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Rani PK, Kalavalapalli D, Narayanan R, Kalavalapalli S, Narula R, Sahay RK, Deo S. SMART (artificial intelligence enabled) DROP (diabetic retinopathy outcomes and pathways): Study protocol for diabetic retinopathy management. PLoS One 2025; 20:e0324382. [PMID: 40388448 PMCID: PMC12088010 DOI: 10.1371/journal.pone.0324382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2024] [Accepted: 04/22/2025] [Indexed: 05/21/2025] Open
Abstract
INTRODUCTION Delayed diagnosis of diabetic retinopathy (DR) remains a significant challenge, often leading to preventable blindness and visual impairment. Given that physicians are frequently the first point of contact for people with diabetes, there is a critical need for integrated screening programs within diabetes clinics to enhance DR management and reduce the risk of severe vision loss. METHODS AND ANALYSIS We will conduct a prospective cohort study comparing (i) the intervention cohort, screened at diabetes clinics and referred to eye clinics per the proposed pathway, and (ii) the standard-of-care (SOC) eye clinic cohort. The study will be conducted in Hyderabad, India, at LV Prasad Eye Institute and four IDEA (Institute of Diabetes, Endocrinology, and Adiposity) Clinics. The primary objective is to evaluate the effectiveness of a systematic diabetic retinopathy screening program in achieving earlier detection and reducing visual impairment among People With Diabetes (PWD) attending IDEA clinics compared to routine care at eye care settings. The screening program will be operationalized using AI-enabled tools and supported by trained non-medical technicians. We will perform visual acuity tests and non-mydriatic fundus photography using AI-assisted cameras. DR-positive patients will be referred for treatment and follow-up. We aim to achieve high accuracy (>90%) in appropriate referral of DR and high screening coverage (>80%) of eligible PWD. Success metrics include screening uptake, AI diagnostic accuracy, referral rates, cost-effectiveness, patient satisfaction, follow-up adherence, and long-term outcomes. CONCLUSION This study aims to enhance diabetic retinopathy screening and management through an AI-enabled approach at diabetes clinics, improving early detection and care pathways. The findings will contribute to evidence-based strategies for optimizing DR screening and management, with results disseminated through peer-reviewed publications to inform policy and practice. TRIAL REGISTRATION Trial registration number: CTRI/2024/03/064518 [Registered on: 20/03/2024] (https://ctri.nic.in/).
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Affiliation(s)
- Padmaja Kumari Rani
- Department of Teleophthalmology, L V Prasad Eye Institute, Hyderabad, Telangana, India
- Anant Bajaj Retina Institute, L V Prasad Eye Institute, Hyderabad, Telangana, India
| | | | - Raja Narayanan
- Anant Bajaj Retina Institute, L V Prasad Eye Institute, Hyderabad, Telangana, India
| | - Shyam Kalavalapalli
- IDEA (Institute of Diabetes, Endocrinology, and Adiposity) Clinics, Hyderabad, Telangana, India
| | - Ritesh Narula
- Anant Bajaj Retina Institute, L V Prasad Eye Institute, Hyderabad, Telangana, India
- LILAC (L V Prasad eye institute Image Laboratory and Analysis Centre), Hyderabad, Telangana, India
| | - Rakesh K. Sahay
- IDEA (Institute of Diabetes, Endocrinology, and Adiposity) Clinics, Hyderabad, Telangana, India
- Osmania General Hospital, Hyderabad, Telangana, India
| | - Sarang Deo
- Max Institute of Health Care Management, Indian School of Business, Hyderabad, Telangana, India
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Scanlon PH, Norridge CFE, Prentis D, Holman N, Rankin P, Valabhji J. Effect of the COVID-19 pandemic on diabetic retinopathy and referral levels in the English National Health Service Diabetic Eye Screening Programme. Diabet Med 2025; 42:e15518. [PMID: 39901468 PMCID: PMC12006553 DOI: 10.1111/dme.15518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2024] [Revised: 11/16/2024] [Accepted: 01/17/2025] [Indexed: 02/05/2025]
Abstract
AIMS The aim was to determine the effect of the COVID-19 pandemic on diabetic retinopathy and referral rates in the English National Health Service (NHS) Diabetic Eye Screening Programme (DESP). METHODS Non-patient identifiable data are submitted centrally from the 57 regional centres in the NHS DESP on a quarterly basis and analysed using STATA, comparing 01/04/2019-31/03/2020 and 01/04/2021-31/03/2022. Patient characteristics were analysed from National Diabetes Audit (NDA) data. RESULTS There were 2,274,635 grades from the 57 centres in 2019-2020 and 2,199,623 grades in 2021-2022. The proportion of eyes with referable DR increased from 3.1% in 2019-2020 to 3.2% in the 2021-2022 NHS year (p < 0.01) with a small increase in the level of non-referable DR from 24.6% to 24.8% (p < 0.01). The median proportion of ungradable eyes in 2019-2020 was 2.6% (IQR: 2.3% to 3.3%) increasing to 3.1% (IQR: 2.5% to 3.7%) in 2021-2022. NDA data demonstrated that the proportions with type 1 diabetes receiving eye screening were higher in the latter year (8.3% vs. 7.3%). CONCLUSION The COVID-19 pandemic was associated with small increases in referable retinopathy rates from 3.1% to 3.2%, non-referable DR from 24·6% to 24.8% and an increase in the ungradable image rate from 2.6% to 3.1%, the latter increase possibly being caused by untreated cataract during the pandemic. Risk stratification of invitations in the recovery period was believed to have contributed to keeping the referable rates low and supports a similar approach in extension of the screening interval for low-risk groups.
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Affiliation(s)
- P. H. Scanlon
- Gloucestershire Retinal Research Group (GRRG)Gloucestershire Hospitals NHS Foundation TrustCheltenhamUK
- Nuffield Department of Clinical NeuroscienceUniversity of OxfordOxfordUK
- University of GloucestershireCheltenhamUK
| | - C. F. E. Norridge
- Gloucestershire Retinal Research Group (GRRG)Gloucestershire Hospitals NHS Foundation TrustCheltenhamUK
| | | | - N. Holman
- Department of Epidemiology and Bio‐StatisticsImperial CollegeLondonUK
- School of Population HealthRoyal College of Surgeons of IrelandDublinIreland
| | | | - J. Valabhji
- NHS EnglandLondonUK
- Division of Metabolism, Digestion and Reproduction, Imperial College LondonChelsea and Westminster Hospital CampusLondonUK
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Macdonald T, Zhelev Z, Liu X, Hyde C, Fajtl J, Egan C, Tufail A, Rudnicka AR, Shinkins B, Given-Wilson R, Dunbar JK, Halligan S, Scanlon P, Mackie A, Taylor-Philips S, Denniston AK. Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee. Lancet Digit Health 2025; 7:100840. [PMID: 40185647 DOI: 10.1016/j.landig.2024.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 07/22/2024] [Accepted: 12/11/2024] [Indexed: 04/07/2025]
Abstract
Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with diabetes, because of early detection and treatment of sight-threatening disease. There is long-standing interest in the possibility of automating parts of this process through artificial intelligence, commonly known as automated retinal imaging analysis software (ARIAS). A number of such products are now on the market. In the UK, Scotland has used a rules-based autograder since 2011, but the diabetic eye screening programmes in the rest of the UK rely solely on human graders. With more sophisticated machine learning-based ARIAS now available and greater challenges in terms of human grader capacity, in 2019 the UK's National Screening Committee (NSC) was asked to consider the modification of diabetic eye screening in England with ARIAS. Following up on a review of ARIAS research highlighting the strengths and limitations of existing evidence, the NSC here sets out their considerations for evaluating evidence to support the introduction of ARIAS into the diabetic eye screening programme.
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Affiliation(s)
- Trystan Macdonald
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHSFT, Birmingham, UK
| | - Zhivko Zhelev
- Exeter Test Group, College of Medicine and Health, University of Exeter Medical School, Exeter, UK
| | - Xiaoxuan Liu
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHSFT, Birmingham, UK
| | - Christopher Hyde
- Exeter Test Group, College of Medicine and Health, University of Exeter Medical School, Exeter, UK
| | - Jiri Fajtl
- School of Computer Science and Mathematics, Kingston University London, London, UK
| | - Catherine Egan
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London, UK
| | - Adnan Tufail
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, London, UK
| | - Alicja R Rudnicka
- Population Health Research Institute, St George's University of London, London, UK
| | | | | | - J Kevin Dunbar
- Vaccination and Screening Directorate, NHS England, London, UK
| | - Steve Halligan
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Peter Scanlon
- Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK
| | - Anne Mackie
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Sian Taylor-Philips
- Warwick Medical School, University of Warwick, Coventry, UK; UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Alastair K Denniston
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK; NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHSFT, Birmingham, UK.
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Yamamoto K, Ihana‐Sugiyama N, Sugiyama T, Yamaoka T, Wakui‐Kimura A, Imai K, Kuroda N, Ohsugi M, Ueki K, Yamauchi T, Tamiya N. Recognition of ophthalmology consultation and fundus examination among individuals with diabetes in Japan: A cross-sectional study using claims-questionnaire linked data. Diabetes Obes Metab 2025; 27:1762-1772. [PMID: 39887521 PMCID: PMC11885107 DOI: 10.1111/dom.16164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2024] [Revised: 12/09/2024] [Accepted: 12/17/2024] [Indexed: 02/01/2025]
Abstract
AIMS This study aimed to assess the relationship between individuals' recognition of ophthalmology consultation recommendations, their knowledge of the recommended frequency of diabetic retinopathy screening, and the likelihood of undergoing fundus examinations. MATERIALS AND METHODS This cross-sectional secondary analysis linked claims and health checkup data to a questionnaire survey. Questionnaires were distributed to randomly sampled National Health Insurance beneficiaries in Tsukuba City, using data from claims and health checkups. Weighting was applied based on sample extraction and response rates. We calculated the proportions of fundus examinations and knowledge of screening frequency according to the recognition of ophthalmology consultation recommendations. The association between visits to medical facilities with diabetes specialists and diabetic retinopathy screening was also examined. RESULTS Among 290 participants, 47.6% recognized ophthalmology consultation recommendations. Those who recognized these recommendations had better knowledge of the screening frequency (93.4% vs. 49.6%) and were more likely to undergo fundus examinations (72.9% vs. 30.1%; adjusted risk ratio 2.36; 95% CI, 1.65-3.38). Participants who visited medical facilities with diabetes specialists were more likely to recognize recommendations, have knowledge of screening frequency, and undergo fundus examinations. CONCLUSIONS Recognition of ophthalmology consultation recommendations was associated with better knowledge of screening frequency and higher participation in fundus examinations. Increasing awareness through healthcare provider recommendations may improve diabetic retinopathy screening rates, highlighting the need for targeted interventions to promote eye care among individuals with diabetes.
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Affiliation(s)
- Kouko Yamamoto
- Department of Health Services Research, Graduate School of Comprehensive Human SciencesUniversity of TsukubaTsukuba, IbarakiJapan
- Department of Health Services Research, Institute of MedicineUniversity of TsukubaTsukuba, IbarakiJapan
- Diabetes and Metabolism Information Center, Research InstituteNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
| | - Noriko Ihana‐Sugiyama
- Diabetes and Metabolism Information Center, Research InstituteNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
- Department of Diabetes, Endocrinology, and MetabolismNational Center for Global and Medicine HospitalShinjuku‐Ku, TokyoJapan
- Health Services Research and Development CenterUniversity of TsukubaTsukuba, IbarakiJapan
| | - Takehiro Sugiyama
- Department of Health Services Research, Institute of MedicineUniversity of TsukubaTsukuba, IbarakiJapan
- Diabetes and Metabolism Information Center, Research InstituteNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
- Health Services Research and Development CenterUniversity of TsukubaTsukuba, IbarakiJapan
- Institute for Global Health Policy Research, Bureau of International Health CooperationNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
| | - Takuya Yamaoka
- Department of Health Services Research, Graduate School of Comprehensive Human SciencesUniversity of TsukubaTsukuba, IbarakiJapan
- Department of Health Services Research, Institute of MedicineUniversity of TsukubaTsukuba, IbarakiJapan
- Diabetes and Metabolism Information Center, Research InstituteNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
| | - Akiko Wakui‐Kimura
- Diabetes and Metabolism Information Center, Research InstituteNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
- Health Services Research and Development CenterUniversity of TsukubaTsukuba, IbarakiJapan
| | - Kenjiro Imai
- Diabetes and Metabolism Information Center, Research InstituteNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
- Health Services Research and Development CenterUniversity of TsukubaTsukuba, IbarakiJapan
| | - Naoaki Kuroda
- Health Services Research and Development CenterUniversity of TsukubaTsukuba, IbarakiJapan
- Health Department of Tsukuba CityTsukuba, IbarakiJapan
- Department of Public Mental Health ResearchNational Institute of Mental Health, National Center of Neurology and PsychiatryKodaira, TokyoJapan
| | - Mitsuru Ohsugi
- Diabetes and Metabolism Information Center, Research InstituteNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
- Department of Diabetes, Endocrinology, and MetabolismNational Center for Global and Medicine HospitalShinjuku‐Ku, TokyoJapan
| | - Kohjiro Ueki
- Department of Diabetes, Endocrinology, and MetabolismNational Center for Global and Medicine HospitalShinjuku‐Ku, TokyoJapan
- Diabetes Research Center, Research InstituteNational Center for Global Health and MedicineShinjuku‐Ku, TokyoJapan
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of MedicineThe University of TokyoBunkyo‐ku, TokyoJapan
| | - Nanako Tamiya
- Department of Health Services Research, Institute of MedicineUniversity of TsukubaTsukuba, IbarakiJapan
- Health Services Research and Development CenterUniversity of TsukubaTsukuba, IbarakiJapan
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Poh SSJ, Teo KYC, Goh RA, Lee QX, Hamzah H, Sim SSC, Tan CS, Tan NC, Wong TY, Tan GSW. Predicting the need for diabetic macular oedema treatment from photographic screening in the Singapore Integrated Diabetic Retinopathy Programme (SiDRP). Eye (Lond) 2025:10.1038/s41433-025-03725-1. [PMID: 40021782 DOI: 10.1038/s41433-025-03725-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Revised: 02/07/2025] [Accepted: 02/12/2025] [Indexed: 03/03/2025] Open
Abstract
OBJECTIVE To identify diabetic maculopathy features from photographic screening that are predictive of treatment on referral to a tertiary care centre. METHODS Retrospective review of participants who underwent screening by Singapore Integrated Diabetic Retinopathy Programme from 2015 to 2019. Participants underwent visual acuity (VA) test and non-stereoscopic retinal photographs. Maculopathy features include haemorrhages, microaneurysm and hard exudates (HE), stratified by inner and outer zone (1 and 1-2 disc diameter from fovea respectively) and VA of 6/12. Diabetic macular oedema (DMO) treatment was defined as intravitreal injection or macular photocoagulation up to 540 days from point of referral. RESULTS 16,712 patients screened had referable eye disease. Out of 3518 maculopathy suspects, 281 (8.0%) received DMO treatment within 540 days. Those treated for DMO had shorter duration of diabetes (6.90 vs. 9.13 years, p < 0.001), higher total cholesterol (4.65 ± 1.20 vs. 4.36 ± 1.13 mmol/L, p = 0.001) and LDL cholesterol (2.59 ± 1.05 vs. 2.37 ± 0.93 mmol/L, p < 0.05) than those without treatment. High-risk features, including inner zone haemorrhages with VA ≤ 6/12 (HR 12.0, 95% CI: 5.5-25.9) and inner zone hard exudates (HR 7.4, 95% CI: 3.4-15.8), significantly increased the likelihood of requiring DMO treatment compared to low-risk features. Higher body mass index is protective of DMO treatment in mild non-proliferative diabetic retinopathy (HR 0.84, 95% CI: 0.73-0.97). CONCLUSION Haemorrhages, microaneurysms and HE within inner zone are important photographic features predictive of DMO treatment. VA is an important stratification for screening especially in patients with only visible haemorrhages.
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Affiliation(s)
| | | | - Rose Ann Goh
- SNEC Ocular Reading Centre, Singapore National Eye Centre, Singapore, Singapore
| | - Qian Xin Lee
- SNEC Ocular Reading Centre, Singapore National Eye Centre, Singapore, Singapore
| | - Haslina Hamzah
- SNEC Ocular Reading Centre, Singapore National Eye Centre, Singapore, Singapore
| | - Serene S C Sim
- SNEC Ocular Reading Centre, Singapore National Eye Centre, Singapore, Singapore
| | - Colin S Tan
- Duke-NUS Medical School, National University of Singapore, Ophthalmology and Visual Sciences Academic Clinical Programme, Singapore, Singapore
- Ophthalmology, Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore, Singapore
| | - Ngiap Chuan Tan
- Outram Polyclinic, SingHealth Polyclinics, Singapore, Singapore
| | - Tien Yin Wong
- Singapore National Eye Centre, Singapore, Singapore
- Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Ophthalmology and Visual Sciences Academic Clinical Programme, Singapore, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, People's Republic of China
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, People's Republic of China
| | - Gavin S W Tan
- Singapore National Eye Centre, Singapore, Singapore.
- Singapore Eye Research Institute, Singapore, Singapore.
- SNEC Ocular Reading Centre, Singapore National Eye Centre, Singapore, Singapore.
- Duke-NUS Medical School, National University of Singapore, Ophthalmology and Visual Sciences Academic Clinical Programme, Singapore, Singapore.
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Ha SK, Gilbert JB, Le E, Ross C, Lorch A. Impact of teleretinal screening program on diabetic retinopathy screening compliance rates in community health centers: a quasi-experimental study. BMC Health Serv Res 2025; 25:318. [PMID: 40011921 PMCID: PMC11863591 DOI: 10.1186/s12913-025-12472-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2024] [Accepted: 02/24/2025] [Indexed: 02/28/2025] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) remains a leading cause of preventable blindness, with inadequate screening rates even in urban areas with high concentrations of medical professionals. While medical guidelines recommend annual diabetic retinopathy screening for patients with diabetes mellitus, adherence to these recommendations remains low. This study evaluates the impact of a novel teleretinal DR screening program on screening compliance across urban community health centers in Boston, Massachusetts. METHODS We conducted a quasi-experimental study comparing DR screening compliance between intervention and comparison community health centers before and after implementing a teleretinal screening program. Participants included patients diagnosed with diabetes mellitus with primary care providers at the studied sites. We defined compliance as completion of either teleretinal screening or a documented eye care professional examination within the previous 365 days. Monthly compliance rates were analyzed using two-way fixed effects regression and event study techniques. RESULTS The study included 10,247 patients with diabetes mellitus who received care at six participating sites, generating 222 monthly compliance rate estimates. Baseline compliance rates before implementation ranged from 25 to 40% across sites. The two-way fixed effects regression analysis revealed that the screening program significantly increased DR compliance rates by an average of 7.2% points (p < 0.001). Event study analysis showed positive effects across all sites, though the initial improvement tended to diminish over time. CONCLUSIONS Implementation of a community-based teleretinal DR screening program significantly improved compliance with annual screening guidelines in urban communities. These findings support the broader adoption of teleretinal screening as an effective strategy for preventing DR-related vision loss in vulnerable populations. Further research is needed to assess long-term clinical outcomes and optimize program sustainability.
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Affiliation(s)
- Sierra K Ha
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Joshua B Gilbert
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Erin Le
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Connor Ross
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Alice Lorch
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
- Mass General Hospital/Mass Eye and Ear, 243 Charles Street, Boston, MA, 02114, USA.
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Pardhan S, Wijewickrama RCA, Gilbert CE, Piyasena MP, Sapkota R. Impact of COVID-19 and recovery of routine diabetic retinopathy digital screening across different regions in England: an analysis of publicly available data. BMJ Open 2024; 14:e089710. [PMID: 39732486 PMCID: PMC11683968 DOI: 10.1136/bmjopen-2024-089710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 12/02/2024] [Indexed: 12/30/2024] Open
Abstract
OBJECTIVE This study aims to examine the reduction and subsequent recovery of routine digital screening (RDS) uptake in England from 2018 to 2022, exploring national, regional and individual Diabetic Eye Screening Programme (DESP) levels. The COVID-19 lockdown in most areas of England was from 26 March 2020 to 23 June 2020 (first national lockdown), 5 November 2020 to 2 December 2020 (second national lockdown) and 6 January 2021 to 8 March 2021 (third national lockdown). DESIGN Retrospective data analysis. SETTING DESPs of England. PARTICIPANTS Individuals with diabetes who were invited to take part in the DESP programmes. METHODS Publicly available data from Public Health England (2018-2019) and National Health Service England (2019-2022) were examined to identify the rate of uptake (proportion of those who attended the DESPs to those who were invited) of RDS at national and regional levels and by each DESP in England. PRIMARY OUTCOME MEASURES Rate of uptake of RDS. RESULTS The national uptake of RDS decreased from 82% (2019-2020) to 68% (2020-2021) and then increased to 78% (2021-2022). At the regional level, the sharpest drop was in the Midlands which decreased from 79% (2019-2020) to 53% (2020-2021), increasing to 73% (2021-2022) but did not reach pre-COVID-19 levels. At individual DESP levels across England, the greatest drop in attendance (2020-2021) was recorded in Derbyshire (79% to 45%), Barnsley and Rotherham (78% to 45%) and Arden, Herefordshire and Worcestershire (78% to 46%). Although these DESPs showed an increase in 2021-2022 of 33%, 21% and 31%, they did not reach prepandemic (2018-2019) rates of 81%, 85% and 82%, respectively. Data suggest that West Sussex, East Sussex and East and North Hertfordshire DESPs maintained relatively higher uptake rates (86%-89%) in 2020-2021. CONCLUSION COVID-19 had an impact on England's diabetic eye screening attendance, with notable variations across regions and DESPs. Different regions and DESPs showed variable post-COVID-19 recovery. More importantly, what was not evident is the increased uptake that should have occurred after the COVID-19 lockdown to compensate for the low uptake during the lockdown. In some areas, addressing some of the barriers that affect retinal screening uptake may improve future attendance.
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Affiliation(s)
- Shahina Pardhan
- Vision and Eye Research Institute, School of Medicine, Anglia Ruskin University, Cambridge, UK
- Centre for Inclusive Community Eye Health, School of Medicine, Anglia Ruskin University, Cambridge, UK
| | - Rumalie Chanika Alwis Wijewickrama
- Vision and Eye Research Institute, School of Medicine, Anglia Ruskin University, Cambridge, UK
- Centre for Inclusive Community Eye Health, School of Medicine, Anglia Ruskin University, Cambridge, UK
| | - Clare E Gilbert
- Clinical Research Unit, ITD, London School of Hygiene and Tropical Medicine, London, UK
| | - Mapa Prabhath Piyasena
- Vision and Eye Research Institute, School of Medicine, Anglia Ruskin University, Cambridge, UK
- Centre for Inclusive Community Eye Health, School of Medicine, Anglia Ruskin University, Cambridge, UK
| | - Raju Sapkota
- Vision and Eye Research Institute, School of Medicine, Anglia Ruskin University, Cambridge, UK
- Centre for Inclusive Community Eye Health, School of Medicine, Anglia Ruskin University, Cambridge, UK
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Sloan G, Dela Pena P, Andag-Silva A, Cunanan E, Jimeno C, Robles JJ, Tesfaye S. Sheffield One-Stop Service: A potential model to improve the screening uptake of diabetic peripheral neuropathy and other microvascular complications of diabetes. J Diabetes Investig 2024; 15:1355-1362. [PMID: 39037334 PMCID: PMC11442755 DOI: 10.1111/jdi.14268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/23/2024] Open
Abstract
The world is experiencing an enormous rise in the prevalence of diabetes, which is associated with massive healthcare costs that threaten to overwhelm many healthcare systems. Most of the diabetes expenditure is attributed to the management of chronic diabetes complications, including diabetic peripheral neuropathy (DPN)/diabetic foot complications, chronic kidney disease, sight-threatening retinopathy and cardiovascular diseases. Of these complications, the most overlooked is DPN. Most consultations around the world do not even involve taking off shoes and socks to carry out a foot examination, and even when carried out, the peripheral neurological examination using the 10-g monofilament diagnoses DPN when it is already at an advanced stage. Thus, all too often diabetes complications are diagnosed late, resulting in devastating outcomes, particularly in low- to middle-income countries. There is, therefore, an urgent need to instigate new strategies to improve microvascular screening uptake using a holistic protocol for annual diabetes health checks outside the busy diabetes clinic. One such approach, the Sheffield One-Stop Microvascular Screening Service, which involves modern point of care devices to diagnose DPN, has been shown to be feasible and effective, resulting in high uptake and early management of diabetes complications. This article outlines the advantages of this One-Stop Microvascular Screening Service and a plan to trial an adapted version of this service to a resource-limited country, the Philippines. If successful, this model has the potential for implementation in other countries around the world.
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Affiliation(s)
- Gordon Sloan
- Division of Clinical Medicine, University of Sheffield, Sheffield, UK
- Diabetes Research Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Pepito Dela Pena
- Section of Endocrinology, Diabetes and Metabolism, East Avenue Medical Center, Quezon City, Philippines
| | - Aimee Andag-Silva
- Section of Endocrinology and Diabetes, De La Salle University Medical Center, Cavite, Philippines
| | - Elaine Cunanan
- Section of Endocrinology, Diabetes and Metabolism, University of St. Tomas Hospital, Manila, Philippines
| | - Cecilia Jimeno
- Section of Endocrinology, Diabetes and Metabolism, University of the Philippines, Philippine General Hospital, Manila, Philippines
| | - Jeremy Jones Robles
- Section of Endocrinology, Diabetes and Metabolism, Chong Hua Hospital, Cebu, Philippines
| | - Solomon Tesfaye
- Diabetes Research Unit, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
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Li J, Guan Z, Wang J, Cheung CY, Zheng Y, Lim LL, Lim CC, Ruamviboonsuk P, Raman R, Corsino L, Echouffo-Tcheugui JB, Luk AOY, Chen LJ, Sun X, Hamzah H, Wu Q, Wang X, Liu R, Wang YX, Chen T, Zhang X, Yang X, Yin J, Wan J, Du W, Quek TC, Goh JHL, Yang D, Hu X, Nguyen TX, Szeto SKH, Chotcomwongse P, Malek R, Normatova N, Ibragimova N, Srinivasan R, Zhong P, Huang W, Deng C, Ruan L, Zhang C, Zhang C, Zhou Y, Wu C, Dai R, Koh SWC, Abdullah A, Hee NKY, Tan HC, Liew ZH, Tien CSY, Kao SL, Lim AYL, Mok SF, Sun L, Gu J, Wu L, Li T, Cheng D, Wang Z, Qin Y, Dai L, Meng Z, Shu J, Lu Y, Jiang N, Hu T, Huang S, Huang G, Yu S, Liu D, Ma W, Guo M, Guan X, Yang X, Bascaran C, Cleland CR, Bao Y, Ekinci EI, Jenkins A, Chan JCN, Bee YM, Sivaprasad S, Shaw JE, Simó R, Keane PA, Cheng CY, Tan GSW, Jia W, Tham YC, Li H, Sheng B, Wong TY. Integrated image-based deep learning and language models for primary diabetes care. Nat Med 2024; 30:2886-2896. [PMID: 39030266 PMCID: PMC11485246 DOI: 10.1038/s41591-024-03139-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 06/18/2024] [Indexed: 07/21/2024]
Abstract
Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.
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Affiliation(s)
- Jiajia Li
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhouyu Guan
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Jing Wang
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Cynthia Ciwei Lim
- Department of Renal Medicine, Singapore General Hospital, SingHealth-Duke Academic Medical Centre, Singapore, Singapore
| | - Paisan Ruamviboonsuk
- Faculty of Medicine, Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Leonor Corsino
- Department of Medicine, Division of Endocrinology, Metabolism and Nutrition, and Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Justin B Echouffo-Tcheugui
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Andrea O Y Luk
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Asia Diabetes Foundation, Hong Kong Special Administrative Region, China
| | - Li Jia Chen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Qiang Wu
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruhan Liu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China
| | - Tingli Chen
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Xiao Zhang
- The People's Hospital of Sixian County, Anhui, China
| | - Xiaolong Yang
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Jun Yin
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Jing Wan
- Department of Endocrinology and Metabolism, Shanghai Eighth People's Hospital, Shanghai, China
| | - Wei Du
- Department of Endocrinology and Metabolism, Shanghai Eighth People's Hospital, Shanghai, China
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Jocelyn Hui Lin Goh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiaoyan Hu
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Truong X Nguyen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Simon K H Szeto
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peranut Chotcomwongse
- Faculty of Medicine, Department of Ophthalmology, Rajavithi Hospital, College of Medicine, Rangsit University, Bangkok, Thailand
| | - Rachid Malek
- Department of Internal Medicine, Setif University Ferhat Abbas, Setif, Algeria
| | - Nargiza Normatova
- Ophthalmology Department at Tashkent Advanced Training Institute for Doctors, Tashkent, Uzbekistan
| | - Nilufar Ibragimova
- Charity Union of Persons with Disabilities and People with Diabetes UMID, Tashkent, Uzbekistan
| | - Ramyaa Srinivasan
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Pingting Zhong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Wenyong Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Chenxin Deng
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lei Ruan
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cuntai Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chenxi Zhang
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Zhou
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chan Wu
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Rongping Dai
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Sky Wei Chee Koh
- National University Polyclinics, National University Health System, Department of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Adina Abdullah
- Department of Primary Care Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia
| | | | - Hong Chang Tan
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Zhong Hong Liew
- Department of Renal Medicine, Singapore General Hospital, SingHealth-Duke Academic Medical Centre, Singapore, Singapore
| | - Carolyn Shan-Yeu Tien
- Department of Renal Medicine, Singapore General Hospital, SingHealth-Duke Academic Medical Centre, Singapore, Singapore
| | - Shih Ling Kao
- Division of Endocrinology, University Medicine Cluster, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Amanda Yuan Ling Lim
- Division of Endocrinology, University Medicine Cluster, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Shao Feng Mok
- Division of Endocrinology, University Medicine Cluster, National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Lina Sun
- Department of Internal Medicine, Huadong Sanatorium, Wuxi, China
| | - Jing Gu
- Department of Internal Medicine, Huadong Sanatorium, Wuxi, China
| | - Liang Wu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Tingyao Li
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Di Cheng
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Zheyuan Wang
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yiming Qin
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Dai
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ziyao Meng
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jia Shu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yuwei Lu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Nan Jiang
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Tingting Hu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Shan Huang
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Gengyou Huang
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shujie Yu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Dan Liu
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Weizhi Ma
- Institute for AI Industry Research, Tsinghua University, Beijing, China
| | - Minyi Guo
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Xinping Guan
- Department of Automation and the Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Covadonga Bascaran
- International Centre for Eye Health, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - Charles R Cleland
- International Centre for Eye Health, London School of Hygiene and Tropical Medicine, University of London, London, UK
| | - Yuqian Bao
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Elif I Ekinci
- Department of Endocrinology, Austin Health, Melbourne, Victoria, Australia
- Department of Medicine, The University of Melbourne (Austin Health), Melbourne, Victoria, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Parkville, Victoria, Australia
| | - Alicia Jenkins
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Parkville, Victoria, Australia
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, New South Wales, Australia
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
- Asia Diabetes Foundation, Hong Kong Special Administrative Region, China
| | - Yong Mong Bee
- Department of Endocrinology, Singapore General Hospital, Singapore, Singapore
| | - Sobha Sivaprasad
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
| | - Jonathan E Shaw
- Department of Medicine, The University of Melbourne (Austin Health), Melbourne, Victoria, Australia
| | - Rafael Simó
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
- Diabetes and Metabolism Research Unit, Vall d'Hebron Research Institut, Autonomous University of Barcelona, Barcelona, Spain
| | - Pearse A Keane
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Center for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Weiping Jia
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Center for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore.
| | - Huating Li
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
| | - Bin Sheng
- Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China.
- Beijing Tsinghua Changgung Hospital, Beijing, China.
- Zhongshan Ophthalmic Center, Guangzhou, China.
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Liu M, Li Z, Zhang H, Cao T, Feng X, Wang X, Wang Z. Inhibition of BMP4 alleviates diabetic retinal vascular dysfunction via the VEGF and smad1/5 signalling. Arch Physiol Biochem 2024; 130:529-536. [PMID: 37074680 DOI: 10.1080/13813455.2023.2190054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 12/25/2022] [Accepted: 03/01/2023] [Indexed: 04/20/2023]
Abstract
Objective:The aim of our study was to determine the molecular mechanism of BMP4 (bone morphogenetic protein 4) in DR (diabetic retinopathy).Methods: Human retinal endothelial cell (HRECs) induced by high glucose to simulate one of the pathogenesis in the diabetic retinopathy (DR) model. RT-qPCR and western blot were used to detect the mRNA and protein levels of BMP4 in the STZ/HG group. Flow cytometry and TUNEL staining were performed to detect the apoptosis. Angiogenesis was evaluated by tube formation assay. Transwell assay and wound healing assay were used to detect cell migration ability. H&E staining was used to evaluate the pathological changes.Results: BMP4 was significantly upregulated in the STZ/HG group. Sh-BMP4 significantly inhibited the migration and angiogenesis of RVECs induced by HG. In addition, both in vivo and in vitro experiments confirmed that sh-BMP4 could significantly promote RVECs apoptosis in the HG/STZ group. Western blot results showed that sh-BMP4 could down-regulate the expressions of p-smad1, p-smad5 and VEGF.Conclusions: Inhibition of BMP4 could alleviate the damage of diabetic retinopathy by regulating the p-smad1/5/VEGF signaling axis, inhibiting angiogenesis and promoting apoptosis.
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Affiliation(s)
- Mingyuan Liu
- Anesthesiology Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, P.R. China
| | - Zhaoxia Li
- Ophthalmology Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, P.R. China
| | - Huiqin Zhang
- Ophthalmology Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, P.R. China
| | - Tingting Cao
- Ophthalmology Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, P.R. China
| | - Xueyan Feng
- Ophthalmology Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, P.R. China
| | - Xi Wang
- Pneumology Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, P.R. China
| | - Zhixue Wang
- Ophthalmology Department, Cangzhou Central Hospital, Cangzhou, Hebei Province, P.R. China
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11
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Nderitu P, Nunez do Rio JM, Webster L, Mann S, Cardoso MJ, Modat M, Hopkins D, Bergeles C, Jackson TL. Predicting 1, 2 and 3 year emergent referable diabetic retinopathy and maculopathy using deep learning. COMMUNICATIONS MEDICINE 2024; 4:167. [PMID: 39169209 PMCID: PMC11339445 DOI: 10.1038/s43856-024-00590-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 08/07/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Predicting diabetic retinopathy (DR) progression could enable individualised screening with prompt referral for high-risk individuals for sight-saving treatment, whilst reducing screening burden for low-risk individuals. We developed and validated deep learning systems (DLS) that predict 1, 2 and 3 year emergent referable DR and maculopathy using risk factor characteristics (tabular DLS), colour fundal photographs (image DLS) or both (multimodal DLS). METHODS From 162,339 development-set eyes from south-east London (UK) diabetic eye screening programme (DESP), 110,837 had eligible longitudinal data, with the remaining 51,502 used for pretraining. Internal and external (Birmingham DESP, UK) test datasets included 27,996, and 6928 eyes respectively. RESULTS Internal multimodal DLS emergent referable DR, maculopathy or either area-under-the receiver operating characteristic (AUROC) were 0.95 (95% CI: 0.92-0.98), 0.84 (0.82-0.86), 0.85 (0.83-0.87) for 1 year, 0.92 (0.87-0.96), 0.84 (0.82-0.87), 0.85 (0.82-0.87) for 2 years, and 0.85 (0.80-0.90), 0.79 (0.76-0.82), 0.79 (0.76-0.82) for 3 years. External multimodal DLS emergent referable DR, maculopathy or either AUROC were 0.93 (0.88-0.97), 0.85 (0.80-0.89), 0.85 (0.76-0.85) for 1 year, 0.93 (0.89-0.97), 0.79 (0.74-0.84), 0.80 (0.76-0.85) for 2 years, and 0.91 (0.84-0.98), 0.79 (0.74-0.83), 0.79 (0.74-0.84) for 3 years. CONCLUSIONS Multimodal and image DLS performance is significantly better than tabular DLS at all intervals. DLS accurately predict 1, 2 and 3 year emergent referable DR and referable maculopathy using colour fundal photographs, with additional risk factor characteristics conferring improvements in prognostic performance. Proposed DLS are a step towards individualised risk-based screening, whereby AI-assistance allows high-risk individuals to be closely monitored while reducing screening burden for low-risk individuals.
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Affiliation(s)
- Paul Nderitu
- Section of Ophthalmology, Faculty of Life Sciences and Medicine, King's College London, London, UK.
- Department of Ophthalmology, King's Ophthalmology Research Unit (KORU), King's College Hospital, London, UK.
| | - Joan M Nunez do Rio
- Department of Ophthalmology, King's Ophthalmology Research Unit (KORU), King's College Hospital, London, UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Laura Webster
- Department of Ophthalmology, South East London Diabetic Eye Screening Service, St Thomas' Hospital, London, UK
| | - Samantha Mann
- Department of Ophthalmology, South East London Diabetic Eye Screening Service, St Thomas' Hospital, London, UK
| | - M Jorge Cardoso
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - David Hopkins
- Institute of Diabetes, Endocrinology and Obesity, King's Health Partners, London, UK
| | - Christos Bergeles
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Timothy L Jackson
- Section of Ophthalmology, Faculty of Life Sciences and Medicine, King's College London, London, UK
- Department of Ophthalmology, King's Ophthalmology Research Unit (KORU), King's College Hospital, London, UK
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12
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Vision Loss Expert Group of the Global Burden of Disease Study, Curran K, Peto T, Jonas JB, Friedman D, Kim JE, Leasher J, Tapply I, Fernandes AG, Cicinelli MV, Arrigo A, Leveziel N, Resnikoff S, Taylor HR, Sedighi T, Flaxman S, Bikbov MM, Braithwaite T, Bron A, Cheng CY, Del Monte MA, Ehrlich JR, Furtado JM, Gazzard G, Hartnett ME, Kahloun R, Kempen JH, Khairallah M, Khanna RC, Lansingh VC, Naidoo KS, Nangia V, Nowak M, Pesudovs K, Ramulu P, Topouzis F, Tsilimbaris M, Wang YX, Wang N, Bourne RRA, the GBD 2019 Blindness and Vision Impairment Collaborators, Curran K, Peto T, Bourne R, Leasher JL, Jonas JB, Friedman DS, Kim JE, Fernandes AG, Ahinkorah BO, Ahmadieh H, Ahmed A, Alfaar AS, Almidani L, Amu H, Androudi S, Arabloo J, Aravkin AY, Asemu MT, Azzam AY, Baghcheghi N, Bailey F, Baran MF, Bardhan M, Bärnighausen TW, Barrow A, Bhardwaj P, Bikbov M, Braithwaite T, Briant PS, Burkart K, Cámera LA, Coberly K, Dadras O, Dai X, Dehghan A, Demessa BH, Diress M, Do TC, Do THP, Dokova KG, Duncan BB, Ekholuenetale M, Elhadi M, Emamian MH, Emamverdi M, Farrokhpour H, Fatehizadeh A, Desideri LF, Furtado JM, Gebrehiwot M, Ghassemi F, Gudeta MD, Gupta S, Gupta VB, Gupta VK, Hammond BR, Harorani M, Hasani H, Heidari G, et alVision Loss Expert Group of the Global Burden of Disease Study, Curran K, Peto T, Jonas JB, Friedman D, Kim JE, Leasher J, Tapply I, Fernandes AG, Cicinelli MV, Arrigo A, Leveziel N, Resnikoff S, Taylor HR, Sedighi T, Flaxman S, Bikbov MM, Braithwaite T, Bron A, Cheng CY, Del Monte MA, Ehrlich JR, Furtado JM, Gazzard G, Hartnett ME, Kahloun R, Kempen JH, Khairallah M, Khanna RC, Lansingh VC, Naidoo KS, Nangia V, Nowak M, Pesudovs K, Ramulu P, Topouzis F, Tsilimbaris M, Wang YX, Wang N, Bourne RRA, the GBD 2019 Blindness and Vision Impairment Collaborators, Curran K, Peto T, Bourne R, Leasher JL, Jonas JB, Friedman DS, Kim JE, Fernandes AG, Ahinkorah BO, Ahmadieh H, Ahmed A, Alfaar AS, Almidani L, Amu H, Androudi S, Arabloo J, Aravkin AY, Asemu MT, Azzam AY, Baghcheghi N, Bailey F, Baran MF, Bardhan M, Bärnighausen TW, Barrow A, Bhardwaj P, Bikbov M, Braithwaite T, Briant PS, Burkart K, Cámera LA, Coberly K, Dadras O, Dai X, Dehghan A, Demessa BH, Diress M, Do TC, Do THP, Dokova KG, Duncan BB, Ekholuenetale M, Elhadi M, Emamian MH, Emamverdi M, Farrokhpour H, Fatehizadeh A, Desideri LF, Furtado JM, Gebrehiwot M, Ghassemi F, Gudeta MD, Gupta S, Gupta VB, Gupta VK, Hammond BR, Harorani M, Hasani H, Heidari G, Hosseinzadeh M, Huang JJ, Islam SMS, Javadi N, Jimenez-Corona A, Jokar M, Joshua CE, Kadashetti V, Kandel H, Kasraei H, Kaur RJ, Khanal S, Khorrami Z, Koohestani HR, Krishan K, Lim SS, El Razek MMA, Mansouri V, Maugeri A, Mestrovic T, Misganaw A, Mokdad AH, Momeni-Moghaddam H, Momtazmanesh S, Murray CJL, Negash H, Osuagwu UL, Pardhan S, Patel J, Pawar S, Petcu IR, Pham HT, Pourazizi M, Qattea I, Rahman M, Saeed U, Sahebkar A, Salehi MA, Shayan M, Shittu A, Steinmetz JD, Tan Y, Topouzis F, Tsatsakis A, Umair M, Vos T, Xiao H, You Y, Zastrozhin MS, Zhang ZJ, Zheng P. Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 2000 to 2020. Eye (Lond) 2024; 38:2047-2057. [PMID: 38937557 PMCID: PMC11269692 DOI: 10.1038/s41433-024-03101-5] [Show More Authors] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 04/06/2024] [Accepted: 04/19/2024] [Indexed: 06/29/2024] Open
Abstract
OBJECTIVES To estimate global and regional trends from 2000 to 2020 of the number of persons visually impaired by diabetic retinopathy and their proportion of the total number of vision-impaired individuals. METHODS Data from population-based studies on eye diseases between 1980 to 2018 were compiled. Meta-regression models were performed to estimate the prevalence of blindness (presenting visual acuity <3/60) and moderate or severe vision impairment (MSVI; <6/18 to ≥3/60) attributed to DR. The estimates, with 95% uncertainty intervals [UIs], were stratified by age, sex, year, and region. RESULTS In 2020, 1.07 million (95% UI: 0.76, 1.51) people were blind due to DR, with nearly 3.28 million (95% UI: 2.41, 4.34) experiencing MSVI. The GBD super-regions with the highest percentage of all DR-related blindness and MSVI were Latin America and the Caribbean (6.95% [95% UI: 5.08, 9.51]) and North Africa and the Middle East (2.12% [95% UI: 1.55, 2.79]), respectively. Between 2000 and 2020, changes in DR-related blindness and MSVI were greater among females than males, predominantly in the super-regions of South Asia (blindness) and Southeast Asia, East Asia, and Oceania (MSVI). CONCLUSIONS Given the rapid global rise in diabetes and increased life expectancy, DR is anticipated to persist as a significant public health challenge. The findings emphasise the need for gender-specific interventions and region-specific DR healthcare policies to mitigate disparities and prevent avoidable blindness. This study contributes to the expanding body of literature on the burden of DR, highlighting the need for increased global attention and investment in this research area.
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Meunier A, Opeifa O, Longworth L, Cox O, Bührer C, Durand-Zaleski I, Kelly SP, Gale RP. An eye on equity: faricimab-driven health equity improvements in diabetic macular oedema using a distributional cost-effectiveness analysis from a UK societal perspective. Eye (Lond) 2024; 38:1917-1925. [PMID: 38555401 PMCID: PMC11226444 DOI: 10.1038/s41433-024-03043-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 02/26/2024] [Accepted: 03/15/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND/OBJECTIVES Diabetic macular oedema (DMO) is a leading cause of blindness in developed countries, with significant disease burden associated with socio-economic deprivation. Distributional cost-effectiveness analysis (DCEA) allows evaluation of health equity impacts of interventions, estimation of how health outcomes and costs are distributed in the population, and assessments of potential trade-offs between health maximisation and equity. We conducted an aggregate DCEA to determine the equity impact of faricimab. METHODS Data on health outcomes and costs were derived from a cost-effectiveness model of faricimab compared with ranibizumab, aflibercept and off-label bevacizumab using a societal perspective in the base case and a healthcare payer perspective in scenario analysis. Health gains and health opportunity costs were distributed across socio-economic subgroups. Health and equity impacts, measured using the Atkinson inequality index, were assessed visually on an equity-efficiency impact plane and combined into a measure of societal welfare. RESULTS At an opportunity cost threshold of £20,000/quality-adjusted life year (QALY), faricimab displayed an increase in net health benefits against all comparators and was found to improve equity. The equity impact increased the greater the concerns for reducing health inequalities over maximising population health. Using a healthcare payer perspective, faricimab was equity improving in most scenarios. CONCLUSIONS Long-acting therapies with fewer injections, such as faricimab, may reduce costs, improve health outcomes and increase health equity. Extended economic evaluation frameworks capturing additional value elements, such as DCEA, enable a more comprehensive valuation of interventions, which is of relevance to decision-makers, healthcare professionals and patients.
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Affiliation(s)
| | | | | | - Oliver Cox
- F. Hoffmann-La Roche Ltd, Grenzacherstrasse, Basel, Switzerland
| | | | | | | | - Richard P Gale
- Hull York Medical School, York and Scarborough Teaching Hospitals NHS Foundation Trust, York, UK
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14
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Wu G, Hu Y, Zhu Q, Liang A, Du Z, Zheng C, Liang Y, Zheng Y, Hu Y, Kong L, Liang Y, Amadou MLDJ, Fang Y, Liu Y, Feng S, Yuan L, Cao D, Lin J, Yu H. Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables. J Transl Med 2024; 22:523. [PMID: 38822359 PMCID: PMC11140894 DOI: 10.1186/s12967-024-05328-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/20/2024] [Indexed: 06/02/2024] Open
Abstract
OBJECTIVE Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.
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Affiliation(s)
- Guanrong Wu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
- Department of Endocrinology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- School of Medicine, South China University of Technology, Guangzhou, China
| | - Yijun Hu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Qibo Zhu
- Department of Endocrinology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Anyi Liang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Zijing Du
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Chunwen Zheng
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Yanhua Liang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
- Department of Ophthalmology, The People's Hospital of JiangMen, Jiangmen, China
| | - Yuxiang Zheng
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yunyan Hu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Lingcong Kong
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Yingying Liang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Maman Lawali Dan Jouma Amadou
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Ying Fang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Yuejuan Liu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China
| | - Songfu Feng
- Department of Ophthalmology, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Ling Yuan
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Dan Cao
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China.
| | - Jinxin Lin
- Department of Endocrinology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangdong Eye Institute, Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
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15
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Aravindhan A, Fenwick EK, Chan AWD, Man REK, Tan NC, Wong WT, Soo WF, Lim SW, Wee SYM, Sabanayagam C, Finkelstein E, Tan G, Hamzah H, Chakraborty B, Acharyya S, Shyong TE, Scanlon P, Wong TY, Lamoureux EL. Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study. BMC Public Health 2024; 24:786. [PMID: 38481239 PMCID: PMC10935797 DOI: 10.1186/s12889-024-18287-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 03/05/2024] [Indexed: 03/17/2024] Open
Abstract
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.
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Affiliation(s)
- Amudha Aravindhan
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Eva K Fenwick
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Aurora Wing Dan Chan
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
| | - Ryan Eyn Kidd Man
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | | | | | | | | | | | - Charumathi Sabanayagam
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | | | - Gavin Tan
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Haslina Hamzah
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore
| | | | | | - Tai E Shyong
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Peter Scanlon
- Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK
| | | | - Ecosse L Lamoureux
- Singapore Eye Research Institute and Singapore National Eye Centre, Singapore, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
- The University of Melbourne, Melbourne, Australia.
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16
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Gad H, Kalra S, Pinzon R, Garcia RAN, Yotsombut K, Coetzee A, Nafach J, Lim LL, Fletcher PE, Lim V, Malik RA. Earlier diagnosis of peripheral neuropathy in primary care: A call to action. J Peripher Nerv Syst 2024; 29:28-37. [PMID: 38268316 DOI: 10.1111/jns.12613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
Peripheral neuropathy (PN) often remains undiagnosed (~80%). Earlier diagnosis of PN may reduce morbidity and enable earlier risk factor reduction to limit disease progression. Diabetic peripheral neuropathy (DPN) is the most common PN and the 10 g monofilament is endorsed as an inexpensive and easily performed test for DPN. However, it only detects patients with advanced neuropathy at high risk of foot ulceration. There are many validated questionnaires to diagnose PN, but they can be time-consuming and have complex scoring systems. Primary care physicians (PCPs) have busy clinics and lack access to a readily available screening method to diagnose PN. They would prefer a short, simple, and accurate tool to screen for PN. Involving the patient in the screening process would not only reduce the time a physician requires to make a diagnosis but would also empower the patient. Following an expert meeting of diabetologists and neurologists from the Middle East, South East Asia and Latin America, a consensus was formulated to help improve the diagnosis of PN in primary care using a simple tool for patients to screen themselves for PN followed by a consultation with the physician to confirm the diagnosis.
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Affiliation(s)
- Hoda Gad
- Research Department, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Sanjay Kalra
- Department of Endocrinology, Bharti Hospital, Karnal, India
| | - Rizaldy Pinzon
- Neurology Department of the Bethesda, General Hospital Yogyakarta, Yogyakarta, Indonesia
| | - Rey-An Nino Garcia
- College of Medicine, De LA Salle, Health Medical and Science Institute College of Medicine, Manila, Philippines
| | - Kitiyot Yotsombut
- Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, Thailand
| | - Ankia Coetzee
- Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jalal Nafach
- Dubai Diabetes Center, Dubai Academic Health Corporation, Dubai, UAE
| | - Lee-Ling Lim
- Department of Medicine, Diabetes Care Unit, University of Malaya, Kuala Lumpur, Malaysia
| | - Pablo E Fletcher
- Endocrinology Department, Medical School, University of Panama, Panama, Panama
| | - Vivien Lim
- Endocrinology Department, Gleneagles Hospital, Singapore, Singapore
| | - Rayaz A Malik
- Research Department, Weill Cornell Medicine-Qatar, Doha, Qatar
- Institute of Cardiovascular Medicine, University of Manchester, Manchester, UK
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17
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Thomas K, Albutt N, Hamid A, Wharton H, Jacob S. Five-year outcomes of digital diabetic eye screening in individuals aged 80 and 85 years. Eye (Lond) 2023; 37:3661-3665. [PMID: 37210455 PMCID: PMC10686376 DOI: 10.1038/s41433-023-02577-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 04/07/2023] [Accepted: 05/05/2023] [Indexed: 05/22/2023] Open
Abstract
OBJECTIVE To assess the incidence of referable diabetic retinopathy (DR) in patients aged 80 and 85 years to determine whether screening interval can be extended safely in this age group. METHODS Patients who were aged 80 and 85 years when they attended digital screening during April 2014-March 2015 were included. Screening results at baseline and over the next four years were analysed. RESULTS 1880 patients aged 80 and 1105 patients aged 85 were included. Patients referred to hospital eye service (HES) for DR ranged from 0.7% to 1.4% in the 80-year-old cohort over 5 years. In this cohort a total of 76 (4%) were referred to HES for DR, of which 11 (0.6%) received treatment. Over the course of the follow up (FU), 403 (21%) died. In the 85-year-old cohort, referral to HES for DR each year ranged from 0.1% to 1.3%. In this cohort a total of 27 (2.4%) were referred to HES for DR, of which 4 (0.4%) received treatment. Over the course of follow-up 541(49%) died. All treated cases were for maculopathy in both cohorts and there were no cases of proliferative diabetic retinopathy requiring treatment. CONCLUSION This study showed that the risk of progression of retinopathy is quite low in this age group and only a small proportion of patients developed referable retinopathy requiring treatment. This suggests relooking at the need for screening and ideal screening intervals in patients aged 80 years and over with no referable DR as they can be potentially classed as a group with low risk of sight loss.
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Affiliation(s)
- Kevin Thomas
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Nichola Albutt
- Birmingham, Solihull and Black Country Diabetic Eye Screening Programme, Birmingham, UK
- University Hospitals Birmingham, Birmingham, UK
| | - Aisha Hamid
- Birmingham, Solihull and Black Country Diabetic Eye Screening Programme, Birmingham, UK
- University Hospitals Birmingham, Birmingham, UK
| | - Helen Wharton
- Birmingham, Solihull and Black Country Diabetic Eye Screening Programme, Birmingham, UK
- University Hospitals Birmingham, Birmingham, UK
| | - Sarita Jacob
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
- Birmingham, Solihull and Black Country Diabetic Eye Screening Programme, Birmingham, UK.
- University Hospitals Birmingham, Birmingham, UK.
- College of Health and Life Sciences, Aston University, Birmingham, UK.
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18
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Loewenstein A, Berger A, Daly A, Creuzot-Garcher C, Gale R, Ricci F, Zarranz-Ventura J, Guymer R. Save our Sight (SOS): a collective call-to-action for enhanced retinal care across health systems in high income countries. Eye (Lond) 2023; 37:3351-3359. [PMID: 37280350 PMCID: PMC10630379 DOI: 10.1038/s41433-023-02540-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 06/08/2023] Open
Abstract
With a growing aging population, the prevalence of age-related eye disease and associated eye care is expected to increase. The anticipated growth in demand, coupled with recent medical advances that have transformed eye care for people living with retinal diseases, particularly neovascular age-related macular degeneration (nAMD) and diabetic eye disease, has presented an opportunity for health systems to proactively manage the expected burden of these diseases. To do so, we must take collective action to address existing and anticipated capacity limitations by designing and implementing sustainable strategies that enable health systems to provide an optimal standard of care. Sufficient capacity will enable us to streamline and personalize the patient experience, reduce treatment burden, enable more equitable access to care and ensure optimal health outcomes. Through a multi-modal approach that gathered unbiased perspectives from clinical experts and patient advocates from eight high-income countries, substantiated perspectives with evidence from the published literature and validated findings with the broader eye care community, we have exposed capacity challenges that are motivating the community to take action and advocate for change. Herein, we propose a collective call-to-action for the future management of retinal diseases and potential strategies to achieve better health outcomes for individuals at-risk of, or living with, retinal disease.
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Affiliation(s)
- Anat Loewenstein
- Ophthalmology Division, Tel Aviv Medical Center, Tel Aviv University, Tel Aviv, Israel.
| | - Alan Berger
- St. Michael's Hospital, University of Toronto, Toronto, ON, Canada
- Toronto Retina Institute, Toronto, ON, Canada
| | | | | | - Richard Gale
- Hull York Medical School, University of York, York, UK
- York and Scarborough Teaching Hospitals NHS Foundation Trust, York, UK
| | - Federico Ricci
- Dept. Experimental Medicine - University Tor Vergata of Rome, Rome, Italy
| | - Javier Zarranz-Ventura
- Hospital Clinic of Barcelona, University of Barcelona, Barcelona, Spain
- August Pi and Sunyer Biomedical Research Institute, University of Barcelona, Barcelona, Spain
| | - Robyn Guymer
- Centre for Eye Research, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, VIC, Australia
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Olvera-Barrios A, Owen CG, Anderson J, Warwick AN, Chambers R, Bolter L, Wu Y, Welikala R, Fajtl J, Barman SA, Remagnino P, Chew EY, Ferris FL, Hingorani AD, Sofat R, Lee AY, Egan C, Tufail A, Rudnicka AR. Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study. BMJ Open Diabetes Res Care 2023; 11:e003683. [PMID: 37949472 PMCID: PMC10649497 DOI: 10.1136/bmjdrc-2023-003683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 09/08/2023] [Indexed: 11/12/2023] Open
Abstract
INTRODUCTION The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual eye screening. We examined incidence and determinants of sight-threatening diabetic retinopathy (STDR) in a sociodemographically diverse multi-ethnic population. RESEARCH DESIGN AND METHODS North East London DESP cohort data (January 2012 to December 2021) with 137 591 PLD with no retinopathy, or non-STDR at baseline in one/both eyes, were used to calculate STDR incidence rates by sociodemographic factors, diabetes type, and duration. HR from Cox models examined associations with STDR. RESULTS There were 16 388 incident STDR cases over a median of 5.4 years (IQR 2.8-8.2; STDR rate 2.214, 95% CI 2.214 to 2.215 per 100 person-years). People with no retinopathy at baseline had a lower risk of sight-threatening diabetic retinopathy (STDR) compared with those with non-STDR in one eye (HR 3.03, 95% CI 2.91 to 3.15, p<0.001) and both eyes (HR 7.88, 95% CI 7.59 to 8.18, p<0.001). Black and South Asian individuals had higher STDR hazards than white individuals (HR 1.57, 95% CI 1.50 to 1.64 and HR 1.36, 95% CI 1.31 to 1.42, respectively). Additionally, every 5-year increase in age at inclusion was associated with an 8% reduction in STDR hazards (p<0.001). CONCLUSIONS Ethnic disparities exist in a health system limited by capacity rather than patient economic circumstances. Diabetic retinopathy at first screen is a strong determinant of STDR development. By using basic demographic characteristics, screening programmes or clinical practices can stratify risk for sight-threatening diabetic retinopathy development.
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Affiliation(s)
- Abraham Olvera-Barrios
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Christopher G Owen
- Population Health Research Institute, St. George's University of London, London, UK
| | - John Anderson
- Diabetes, Homerton Healthcare NHS Foundation Trust, London, UK
| | - Alasdair N Warwick
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Cardiovascular Science, University College London, London, UK
| | - Ryan Chambers
- Diabetes, Homerton Healthcare NHS Foundation Trust, London, UK
| | - Louis Bolter
- Diabetes, Homerton Healthcare NHS Foundation Trust, London, UK
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
- Roger and Angie Keralis Johnson Retina Center, Seattle, Washington, USA
| | - Roshan Welikala
- School of Computer Science and Mathematics, Kingston University, London, UK
| | - Jiri Fajtl
- School of Computer Science and Mathematics, Kingston University, London, UK
| | - Sarah A Barman
- School of Computer Science and Mathematics, Kingston University, London, UK
| | - Paolo Remagnino
- Department of Computer Science, Durham University, Durham, UK
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, NEI/NIH, Bethesda, Maryland, USA
| | | | - Aroon D Hingorani
- Institute of Cardiovascular Science, University College London, London, UK
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Reecha Sofat
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
- Roger and Angie Keralis Johnson Retina Center, Seattle, Washington, USA
| | - Catherine Egan
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Adnan Tufail
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Alicja R Rudnicka
- Population Health Research Institute, St. George's University of London, London, UK
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Wang M, Lin T, Wang L, Lin A, Zou K, Xu X, Zhou Y, Peng Y, Meng Q, Qian Y, Deng G, Wu Z, Chen J, Lin J, Zhang M, Zhu W, Zhang C, Zhang D, Goh RSM, Liu Y, Pang CP, Chen X, Chen H, Fu H. Uncertainty-inspired open set learning for retinal anomaly identification. Nat Commun 2023; 14:6757. [PMID: 37875484 PMCID: PMC10598011 DOI: 10.1038/s41467-023-42444-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/11/2023] [Indexed: 10/26/2023] Open
Abstract
Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.
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Affiliation(s)
- Meng Wang
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Tian Lin
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China
| | - Lianyu Wang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211100, Nanjing, Jiangsu, China
- Laboratory of Brain-Machine Intelligence Technology, Ministry of Education Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, Jiangsu, China
| | - Aidi Lin
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China
| | - Ke Zou
- National Key Laboratory of Fundamental Science on Synthetic Vision and the College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan, China
| | - Xinxing Xu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Yi Zhou
- School of Electronics and Information Engineering, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Yuanyuan Peng
- School of Biomedical Engineering, Anhui Medical University, 230032, Hefei, Anhui, China
| | - Qingquan Meng
- School of Electronics and Information Engineering, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Yiming Qian
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Guoyao Deng
- National Key Laboratory of Fundamental Science on Synthetic Vision and the College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan, China
| | - Zhiqun Wu
- Longchuan People's Hospital, 517300, Heyuan, Guangdong, China
| | - Junhong Chen
- Puning People's Hospital, 515300, Jieyang, Guangdong, China
| | - Jianhong Lin
- Haifeng PengPai Memory Hospital, 516400, Shanwei, Guangdong, China
| | - Mingzhi Zhang
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China
| | - Weifang Zhu
- School of Electronics and Information Engineering, Soochow University, 215006, Suzhou, Jiangsu, China
| | - Changqing Zhang
- College of Intelligence and Computing, Tianjin University, 300350, Tianjin, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211100, Nanjing, Jiangsu, China
- Laboratory of Brain-Machine Intelligence Technology, Ministry of Education Nanjing University of Aeronautics and Astronautics, 211106, Nanjing, Jiangsu, China
| | - Rick Siow Mong Goh
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Yong Liu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore
| | - Chi Pui Pang
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, 999077, Hong Kong, China
| | - Xinjian Chen
- School of Electronics and Information Engineering, Soochow University, 215006, Suzhou, Jiangsu, China.
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, 215006, Suzhou, China.
| | - Haoyu Chen
- Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, 515041, Shantou, Guangdong, China.
| | - Huazhu Fu
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore, 138632, Republic of Singapore.
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21
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Rajendrakumar AL, Hapca SM, Nair ATN, Huang Y, Chourasia MK, Kwan RSY, Nangia C, Siddiqui MK, Vijayaraghavan P, Matthew SZ, Leese GP, Mohan V, Pearson ER, Doney ASF, Palmer CNA. Competing risks analysis for neutrophil to lymphocyte ratio as a predictor of diabetic retinopathy incidence in the Scottish population. BMC Med 2023; 21:304. [PMID: 37563596 PMCID: PMC10413718 DOI: 10.1186/s12916-023-02976-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 07/11/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is a major sight-threatening microvascular complication in individuals with diabetes. Systemic inflammation combined with oxidative stress is thought to capture most of the complexities involved in the pathology of diabetic retinopathy. A high level of neutrophil-lymphocyte ratio (NLR) is an indicator of abnormal immune system activity. Current estimates of the association of NLR with diabetes and its complications are almost entirely derived from cross-sectional studies, suggesting that the nature of the reported association may be more diagnostic than prognostic. Therefore, in the present study, we examined the utility of NLR as a biomarker to predict the incidence of DR in the Scottish population. METHODS The incidence of DR was defined as the time to the first diagnosis of R1 or above grade in the Scottish retinopathy grading scheme from type 2 diabetes diagnosis. The effect of NLR and its interactions were explored using a competing risks survival model adjusting for other risk factors and accounting for deaths. The Fine and Gray subdistribution hazard model (FGR) was used to predict the effect of NLR on the incidence of DR. RESULTS We analysed data from 23,531 individuals with complete covariate information. At 10 years, 8416 (35.8%) had developed DR and 2989 (12.7%) were lost to competing events (death) without developing DR and 12,126 individuals did not have DR. The median (interquartile range) level of NLR was 2.04 (1.5 to 2.7). The optimal NLR cut-off value to predict retinopathy incidence was 3.04. After accounting for competing risks at 10 years, the cumulative incidence of DR and deaths without DR were 50.7% and 21.9%, respectively. NLR was associated with incident DR in both Cause-specific hazard (CSH = 1.63; 95% CI: 1.28-2.07) and FGR models the subdistribution hazard (sHR = 2.24; 95% CI: 1.70-2.94). Both age and HbA1c were found to modulate the association between NLR and the risk of DR. CONCLUSIONS The current study suggests that NLR has a promising potential to predict DR incidence in the Scottish population, especially in individuals less than 65 years and in those with well-controlled glycaemic status.
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Affiliation(s)
- Aravind Lathika Rajendrakumar
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
- Biodemography of Aging Research Unit, Duke University, Durham, NC, 27708-0408, USA
| | - Simona M Hapca
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
- Division of Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, Scotland
| | | | - Yu Huang
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
| | - Mehul Kumar Chourasia
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
- IQVIA, 3 Forbury Place, 23 Forbury Road, Reading, RG1 3JH, UK
| | - Ryan Shun-Yuen Kwan
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
- Beatson Institute for Cancer Research, Glasgow, UK
| | - Charvi Nangia
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
| | - Moneeza K Siddiqui
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
- Wolfson Institute of Population Health, Queen Mary University of London, London, E1 4NS, UK
| | | | | | - Graham P Leese
- Department of Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | | | - Ewan R Pearson
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
| | - Alexander S F Doney
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK
| | - Colin N A Palmer
- Division of Population Health and Genomics, Ninewells Hospital, University of Dundee, Dundee, UK.
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22
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Penha FM, Priotto BM, Hennig F, Przysiezny B, Wiethorn BA, Orsi J, Nagel IBF, Wiggers B, Stuchi JA, Lencione D, de Souza Prado PV, Yamanaka F, Lojudice F, Malerbi FK. Single retinal image for diabetic retinopathy screening: performance of a handheld device with embedded artificial intelligence. Int J Retina Vitreous 2023; 9:41. [PMID: 37430345 DOI: 10.1186/s40942-023-00477-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/23/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is a leading cause of blindness. Our objective was to evaluate the performance of an artificial intelligence (AI) system integrated into a handheld smartphone-based retinal camera for DR screening using a single retinal image per eye. METHODS Images were obtained from individuals with diabetes during a mass screening program for DR in Blumenau, Southern Brazil, conducted by trained operators. Automatic analysis was conducted using an AI system (EyerMaps™, Phelcom Technologies LLC, Boston, USA) with one macula-centered, 45-degree field of view retinal image per eye. The results were compared to the assessment by a retinal specialist, considered as the ground truth, using two images per eye. Patients with ungradable images were excluded from the analysis. RESULTS A total of 686 individuals (average age 59.2 ± 13.3 years, 56.7% women, diabetes duration 12.1 ± 9.4 years) were included in the analysis. The rates of insulin use, daily glycemic monitoring, and systemic hypertension treatment were 68.4%, 70.2%, and 70.2%, respectively. Although 97.3% of patients were aware of the risk of blindness associated with diabetes, more than half of them underwent their first retinal examination during the event. The majority (82.5%) relied exclusively on the public health system. Approximately 43.4% of individuals were either illiterate or had not completed elementary school. DR classification based on the ground truth was as follows: absent or nonproliferative mild DR 86.9%, more than mild (mtm) DR 13.1%. The AI system achieved sensitivity, specificity, positive predictive value, and negative predictive value percentages (95% CI) for mtmDR as follows: 93.6% (87.8-97.2), 71.7% (67.8-75.4), 42.7% (39.3-46.2), and 98.0% (96.2-98.9), respectively. The area under the ROC curve was 86.4%. CONCLUSION The portable retinal camera combined with AI demonstrated high sensitivity for DR screening using only one image per eye, offering a simpler protocol compared to the traditional approach of two images per eye. Simplifying the DR screening process could enhance adherence rates and overall program coverage.
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Affiliation(s)
- Fernando Marcondes Penha
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil.
- Botelho Hospital da Visão, Rua 2 de Setembro, 2958, Blumenau, 89052-504, SC, Brazil.
| | - Bruna Milene Priotto
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | - Francini Hennig
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | - Bernardo Przysiezny
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | - Bruno Antunes Wiethorn
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | - Julia Orsi
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | | | - Brenda Wiggers
- Fundacao Universidade Regional de Blumenau, Rua Antonio Veiga 140, Blumenau, 89030-903, SC, Brazil
| | | | | | | | | | - Fernando Lojudice
- Bayer Healthcare - Brazil, São Paulo, SP, Brazil
- Cell and Molecular Theraphy Center (NUCEL), School of Medicine, University of São Paulo, São Paulo, SP, Brazil
| | - Fernando Korn Malerbi
- Department of Ophthalmology, Federal University of São Paulo (UNIFESP), São Paulo, SP, Brazil
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23
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Ramos PL, Santana R, Marques AP, Sousa I, Rocha-Sousa A, Macedo AF. Cross-sectional study investigating the prevalence and causes of vision impairment in Northwest Portugal using capture-recapture. BMJ Open 2022; 12:e056995. [PMID: 36691224 PMCID: PMC9462125 DOI: 10.1136/bmjopen-2021-056995] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 07/06/2022] [Indexed: 01/26/2023] Open
Abstract
OBJECTIVES The aim of this study was to estimate the prevalence and causes of vision impairment (VI) in Portugal. SETTING Information about people with VI was obtained from primary care centres, blind association (ACAPO) and from hospitals (the PCVIP study) in the Northwest of Portugal during a period spanning years 2014-2015. Causes of VI were obtained from hospitals. PARTICIPANTS Administrative and medical records of people with visual acuity in the better seeing eye of 0.5 decimal (0.30logMAR) or worse and/or visual field less than 20° were investigated. Capture-recapture with log-linear models was applied to estimate the number of individuals missing from lists of cases obtained from available sources. PRIMARY AND SECONDARY OUTCOME MEASURES Log-linear models were used to estimate the crude prevalence and the category specific prevalence of VI. RESULTS Crude prevalence of VI was 1.97% (95% CI 1.56% to 2.54%), and standardised prevalence was 1% (95% CI 0.78% to 1.27%). The age-specific prevalence was 3.27% (95% CI 2.36% to 4.90%), older than 64 years, 0.64% (95% CI 0.49% to 0.88%), aged 25-64 years, and 0.07% (95% CI 0.045% to 0.13%), aged less than 25 years. The female-to-male ratio was 1.3, that is, higher prevalence among females. The five leading causes of VI were diabetic retinopathy, cataract, age-related macular degeneration, glaucoma and disorders of the globe. CONCLUSIONS The prevalence of VI in Portugal was within the expected range and in line with other European countries. A significant number of cases of VI might be due to preventable cases and, therefore, a reduction of the prevalence of VI in Portugal seems possible. Women and old people were more likely to have VI and, therefore, these groups require extra attention. Future studies are necessary to characterise temporal changes in prevalence of VI in Portugal.
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Affiliation(s)
- Pedro Lima Ramos
- Department of Medicine and Optometry, Linnaeus University, Kalmar, Sweden
- Low Vision and Visual Rehabilitation Lab, Department and Center of Physics - Optometry and Vision Science, University of Minho, Braga, Portugal
| | - Rui Santana
- Escola Nacional Saude Publica, Comprehensive Health Research Centre Universidade Nova de Lisboa, Lisboa, Portugal
| | - Ana Patricia Marques
- Escola Nacional Saude Publica, Comprehensive Health Research Centre Universidade Nova de Lisboa, Lisboa, Portugal
- International Centre for Eye Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Ines Sousa
- Department of Mathematics and Applications and Center of Molecular and Environmental Biology, School of Sciences, University of Minho, Braga, Portugal
| | - Amandio Rocha-Sousa
- Department of Surgery and Physiology, Faculty of Medicine, University of Porto, Porto, Portugal
- Department of ophthalmology, Centro Hospitalar e Universitário de São João, Porto, Portugal
| | - Antonio Filipe Macedo
- Department of Medicine and Optometry, Linnaeus University, Kalmar, Sweden
- Low Vision and Visual Rehabilitation Lab, Department and Center of Physics - Optometry and Vision Science, University of Minho, Braga, Portugal
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Changes in the Epidemiology of Diabetic Retinopathy in Spain: A Systematic Review and Meta-Analysis. Healthcare (Basel) 2022; 10:healthcare10071318. [PMID: 35885844 PMCID: PMC9320037 DOI: 10.3390/healthcare10071318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/03/2022] [Accepted: 07/07/2022] [Indexed: 11/17/2022] Open
Abstract
Background. The aim of the present study was to determine the prevalence and incidence of diabetic retinopathy (DR) and its changes in the last 20 years in type 2 diabetes mellitus (T2DM) patients in Spain. Methods. A systematic review with a meta-analysis was carried out on the studies published between 2001–2020 on the prevalence and incidence of DR and sight-threatening diabetic retinopathy (STDR) in Spain. The articles included were selected from four databases and publications of the Spanish Ministry of Health and Regional Health Care System (RHCS). The meta-analysis to determine heterogeneity and bias between studies was carried out with the MetaXL 4.0. Results. Since 2001, we have observed an increase in the detection of patients with DM, and at the same time, screening programs for RD have been launched; thus, we can deduce that the increase in the detection of patients with DM, many of them in the initial phases, far exceeds the increased detection of patients with DR. The prevalence of DR was higher between 2001 and 2008 with values of 28.85%. These values decreased over the following period between 2009 and 2020 with a mean of 15.28%. Similarly the STDR prevalence decrease from 3.67% to 1.92% after 2008. The analysis of the longitudinal studies determined that the annual DR incidence was 3.83%, and the STDR annual incidence was 0.41%. Conclusion. In Spain, for T2DM, the current prevalence of DR is 15.28% and 1.92% forSTDR. The annual incidence of DR is 3.83% and is 0.41% for STDR.
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25
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Malerbi FK, Andrade RE. Real-World diabetic retinopathy screening with a handheld fundus camera in a high-burden setting. Acta Ophthalmol 2022; 100:e1771. [PMID: 35507702 DOI: 10.1111/aos.15170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 11/30/2022]
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26
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Scanlon PH. Improving the screening of risk factors in diabetic retinopathy. Expert Rev Endocrinol Metab 2022; 17:235-243. [PMID: 35730170 DOI: 10.1080/17446651.2022.2078305] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 05/12/2022] [Indexed: 10/17/2022]
Abstract
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.
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Affiliation(s)
- Peter H Scanlon
- Consultant Ophthalmologist, Department of Ophthalmologist, Gloucestershire Hospitals NHS Foundation Trust Cheltenham, UK
- National Clinical Lead, NHS Diabetic Eye Screening Programme (Ophthalmology), Public Health Commissioning and Operations, England
- Associate Professor, Nuffield Department of Clinical Neuroscience, University of Oxford, UK
- Visiting Professor, School of Health and Social Care, University of Gloucestershire, UK
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Identification of the Relationship between Hub Genes and Immune Cell Infiltration in Vascular Endothelial Cells of Proliferative Diabetic Retinopathy Using Bioinformatics Methods. DISEASE MARKERS 2022; 2022:7231046. [PMID: 35154512 PMCID: PMC8831064 DOI: 10.1155/2022/7231046] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/19/2021] [Accepted: 01/03/2022] [Indexed: 12/13/2022]
Abstract
Background Diabetic retinopathy (DR) is a serious ophthalmopathy that causes blindness, especially in the proliferative stage. However, the pathogenesis of its effect on endothelial cells, especially its relationship with immune cell infiltration, remains unclear. Methods The dataset GSE94019 was downloaded from the Gene Expression Omnibus (GEO) database to obtain DEGs. Through aggregate analyses such as Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis, a protein-protein interaction (PPI) network was constructed to analyze the potential function of DEGs. Weighted gene coexpression network analysis (WGCNA) and Cytoscape software including molecular complex detection (MCODE) and cytoHubba plug-ins were used to comprehensively analyze and determine the hub genes. ImmuCellAI analysis was performed to further study the relationship between samples, hub genes, and 24 types of immune cell infiltration. Finally, gene-set enrichment analysis (GSEA) was employed to identify the enrichment of immune cell infiltration and endothelial cell phenotype modifications in GO biological processes (BP) based on the expression level of hub genes. Results 2393 DEGs were identified, of which 800 genes were downregulated, and 1593 genes were upregulated. The results of functional enrichment revealed that 1398 BP terms were significantly enriched in DEGs. Three hub genes, EEF1A1, RPL11, and RPS27A, which were identified by conjoint analysis using WGCNA and Cytoscape software, were positively correlated with the number of CD4 naive T cells and negatively correlated with the numbers of B cells. The number of CD4 naive T cells, T helper 2 (Th2) cells, and effector memory T (Tem) cells were significantly higher while CD8 naive T cells and B cells significantly were lower in the diabetic group than in the nondiabetic group. Conclusions We unearthed the DEGs and Hub genes of endothelial cells related to the pathogenesis of PDR: EEF1A1, RPL11, and RPS27A, which are highly related to each other and participate in the specific biological process of inflammation-related immune cell infiltration and endothelial cell development, chemotaxis, and proliferation, thus providing new perspectives into the diagnosis of and potential “killing two birds with one stone” targeted therapy for PDR.
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Sivaprasad S, Conroy D, Das T. Bridging the valley of death between research and implementing a systematic diabetic retinopathy screening program in low- and medium-income countries. Indian J Ophthalmol 2021; 69:3068-3071. [PMID: 34708744 PMCID: PMC8725140 DOI: 10.4103/ijo.ijo_1458_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Translating research findings to routine clinical practice is fraught with obstacles. The gap between the end of a research project and the implementation of its results is often termed the “valley of death.” In this perspective, we highlight the barriers and potential solutions in translating research on diabetic retinopathy care pathways to implementation in the clinic. This gap analysis applies to all countries around the world, though it predominantly applies to low- and middle-income countries.
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Affiliation(s)
- Sobha Sivaprasad
- Department of Medical Retina, NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London UK and UCL Institute of Ophthalmology, London, UK
| | - Dolores Conroy
- Department of Vision Sciences, UCL Institute of Ophthalmology, London, UK
| | - Taraprasad Das
- Department of Vitreoretinal, Srimati Kanuri Shanthamma Centre for Vitreoretinal Diseases, Hyderabad, Telangana, India
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