Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban-rural locations, and socioeconomic indices: results from the SMART India population-based cross-sectional screening study

Health transition and eye care policy planning for people with diabetic retinopathy in south Asia

The prevalence of type 2 diabetes (T2D), associated systemic disorders, diabetic retinopathy (DR) and current health policies in south Asian countries were analysed to assess country-specific preparedness to meet the 2030 Sustainable Development Goals. The south Asian countries were classified by human development index, socio-demographic index, multidimensional poverty indices, and eye health resources for epidemiological resource-level analysis. In south Asia, the prevalence of diagnosed and undiagnosed T2D in adults aged 40 years or above, was higher in Pakistan (26.3%) and Afghanistan (71.4%), respectively; India has the highest absolute number of people with DR, and Afghanistan has the highest prevalence of DR (50.6%). In this region, out-of-pocket spending is high (∼77%). This Health Policy is a situational analysis of data available on the prevalence of DR and common eye diseases in people with T2D in south Asia and available resources to suggest tailored health policies to local needs. The common issues in the region are insufficient human resources for eye health, unequal distribution of available workforce, and inadequate infrastructure. Addressing these challenges of individuals with T2D and DR, a 10-point strategy is suggested to improve infrastructure, augment human resources, reduce out-of-pocket spending, employ targeted screening, and encourage public-private partnerships.

Advancing diabetes surveillance ecosystems: a case study of India

Professional society and expert guidelines recommend the achievement of glycaemic, blood pressure, and cholesterol targets to prevent the microvascular and macrovascular complications of diabetes. The WHO Diabetes Compact recommends that countries meet and monitor these targets for diabetes management. Surveillance-ie, continuous, systematic measurement, analysis, and interpretation of data-is a crucial component of public health. In this Personal View, we use the case of India as an illustration of the challenges and future directions needed for a diabetes surveillance system that documents national progress and persistent gaps. To address the growing burdens of diabetes and cardiometabolic diseases, the Government of India has launched programmes such as the National Programme for Prevention and Control of Non-Communicable Diseases. Different surveys have provided estimates of the diabetes care continuum of awareness, treatment, and control at the national, state, and, very recently, district level. We reviewed the literature to analyse how these surveys have varied in both their data collection methods and the reported estimates of the diabetes care continuum. We propose an integrated surveillance and monitoring framework to augment decentralised decision making, leveraging the complementary strengths of different surveys and electronic health record databases, such as data obtained by the National Programme for Prevention and Control of Non-Communicable Diseases, and building on methodological advances in model-based small-area estimation and data fusion. Such a framework could aid state and district administrators in monitoring the progress of diabetes screening and management initiatives, and benchmarking against national and global standards in all countries.

Improving diabetic retinopathy screening at the point of care: integrating telemedicine to overcome current challenges

OBJECTIVE

To investigate the utility of point of care screening of diabetic retinopathy (DR) and the impact of a telemedicine program to overcome current challenges.

METHODS

This was a retrospective study on people with type 2 diabetes mellitus (T2DM) who were screened for DR using the single-field non-mydriatic fundus photography at the point of care during routine follow-up visits at endocrinology clinic. Retinal images were uploaded and sent to a retina specialist for review. Reports indicating retinopathy status and the need for direct retinal examination were transmitted back to the endocrinology clinic. All patients were informed about DR status and, if needed, referred to the retina specialist for direct retinal examination.

RESULTS

Of the 1159 individuals screened for DR, 417 persons (35.98%) were screen-positive and referred to the retina specialist for direct retinal examination. A total of 121 individuals (29.01%) underwent direct retinal examination by the specialist. Diabetes macular edema (DME) was detected in 12.1%. In addition, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) were detected in 53.4% and 2.6% of the patients, respectively.

CONCLUSION

Integrating DR screening program at the point of care at the secondary care services improves the rate of DR screening as well as detection of sight threatening retinopathy and provides the opportunity for timely intervention in order to prevent advanced retinopathy in people with T2DM.

Unveiling the crucial role of intercellular adhesion molecule-1 in secondary diabetic complications

Diabetes mellitus is associated with secondary complications such as diabetic retinopathy (DR), nephropathy (DN), and cardiomyopathy (DCM), all of which significantly impact patient health. Intercellular adhesion molecule-1 (ICAM-1) has been implicated in inflammatory responses and endothelial dysfunction, both crucial in the pathogenesis of these complications. The goal of this review is to investigate at potential therapy methods that target ICAM-1 pathways and to better understand the multifaceted role of ICAM-1 in secondary diabetic problems. A meticulous analysis of scholarly literature published globally was conducted to examine ICAM-1involvement in inflammatory processes, endothelial dysfunction, and oxidative stress related to diabetes and its complications. Elevated ICAM-1 levels are strongly associated with augmented leukocyte adhesion, compromised microvascular function, and heightened oxidative stress in diabetes. These pathways contribute significantly to DR, DN, and DCM pathogenesis, highlighting ICAM-1 as a key player in their progression. Understanding ICAM-1 role in secondary diabetic complications offers insights into novel therapeutic strategies. Targeting ICAM-1 pathways may mitigate inflammation, improve endothelial function, and ultimately attenuate diabetic complications, thereby enhancing patient health outcomes. Continued research in this area is crucial for developing effective targeted therapies.

National prevalence of vision impairment and blindness and associated risk factors in adults aged 40 years and older with known or undiagnosed diabetes: results from the SMART-India cross-sectional study

BACKGROUND

National estimates of the prevalence of vision impairment and blindness in people with diabetes are required to inform resource allocation. People with diabetes are more susceptible to conditions such as diabetic retinopathy that can impair vision; however, these are often missed in national studies. This study aims to determine the prevalence and risk factors of vision impairment and blindness in people with diabetes in India.

METHODS

Data from the SMART-India study, a cross-sectional survey with national coverage of 42 147 Indian adults aged 40 years and older, collected using a complex sampling design, were used to obtain nationally representative estimates for the prevalence of vision impairment and blindness in people with diabetes in India. Vulnerable adults (primarily those who did not have capacity to provide consent); pregnant and breastfeeding women; anyone deemed too ill to be screened; those who did not provide consent; and people with type 1 diabetes, gestational diabetes, or secondary diabetes were excluded from the study. Vision impairment was defined as presenting visual acuity of 0·4 logMAR or higher and blindness as presenting a visual acuity of 1·0 logMAR or higher in the better-seeing eye. Demographic, anthropometric, and laboratory data along with geographic distribution were analysed in all participants with available data. Non-mydriatic retinal images were used to grade diabetic retinopathy, and risk factors were also assessed.

FINDINGS

A total of 7910 people with diabetes were included in the analysis, of whom 5689 had known diabetes and 2221 were undiagnosed. 4387 (55·5%) of 7909 participants with available sex data were female and 3522 (44·5%) participants were male. The estimated national prevalence of vision impairment was 21·1% (95% CI 15·7-27·7) and blindness 2·4% (1·7-3·4). A higher prevalence of any vision impairment (29·2% vs 19·6%; p=0·016) and blindness (6·7% vs 1·6%; p<0·0001) was observed in those with ungradable images. In known diabetes, diabetic retinopathy (adjusted odds ratio [aOR] 3·06 [95% CI 1·25-7·51]), vision-threatening diabetic retinopathy (aOR 7·21 [3·52-14·75]), and diabetic macular oedema (aOR 5·41 [2·20-13·33]) were associated with blindness in adjusted analysis. Common sociodemographic risk factors for vision impairment and blindness include older age, lower educational attainment, and unemployment.

INTERPRETATION

Based on the estimated 101 million people with diabetes in 2021 and the interpretation of the data from this study, approximately 21 million people with diabetes have vision impairment in India, of whom 2·4 million are blind. Higher prevalence is observed in those from lower socio-economic strata and policy makers should focus on these groups to reduce inequalities in health care.

FUNDING

Global Challenge Research Fund of United Kingdom Research and Innovation through the Medical Research Council.

Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms

PURPOSE

To study the role of artificial intelligence (AI) in developing diabetic macular edema (DME) management recommendations by creating and comparing responses to clinicians in hypothetical AI-generated case scenarios. The study also examined whether its joint recommendations followed national DME management guidelines.

METHODS

The AI hypothetically generated 50 ocular case scenarios from 25 patients using keywords like age, gender, type, duration and control of diabetes, visual acuity, lens status, retinopathy stage, coexisting ocular and systemic co-morbidities, and DME-related retinal imaging findings. For DME and ocular co-morbidity management, we calculated inter-rater agreements (kappa analysis) separately for clinician responses, AI-platforms, and the "majority clinician response" (the maximum number of identical clinician responses) and "majority AI-platform" (the maximum number of identical AI responses). Treatment recommendations for various situations were compared to the Indian national guidelines.

RESULTS

For DME management, clinicians (ĸ=0.6), AI platforms (ĸ=0.58), and the 'majority clinician response' and 'majority AI response' (ĸ=0.69) had moderate to substantial inter-rate agreement. The study showed fair to substantial agreement for ocular co-morbidity management between clinicians (ĸ=0.8), AI platforms (ĸ=0.36), and the 'majority clinician response' and 'majority AI response' (ĸ=0.49). Many of the current study's recommendations and national clinical guidelines agreed and disagreed. When treating center-involving DME with very good visual acuity, lattice degeneration, renal disease, anaemia, and a recent history of cardiovascular disease, there were clear disagreements.

CONCLUSION

For the first time, this study recommends DME management using large language model-based generative AI. The study's findings could guide in revising the global DME management guidelines.

Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios

PURPOSE

To study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases.

METHODS

Five clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the "majority clinician response" (defined as the maximum number of identical responses provided by the clinicians) and "majority AI-platform" (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned.

RESULTS

Clinicians, AI platforms, and the "majority clinician response" and "majority AI response" had fair inter-rater reliability (k value: 0.21-0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0-2 needed screening within 5 years, 3-5 within 1 year, and 6-12 immediately.

CONCLUSION

Based on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations.

CLINICAL TRIAL REGISTRATION

Not applicable.

Knowledge, attitude, and practice of diabetes in patients with and without sight-threatening diabetic retinopathy from two secondary eye care centres in India

BACKGROUND/AIMS

Good knowledge, Attitude, and Practice (KAP) of diabetes influence its control and complications. We examined the KAP of diabetes in patients with sight-threatening diabetic retinopathy (STDR) and non-sight-threatening diabetic retinopathy (NSTDR) attending two different referral hospitals in India.

METHODS

400 consecutive patients (mean age = 58.5 years ± 10.3) with diabetic retinopathy attending retina referral clinics in Chennai (private) and Darjeeling (public) were recruited. A validated questionnaire on diabetic KAP was administered in English or the local language. Data were analysed using an established scalar-scoring method in which a score of 1 was assigned to the correct answer/healthy lifestyle and 0 to an incorrect answer/unhealthy lifestyle/practice. Clinical data included fasting blood sugar, blood pressure, retinopathy, and visual acuity. Retinopathy was graded as STDR/NSTDR from retinal images using Early Treatment of Diabetic Retinopathy Study criteria.

RESULTS

Usable data from 383 participants (95.8%) were analysed. Of these, 83 (21.7%) had STDR, and 300 (78.3%) had NSTDR. The NSTDR group reported a significantly lower total KAP score (mean rank = 183.4) compared to the STDR group (mean rank = 233.1), z = -3.0, p < 0.001. A significantly greater percentage in the NSTDR group reported to being unaware that diabetes could affect eyes, did not know about possible treatment for DR, and checked their blood sugar less frequently than once a month.

CONCLUSION

Patients who had not developed STDR had poorer KAP about diabetes and diabetes-related eye diseases. This is an important issue to address as the risk of their progressing to STDR is high unless appropriate steps to improve their knowledge/awareness and lifestyle practice are introduced early.

Artificial intelligence and hemodynamic studies in optical coherence tomography angiography for diabetic retinopathy evaluation: A review

Diabetic retinopathy (DR) is a rapidly emerging retinal abnormality worldwide, which can cause significant vision loss by disrupting the vascular structure in the retina. Recently, optical coherence tomography angiography (OCTA) has emerged as an effective imaging tool for diagnosing and monitoring DR. OCTA produces high-quality 3-dimensional images and provides deeper visualization of retinal vessel capillaries and plexuses. The clinical relevance of OCTA in detecting, classifying, and planning therapeutic procedures for DR patients has been highlighted in various studies. Quantitative indicators obtained from OCTA, such as blood vessel segmentation of the retina, foveal avascular zone (FAZ) extraction, retinal blood vessel density, blood velocity, flow rate, capillary vessel pressure, and retinal oxygen extraction, have been identified as crucial hemodynamic features for screening DR using computer-aided systems in artificial intelligence (AI). AI has the potential to assist physicians and ophthalmologists in developing new treatment options. In this review, we explore how OCTA has impacted the future of DR screening and early diagnosis. It also focuses on how analysis methods have evolved over time in clinical trials. The future of OCTA imaging and its continued use in AI-assisted analysis is promising and will undoubtedly enhance the clinical management of DR.

The Utility of ChatGPT in Diabetic Retinopathy Risk Assessment: A Comparative Study with Clinical Diagnosis

Purpose

To evaluate the ability of an artificial intelligence (AI) model, ChatGPT, in predicting the diabetic retinopathy (DR) risk.

Methods

This retrospective observational study utilized an anonymized dataset of 111 patients with diabetes who underwent a comprehensive eye examination along with clinical and biochemical assessments. Clinical and biochemical data along with and without central subfield thickness (CST) values of the macula from OCT were uploaded to ChatGPT-4, and the response from the ChatGPT was compared to the clinical DR diagnosis made by an ophthalmologist.

Results

The study assessed the consistency of responses provided by ChatGPT, yielding an Intraclass Correlation Coefficient (ICC) value of 0.936 (95% CI, 0.913-0.954, p < 0.001) (with CST) and 0.915 (95% CI, 0.706-0.846, p < 0.001) (without CST), both situations indicated excellent reliability. The sensitivity and specificity of ChatGPT in predicting the DR cases were evaluated. The results revealed a sensitivity of 67% with CST and 73% without CST. The specificity was 68% with CST and 54% without CST. However, Cohen's kappa revealed only a fair agreement between ChatGPT predictions and clinical DR status in both situations, with CST (kappa = 0.263, p = 0.005) and without CST (kappa = 0.351, p < 0.001).

Conclusion

This study suggests that ChatGPT has the potential of a preliminary DR screening tool with further optimization needed for clinical use.

From randomised controlled trials to real-world data: Clinical evidence to guide management of diabetic macular oedema

Randomised clinical trials (RCTs) are generally considered the gold-standard for providing scientific evidence for treatments' effectiveness and safety but their findings may not always be generalisable to the broader population treated in routine clinical practice. RCTs include highly selected patient populations that fit specific inclusion and exclusion criteria. Although they may have a lower level of certainty than RCTs on the evidence hierarchy, real-world data (RWD), such as observational studies, registries and databases, provide real-world evidence (RWE) that can complement RCTs. For example, RWE may help satisfy requirements for a new indication of an already approved drug and help us better understand long-term treatment effectiveness, safety and patterns of use in clinical practice. Many countries have set up registries, observational studies and databases containing information on patients with retinal diseases, such as diabetic macular oedema (DMO). These DMO RWD have produced significant clinical evidence in the past decade that has changed the management of DMO. RWD and medico-administrative databases are a useful resource to identify low frequency safety signals. They often have long-term follow-up with a large number of patients and minimal exclusion criteria. We will discuss improvements in healthcare information exchange technologies, such as blockchain technology and FHIR (Fast Healthcare Interoperability Resources), which will connect and extend databases already available. These registries can be linked with existing or emerging retinal imaging modalities using artificial intelligence to aid diagnosis, treatment decisions and provide prognostic information. The results of RCTs and RWE are combined to provide evidence-based guidelines.

Prevalence of Retinopathy in Prediabetic Populations: A Systematic Review and Meta-Analysis

Among the leading causes of vision impairment and blindness globally, diabetic retinopathy (DR) is one of the most important causes. There is increasing evidence of DR prevalence in the prediabetic population. This systematic review presents collective data on retinopathy in the prediabetic population. This review article aimed to estimate the reported prevalence of retinopathy in prediabetes, impaired glucose tolerance test (GTT) without diabetes mellitus, and the risk factors involved and to summarize it. Literature searches were done using the Web of Science, CINAHL, Google Scholar, Cochrane, EMBASE, and PubMed databases from inception to April 2023. Our search included the words prediabetes, DR, and risk factors. All searches were looked at for methodological quality and evidence. Thirty-one studies were included after the screening. Population-based data were used in 23 studies (82.1%). The prediabetic population screened was 10,539. The prevalence of retinopathy ranged between 0.3% and 20.9%, showing a median of 8.1% with an interquartile range (IQR) of 4.2-11%, showing great variance in estimates due to the use of different screening methods, methods used for retinopathy grading, and study populations. Several studies compared the population with normal GTT with impaired glucose tolerance (IGT) and inferred that there was a lower prevalence of retinopathy in the normal GTT population (3.0%, IQR 0.3-7.4%) than prediabetes (6.7%, IQR 1.9-10.1%). According to this data, a greater retinopathy prevalence was found in prediabetic populations.

The association between polluted fuel use and self-reported insomnia symptoms among middle-aged and elderly Indian adults: a cross-sectional study based on LASI, wave 1

BACKGROUND

Insomnia predisposes the aging population to reduced quality of life and poor mental and physical health. Evidence of the association between polluted fuel use and insomnia symptoms is limited and is non-existent for the Indian population. Our study aimed to explore the link between polluted fuel use and insomnia symptoms in middle-aged and older (≥ 45 years) Indian populations.

METHODS

We utilized data from nationally representative Longitudinal Aging Study in India (LASI) Wave 1. Participants with complete information on fuel use, insomnia symptoms, and covariates were included. Insomnia symptoms were indicated by the presence of at least one of three symptoms: difficulty in initiating sleep (DIS), difficulty in maintaining sleep (DMS), or early morning awakening (EMA), ≥ 5 times/week. Survey-weighted multivariable logistic regression analyses were conducted to evaluate the association between polluted fuel use and insomnia symptoms. We also assessed the interaction of association in subgroups of age, gender, BMI, drinking, and smoking status.

RESULTS

Sixty thousand five hundred fifteen participants met the eligibility criteria. Twenty-eight thousand two hundred thirty-six (weighted percentage 48.04%) used polluted fuel and 5461 (weighted percentage 9.90%) reported insomnia symptoms. After full adjustment, polluted fuel use was associated with insomnia symptoms (OR 1.16; 95%CI 1.08-1.24) and was linked with DIS, DMS, and EMA (OR 1.14; 95%CI 1.05-1.24, OR 1.12; 95%CI 1.03-1.22, and OR 1.15; 95%CI 1.06-1.25, respectively). No significant interactions for polluted fuel use and insomnia symptoms were observed for analyses stratified by age, sex, BMI, drinking, or smoking.

CONCLUSIONS

Polluted fuel use was positively related to insomnia symptoms among middle-aged and older Indians. Suggestions are offered within this article for further studies to confirm our results, to explore underlying mechanisms, and to inform intervention strategies.

Burden of undiagnosed and suboptimally controlled diabetes in selected regions of India: Results from the SMART India population-level diabetes screening study

AIMS

To estimate the prevalence of undiagnosed diabetes and suboptimally controlled diabetes and the associated risk factors by community screening in India.

METHODS

In this multi-centre, cross-sectional study, house-to-house screening was conducted in people aged ≥40 years in urban and rural areas across 10 states and one union territory in India between November 2018 and March 2020. Participants underwent anthropometry, clinical and biochemical assessments. Capillary random blood glucose and point-of-care glycated haemoglobin (HbA1c ) were used to diagnose diabetes. The prevalence of undiagnosed diabetes and suboptimal control (HbA1c ≥53 mmol/mol [≥7%]) among those with known diabetes was assessed.

RESULTS

Among the 42,146 participants screened (22,150 urban, 19,996 rural), 5689 had known diabetes. The age-standardised prevalence of known diabetes was 13.1% (95% CI 12.8-13.4); 17.2% in urban areas and 9.4% in rural areas. The age-standardised prevalence of undiagnosed diabetes was 6.0% (95% CI 5.7-6.2); similar in both urban and rural areas with the highest proportions seen in the East (8.0%) and South (7.8%) regions. When we consider all people with diabetes in the population, 22.8% of individuals in urban areas and 36.7% in rural areas had undiagnosed diabetes. Almost 75% of the individuals with known diabetes had suboptimal glycaemic control.

CONCLUSIONS

High prevalence of undiagnosed diabetes and suboptimally controlled diabetes emphasises the urgent need to identify and optimally treat people with diabetes to reduce the burden of diabetes.

Development and evaluation of a risk prediction model for diabetes mellitus type 2 patients with vision-threatening diabetic retinopathy

Objective

This study aims to develop and evaluate a non-imaging clinical data-based nomogram for predicting the risk of vision-threatening diabetic retinopathy (VTDR) in diabetes mellitus type 2 (T2DM) patients.

Methods

Based on the baseline data of the Guangdong Shaoguan Diabetes Cohort Study conducted by the Zhongshan Ophthalmic Center (ZOC) in 2019, 2294 complete data of T2DM patients were randomly divided into a training set (n=1605) and a testing set (n=689). Independent risk factors were selected through univariate and multivariate logistic regression analysis on the training dataset, and a nomogram was constructed for predicting the risk of VTDR in T2DM patients. The model was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) in the training and testing datasets to assess discrimination, and Hosmer-Lemeshow test and calibration curves to assess calibration.

Results

The results of the multivariate logistic regression analysis showed that Age (OR = 0.954, 95% CI: 0.940-0.969, p = 0.000), BMI (OR = 0.942, 95% CI: 0.902-0.984, p = 0.007), systolic blood pressure (SBP) (OR =1.014, 95% CI: 1.007-1.022, p = 0.000), diabetes duration (10-15y: OR =3.126, 95% CI: 2.087-4.682, p = 0.000; >15y: OR =3.750, 95% CI: 2.362-5.954, p = 0.000), and glycated hemoglobin (HbA1C) (OR = 1.325, 95% CI: 1.221-1.438, p = 0.000) were independent risk factors for T2DM patients with VTDR. A nomogram was constructed using these variables. The model discrimination results showed an AUC of 0.7193 for the training set and 0.6897 for the testing set. The Hosmer-Lemeshow test results showed a high consistency between the predicted and observed probabilities for both the training set (Chi-square=2.2029, P=0.9742) and the testing set (Chi-square=7.6628, P=0.4671).

Conclusion

The introduction of Age, BMI, SBP, Duration, and HbA1C as variables helps to stratify the risk of T2DM patients with VTDR.

Role of cystatin C in the detection of sight-threatening diabetic retinopathy in Asian Indians with type 2 diabetes

AIM

To study the association between cystatin C and sight-threatening diabetic retinopathy (STDR) in Asian Indians with type 2 diabetes (T2DM).

METHODS

In a cross-sectional study carried out at two tertiary centres in India in 2022, individuals with T2DM underwent clinical and ophthalmic assessments and estimation of serum cystatin C. Grading of DR was done by retina specialists. STDR was defined by the presence of severe non-proliferative DR (NPDR), proliferative DR (PDR) and/or diabetic macular edema. Receiver operating characteristic (ROC) curves were used to identify cystatin C cut-off value for detecting STDR.

RESULTS

Among 420 individuals with T2DM (mean age 56 ± 9 years; mean duration of diabetes 14.5 ± 7.9 years), 121 (24.1 %) had No-DR, 119 (28.3 %) had No-STDR and 200 (49.6 %) had STDR. Mean cystatin C level was significantly higher in individuals with STDR compared to those with no-STDR and No-DR (1.34 vs 1.06 vs 0.93 mg/L, p < 0.001). Cystatin C cut-off value ≥1.11 mg/L had a C statistic of 0.944 (95 % CI: 0.909-0.968, p < 0.001), 96.8 % sensitivity and 78.2 % specificity for detection of STDR.

CONCLUSION

Elevated serum cystatin C was strongly associated with STDR and could possibly be used as a biomarker for screening for sight-threatening diabetic retinopathy.

'I don`t need an eye check-up'. A qualitative study using a behavioural model to understand treatment-seeking behaviour of patients with sight threatening diabetic retinopathy (STDR) in India

Diabetic Retinopathy (DR) affects about 27% of patients with diabetes globally. According to the World Health Organization (WHO), DR is responsible for37 million cases of blindness worldwide. The SMART India study (October 2020-August 2021) documented the prevalence of diabetes, and DR in people40 years and above across ten Indian states and one Union Territory by conducting community screening. About 90% of people with sight threatening diabetic retinopathy (STDR) were referred from this screening study to eye hospitals for management, but failed to attend. This qualitative study, a component of the SMART India study, explored perceptions of referred patients regarding their susceptibility to eye related problems in diabetes and the benefits/barriers to seeking care. Perceived barriers from the viewpoint of ophthalmologists were also explored. Guided by the Health Beliefs Model (HBM), 20 semi structured interviews were carried out with consenting patients diagnosed with STDR. They included nine patients who had sought care recruited from eight eye hospitals across different states in India and eleven patients who did not seek care. Eleven ophthalmologists also participated. Four themes of analysis based on the HBM were, understanding of DR and its treatment, perceptions about susceptibility and severity, perceived barriers, perceived benefits and cues to action. Findings revealed poor understanding of the effects of diabetes on the eye contributing to low risk perception. Prohibitive costs of treatment, difficulties in accessing care services and poor social support were major barriers to seeking care. Ophthalmologists acknowledged that the absence of symptoms and the slow progressive nature of the disease deluded patients into thinking that they were fine. The study attests to the need for greater health literacy around diabetes, DR and STDR; for making treatment more affordable and accessible and for the development of effective patient education and communication strategies towards increasing compliance.