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Objective: There are very few reports on the prevalence of diabetic retinopathy (DR) in children and youth with type-1 diabetes (T1D). Studies have also found very low rates of referral for DR screening in children and youth with T1D. We conducted this study to determine the prevalence of DR, to study the reliability of ISPAD screening recommendations and to identify predictors of DR, its progression and regression in Indian children and youth with T1D. Methods: This study included 882 children and youth with T1D. Demographic data, anthropometry, blood pressure, sexual maturity rating, ophthalmological examination (slit lamp for cataract) and biochemical measurements were performed using standard protocols. Fundus images were captured using the Forus Health 3netra classic digital non-mydriatic fundus camera by the same experienced operator. De-identified images were assessed by a senior grader and ophthalmologist (Belfast Ophthalmic Reading Center). Severity of DR was graded as per the UK National Health Service (NHS) DR classification scale. Result: We report 6.4% and 0.2% prevalence of DR and cataract in Indian children and youth with T1D, respectively. All the subjects with DR had early non-proliferative DR. We report that amongst subjects with DR, only 2 subjects were aged less than 11 years and had duration of illness less than 2 years. Presence of hypertension and older age were significant predictors of DR (P <.05). Subjects with DR had significantly higher triglyceride concentrations (P <.05), of these, 6.9% had progression and 2.9% had regression at 1 year follow up; the change in glycaemic control was a significant positive predictor of progression of DR (P <.05). None of the participants included in the study progressed to develop sight-threatening DR. Conclusion: DR is not uncommon in Indian children and youth with T1D, thus screening for DR needs to be initiated early, particularly in older individuals with higher disease duration. Controlling blood pressure and triglyceride concentrations may prevent occurrence of DR. Improving glycaemic control may prevent progression of DR in Indian children and youth with T1D. Plain Language Summary: Diabetic retinopathy in Indian children with Type 1 Diabetes We found that 6.4% and 0.2% Indian children and youth with type-1 diabetes had diabetic retinopathy and cataract respectively. We report that amongst subjects with DR, only 2 subjects were aged less than 11 years and had duration of illness less than 2 years. Thus, International Society for Paediatric and Adolescent Diabetes (ISPAD) screening criteria must be implemented by all centres to avoid missing cases. Presence of high blood pressure, high triglyceride levels and older age were significant predictors of DR. Of the subjects with DR, 6.9% had progression and 2.9% had regression at 1 year follow up; the change in glycaemic control was a significant positive predictor of progression of DR. None of the participants included in the study progressed to develop sight-threatening DR. DR is not uncommon in Indian children and youth with T1D, hence, screening for DR needs to be initiated early, particularly in older individuals with higher disease duration. Controlling blood pressure and triglyceride concentrations may prevent occurrence of DR. Improving glycaemic control may prevent progression of DR in Indian children and youth with T1D. [ABSTRACT FROM AUTHOR]

Background: Visual impairment is a global problem and, regardless of the cause, it substantially impacts people's daily lives. Navigating towns and cities can be one of the most difficult tasks for someone with a visual impairment. This is because our streetscapes are often inaccessible for navigating safely and independently by people with a visual impairment. Barriers include street clutter, bollards, pavement parking, and shared spaces. Methodology: Participants with varying levels of diabetic retinopathy (DR) and retinitis pigmentosa (RP) were recruited. Each participant completed a clinical visit and a 1-mile walk. Participants discussed confidence, anxiety, difficulty, and any barriers encountered while completing the walkaround. Participants completed quality of life (RetDQol), diabetes distress scales, and a study questionnaire. They also underwent retinal imaging and visual function testing. Retinal imaging and visual function results were compared with confidence, difficulty, and anxiety levels during the walkaround using Spearman's correlation. Results: Thirty-three participants took part in the study, 22 with diabetes and 11 with RP. Results showed that average confidence was correlated with visual acuity, RetDQol, mean visual fields, and vertical peripheral diameter visual fields. Average difficulty was associated with visual acuity, RetDQol, dark adaptation, mean visual fields, percentage of the retina, and both horizontal and vertical diameter visual fields. In addition, some of the barriers discussed were pavement issues, bollards, parked cars, uneven pavements, alfresco dining, light levels, and street features such as tree roots, poles, A-boards, and street clutter. Conclusions: People with RP and treated DR faced common barriers while navigating the walkaround. The removal of these common barriers would make our streetscapes more accessible for all and will allow for more independence in those with visual impairments. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods: Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results: Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions: Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings. [ABSTRACT FROM AUTHOR]

Aims: To characterise continuous glucose monitor and flash glucose monitor use and related issues (both positive and negative) in the population of children and young people living with type 1 diabetes in the Southern Health and Social Care Trust (SHSCT), Northern Ireland. Methods: Four focus groups were conducted in the SHSCT with people with type 1 diabetes and their families, to assess opinions of diabetes technology. In addition, questionnaires were distributed to diabetes specialist nurses (DSNs), young people and their families about use and access to diabetes technology. Results: Questionnaires were completed by 68 children, young people (0–18 years) and their parents/guardians. Nearly all (98.5%) had access to their own diabetes related data, and 70.6% used diabetes data systems, for example, Libreview. Most of those using these systems found them beneficial and easy for daily use and clinical review. Most DSNs (83.9%) agreed that the systems were easy to use, and 82.3% were confident in interpreting the data accurately. DSNs felt virtual review was beneficial in over half of young people with 62.9% advising changes to diabetes management based on the data. Focus groups participants deemed diabetes technology a 'gamechanger' and 'lifechanging'. Some drawbacks included 'disruptive' alarms, self‐confidence issues, visible diabetes technology and problems in school. Conclusions: Most young people, their families and healthcare professionals reported they were confident in interpreting diabetes data and technology was easy to use. Diabetes technology could improve access to care through virtual clinics, improve clinical outcomes and enhance quality of life. [ABSTRACT FROM AUTHOR]