this form of diabetes is at question. In the case of type 2 diabetes, available data really question the utility of adding genetic information on top of well performing, easy available and inexpensive non-genetic markers. Finally, the possibility of using the few available genetic data on diabetic complications for improving our ability to predict them will also be presented and discussed. For cardiovascular complication, the addition of genetic information to models based on clinical features does not translate in a substantial improvement in risk discrimination. For all other diabetic complications genetic information are currently very poor and cannot, therefore, be used for improving risk stratification.

CONCLUSIONS

In all, nowadays the use of genetic testing for predicting diabetes and its chronic complications is definitively of little value in clinical practice.

Diabetic Retinopathy in Italy: Epidemiology Data and Telemedicine Screening Programs

In Italy, the number of people living with diabetes is about 3.5 million (5.5% of the population), with an increase by about 60% in the last 20 years and with 1 person out of 3 older than 65 years. The Italian Health Service system estimates that 10 billion euros is spent annually on caring for patients with diabetes, a figure that increases yearly. No national data on prevalence and incidence of legal blindness in patients with diabetes and no national registry of patients with diabetic retinopathy (DR) are currently available. However, the available epidemiological data (in several locations throughout the country) are consistent with those reported in other European countries. The use of telemedicine for the screening of DR in Italy is confined to geographically limited locations. The available data in the literature on implementation and use of telematic screening proved to be successful from patient, caregiver, and authorities point of view. This review addresses the available epidemiological data on DR and telematic screening realities in Italy and thus may help in establishing a national screening program.

Diabetic Retinopathy Screening Using Telemedicine Tools: Pilot Study in Hungary

Introduction. Diabetic retinopathy (DR) is a sight-threatening complication of diabetes. Telemedicine tools can prevent blindness. We aimed to investigate the patients' satisfaction when using such tools (fundus camera examination) and the effect of demographic and socioeconomic factors on participation in screening. Methods. Pilot study involving fundus camera screening and self-administered questionnaire on participants' experience during fundus examination (comfort, reliability, and future interest in participation), as well as demographic and socioeconomic factors was performed on 89 patients with known diabetes in Csongrád County, a southeastern region of Hungary. Results. Thirty percent of the patients had never participated in any ophthalmological screening, while 25.7% had DR of some grade based upon a standard fundus camera examination and UK-based DR grading protocol (Spectra™ software). Large majority of the patients were satisfied with the screening and found it reliable and acceptable to undertake examination under pupil dilation; 67.3% were willing to undergo nonmydriatic fundus camera examination again. There was a statistically significant relationship between economic activity, education and marital status, and future interest in participation. Discussion. Participants found digital retinal screening to be reliable and satisfactory. Telemedicine can be a strong tool, supporting eye care professionals and allowing for faster and more comfortable DR screening.