Comprehensive guidelines and best practices for implementing effective Diabetic Retinopathy Screening Programs to mitigate global vision loss

Xiaoting Pei, Zhijie Li

Article ID: 6668
Vol 8, Issue 10, 2024

VIEWS - 47 (Abstract) 21 (PDF)

Abstract


Diabetic retinopathy (DR) is a major cause of blindness globally. Effective screening programs are essential to mitigate this burden. This review outlines key principles and practices in implementing DR screening programs, emphasizing the roles of technology, patient education, and healthcare system integration. Our analysis highlights key principles for establishing successful screening initiatives, including the importance of regular screenings, optimal intervals, recommended technologies, and necessary infrastructure. We emphasize the roles of healthcare providers, patients, and policymakers in ensuring the effectiveness of these programs. Our recommendations aim to support the creation of robust policies that mitigate the impact of DR, ultimately improving public health outcomes and reducing the incidence of blindness due to diabetic retinopathy.


Keywords


diabetic retinopathy screening; preventable blindness; public health intervention; healthcare innovation; diabetes management

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References


Arrigo, A., Aragona, E., & Bandello, F. (2022). VEGF-targeting drugs for the treatment of retinal neovascularization in diabetic retinopathy. Annals of Medicine, 54(1), 1089–1111. https://doi.org/10.1080/07853890.2022.2064541

Bastos de Carvalho, A., Lee Ware, S., Belcher, T., et al. (2021). Evaluation of multi-level barriers and facilitators in a large diabetic retinopathy screening program in federally qualified health centers: a qualitative study. Implementation Science Communications, 2(1). https://doi.org/10.1186/s43058-021-00157-2

Bastos de Carvalho, A., Ware, S. L., Lei, F., et al. (2020). Implementation and sustainment of a statewide telemedicine diabetic retinopathy screening network for federally designated safety-net clinics. PLOS ONE, 15(11), e0241767. https://doi.org/10.1371/journal.pone.0241767

Baxter, S. L., Quackenbush, Q., Cerda, J., et al. Implementing Clinical Informatics Tools for Primary Care–Based Diabetic Retinopathy Screening. (2022). The American Journal of Managed Care, 28(10), e355–e362. https://doi.org/10.37765/ajmc.2022.89253

Bellemo, V., Lim, Z. W., Lim, G., et al. (2019). Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. The Lancet Digital health, 1, e35-e44. https://doi.org/10.1016/s2589-7500(19)30004-4

Boucher, M. C., Qian, J., Brent, M. H., et al. (2020). Evidence-based Canadian guidelines for tele-retina screening for diabetic retinopathy: recommendations from the Canadian Retina Research Network (CR2N) Tele-Retina Steering Committee. Canadian Journal of Ophthalmology, 55(1), 14–24. https://doi.org/10.1016/j.jcjo.2020.01.001

Bresnick, G., Cuadros, J. A., Khan, M., et al. (2020). Adherence to ophthalmology referral, treatment and follow-up after diabetic retinopathy screening in the primary care setting. BMJ Open Diabetes Research & Care, 8(1), e001154. https://doi.org/10.1136/bmjdrc-2019-001154

Broadbent, D. M., Sampson, C. J., Wang, A., et al. (2019). Individualised screening for diabetic retinopathy: the ISDR study—rationale, design and methodology for a randomised controlled trial comparing annual and individualised risk-based variable-interval screening. BMJ Open, 9(6), e025788. https://doi.org/10.1136/bmjopen-2018-025788

Broadbent, D. M., Wang, A., Cheyne, C. P., et al. (2021). Safety and cost-effectiveness of individualised screening for diabetic retinopathy: the ISDR open-label, equivalence RCT. Diabetologia, 64(1), 56–69. https://doi.org/10.1007/s00125-020-05313-2

Capellan, P., Dillon, A. B., Rodriguez, G., et al. (2024). Implementation of a Teleophthalmology Screening Program for Diabetic Retinopathy in New York City. Journal of VitreoRetinal Diseases, 8(1), 34–44. https://doi.org/10.1177/24741264231208253

Chalke, S. D., & Kale, P. P. (2021). Combinational Approaches Targeting Neurodegeneration, Oxidative Stress, and Inflammation in the Treatment of Diabetic Retinopathy. Current Drug Targets, 22(16), 1810–1824. https://doi.org/10.2174/1389450122666210319113136

Chung, A. J., & Dang, M. N. (2020). Type 2 Diabetic Retinopathy Screening in a General Practice: A Five-Year Retrospective Analysis. Cureus. https://doi.org/10.7759/cureus.11713

Chung, Y. C., Xu, T., Tung, T. H., et al. (2022). Early Screening for Diabetic Retinopathy in Newly Diagnosed Type 2 Diabetes and Its Effectiveness in Terms of Morbidity and Clinical Treatment: A Nationwide Population-Based Cohort. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.771862

Dai, L., Wu, L., Li, H., et al. (2021). A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications, 12(1). https://doi.org/10.1038/s41467-021-23458-5

Deswal, J., Narang, S., Gupta, N., et al. (2020). To study the impact of diabetic retinopathy on quality of life in Indian diabetic patients. Indian journal of ophthalmology, 68, 848-853. https://doi.org/10.4103/ijo.IJO_1553_19

Egunsola, O., Dowsett, L. E., Diaz, R., et al. (2021). Diabetic Retinopathy Screening: A Systematic Review of Qualitative Literature. Canadian Journal of Diabetes, 45(8), 725-733.e12. https://doi.org/10.1016/j.jcjd.2021.01.014

Elsharkawy, M., Sharafeldeen, A., Soliman, A., et al. (2022). A Novel Computer-Aided Diagnostic System for Early Detection of Diabetic Retinopathy Using 3D-OCT Higher-Order Spatial Appearance Model. Diagnostics, 12(2), 461. https://doi.org/10.3390/diagnostics12020461

Fenner, B. J., Wong, R. L. M., Lam, W. C., et al. (2018). Advances in Retinal Imaging and Applications in Diabetic Retinopathy Screening: A Review. Ophthalmology and Therapy, 7(2), 333–346. https://doi.org/10.1007/s40123-018-0153-7

Granado-Casas, M., Castelblanco, E., Ramírez-Morros, A., et al. (2019). Poorer Quality of Life and Treatment Satisfaction is Associated with Diabetic Retinopathy in Patients with Type 1 Diabetes without Other Advanced Late Complications. Journal of Clinical Medicine, 8(3), 377. https://doi.org/10.3390/jcm8030377

Gulshan, V., Rajan, R. P., Widner, K., et al. (2019). Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India. JAMA Ophthalmology, 137(9), 987. https://doi.org/10.1001/jamaophthalmol.2019.2004

Gupta, V., Azad, S. V., Vashist, P., et al. (2022). Diabetic retinopathy screening in the public sector in India: What is needed? Indian journal of ophthalmology, 70, 759-767. https://doi.org/10.4103/ijo.IJO_1298_21

Hathwar, S. B., & Srinivasa, G. (2019). Automated Grading of Diabetic Retinopathy in Retinal Fundus Images using Deep Learning. In: Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). https://doi.org/10.1109/icsipa45851.2019.8977760

Heydon, P., Egan, C., Bolter, L., et al. (2021). Prospective evaluation of an artificial intelligence-enabled algorithm for automated diabetic retinopathy screening of 30 000 patients. British Journal of Ophthalmology, 105(5), 723–728. https://doi.org/10.1136/bjophthalmol-2020-316594

Huemer, J., Wagner, S. K., & Sim, D. A. (2020). The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence. Clinical Ophthalmology, Volume 14, 2021–2035. https://doi.org/10.2147/opth.s261629

Islam, F. M. A., Kawasaki, R., & Finger, R. P. (2018). Factors associated with participation in a diabetic retinopathy screening program in a rural district in Bangladesh. Diabetes Research and Clinical Practice, 144, 111–117. https://doi.org/10.1016/j.diabres.2018.08.012

Januszewski, A. S., Velayutham, V., Benitez-Aguirre, P. Z., et al. (2022). Optimal Frequency of Retinopathy Screening in Adolescents with Type 1 Diabetes: Markov Modeling Approach Based on 30 Years of Data. Diabetes Care, 45(10), 2383–2390. https://doi.org/10.2337/dc22-0071

Kárason, K. T., Vo, D., Grauslund, J., et al. (2021). Comparison of different methods of retinal imaging for the screening of diabetic retinopathy: a systematic review. Acta Ophthalmologica, 100(2), 127–135. https://doi.org/10.1111/aos.14767

Kashim, R., Newton, P., & Ojo, O. (2018). Diabetic Retinopathy Screening: A Systematic Review on Patients’ Non-Attendance. International Journal of Environmental Research and Public Health, 15(1), 157. https://doi.org/10.3390/ijerph15010157

Keel, S., Lee, P. Y., Scheetz, J., et al. (2018). Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-22612-2

Kreft, D., McGuinness, M. B., Doblhammer, G., et al. (2018). Diabetic retinopathy screening in incident diabetes mellitus type 2 in Germany between 2004 and 2013 - A prospective cohort study based on health claims data. PLOS ONE, 13(4), e0195426. https://doi.org/10.1371/journal.pone.0195426

Lake, A. J., Browne, J. L., Abraham, C., et al. (2018). A tailored intervention to promote uptake of retinal screening among young adults with type 2 diabetes - an intervention mapping approach. BMC Health Services Research, 18(1). https://doi.org/10.1186/s12913-018-3188-5

Lam, C. K. L., Zborovski, S., Palmert, M. R., et al. (2019). Seeing Clearly: Effects of Initiatives to Improve Diabetic Retinopathy Screening at a Pediatric Center. Clinical Diabetes, 37(3), 287–290. https://doi.org/10.2337/cd18-0084

Lanzetta, P., Sarao, V., Scanlon, P. H., et al. (2020). Fundamental principles of an effective diabetic retinopathy screening program. Acta Diabetologica, 57(7), 785–798. https://doi.org/10.1007/s00592-020-01506-8

Lawrenson, J. G., Graham-Rowe, E., Lorencatto, F., et al. (2018). What works to increase attendance for diabetic retinopathy screening? An evidence synthesis and economic analysis. Health Technology Assessment, 22(29), 1–160. https://doi.org/10.3310/hta22290

Lee, J. C., Nguyen, L., Hynan, L. S., et al. (2019). Comparison of 1-field, 2-fields, and 3-fields fundus photography for detection and grading of diabetic retinopathy. Journal of Diabetes and Its Complications, 33(12), 107441. https://doi.org/10.1016/j.jdiacomp.2019.107441

Lin, J., Yu, L., Weng, Q., et al. (2019). Retinal image quality assessment for diabetic retinopathy screening: A survey. Multimedia Tools and Applications, 79(23–24), 16173–16199. https://doi.org/10.1007/s11042-019-07751-6

Liu, J., Gibson, E., Ramchal, S., et al. (2021). Diabetic Retinopathy Screening with Automated Retinal Image Analysis in a Primary Care Setting Improves Adherence to Ophthalmic Care. Ophthalmology Retina, 5(1), 71–77. https://doi.org/10.1016/j.oret.2020.06.016

Mansour, S. E., Browning, D. J., Wong, K., et al. (2020). The Evolving Treatment of Diabetic Retinopathy. Clinical Ophthalmology, Volume 14, 653–678. https://doi.org/10.2147/opth.s236637

Nørgaard, M. F., & Grauslund, J. (2018). Automated Screening for Diabetic Retinopathy – A Systematic Review. Ophthalmic Research, 60(1), 9–17. https://doi.org/10.1159/000486284

Olvera-Barrios, A., Heeren, T. F., Balaskas, K., et al. (2021). Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images. British Journal of Ophthalmology, 105(2), 265–270. https://doi.org/10.1136/bjophthalmol-2019-315394

Pan, C. W., Wang, S., Wang, P., et al. (2018). Diabetic retinopathy and health-related quality of life among Chinese with known type 2 diabetes mellitus. Quality of Life Research, 27(8), 2087–2093. https://doi.org/10.1007/s11136-018-1876-6

Pandey, R., Morgan, M. M., Murphy, C., et al. (2022). Irish National Diabetic RetinaScreen Programme: report on five rounds of retinopathy screening and screen-positive referrals. (INDEAR study report no. 1). British Journal of Ophthalmology, 106(3), 409–414. https://doi.org/10.1136/bjophthalmol-2020-317508

Pedrosa, M., Silva, J. M., Silva, J. F., et al. (2018). SCREEN-DR: Collaborative platform for diabetic retinopathy. International Journal of Medical Informatics, 120, 137–146. https://doi.org/10.1016/j.ijmedinf.2018.10.005

Petersen, G. B., Byberg, S., Vistisen, D., et al. (2022). Factors Associated with Nonattendance in a Nationwide Screening Program for Diabetic Retinopathy: A Register-Based Cohort Study. Diabetes Care, 45(2), 303–310. https://doi.org/10.2337/dc21-1380

Pires, R., Avila, S., Wainer, J., et al. (2019). A data-driven approach to referable diabetic retinopathy detection. Artificial Intelligence in Medicine, 96, 93–106. https://doi.org/10.1016/j.artmed.2019.03.009

Piyasena, M. M. P. N., Murthy, G. V. S., Yip, J. L. Y., et al. (2019). Systematic review on barriers and enablers for access to diabetic retinopathy screening services in different income settings. PLOS ONE, 14(4), e0198979. https://doi.org/10.1371/journal.pone.0198979

Raman, R., Ramasamy, K., Rajalakshmi, R., et al. (2021). Diabetic retinopathy screening guidelines in India: All India Ophthalmological Society diabetic retinopathy task force and Vitreoretinal Society of India Consensus Statement. Indian journal of ophthalmology, 69, 678-88. https://doi.org/10.4103/ijo.IJO_667_20

Ratanapakorn, T., Daengphoonphol, A., Eua-Anant, N., et al. (2019). Digital image processing software for diagnosing diabetic retinopathy from fundus photograph. Clinical Ophthalmology, 13, 641–648. https://doi.org/10.2147/opth.s195617

Riordan, F., Racine, E., Phillip, E. T., et al. (2020a). Development of an intervention to facilitate implementation and uptake of diabetic retinopathy screening. Implementation Science, 15(1). https://doi.org/10.1186/s13012-020-00982-4

Riordan, F., Racine, E., Smith, S. M., et al. (2020b). Feasibility of an implementation intervention to increase attendance at diabetic retinopathy screening: protocol for a cluster randomised pilot trial. Pilot and Feasibility Studies, 6(1). https://doi.org/10.1186/s40814-020-00608-y

Rodriguez-Acuña, R., Mayoral, E., Aguilar-Diosdado, M., et al. (2020). Andalusian program for early detection of diabetic retinopathy: implementation and 15-year follow-up of a population-based screening program in Andalusia, Southern Spain. BMJ Open Diabetes Research & Care, 8(1), e001622. https://doi.org/10.1136/bmjdrc-2020-001622

Safi, H., Safi, S., Hafezi-Moghadam, A., et al. (2018). Early detection of diabetic retinopathy. Survey of Ophthalmology, 63(5), 601–608. https://doi.org/10.1016/j.survophthal.2018.04.003

Schreur, V., Ng, H., Nijpels, G., et al. (2021). Validation of a model for the prediction of retinopathy in persons with type 1 diabetes. British Journal of Ophthalmology, 105(9), 1286–1288. https://doi.org/10.1136/bjophthalmol-2018-313539

Sharif, A., Jendle, J., & Hellgren, K. J. (2021). Screening for Diabetic Retinopathy with Extended Intervals, Safe and Without Compromising Adherence: A Retrospective Cohort Study. Diabetes Therapy, 12(1), 223–234. https://doi.org/10.1007/s13300-020-00957-0

Simó, R., & Hernández, C. (2022). New Insights into Treating Early and Advanced Stage Diabetic Retinopathy. International Journal of Molecular Sciences, 23(15), 8513. https://doi.org/10.3390/ijms23158513

Srihatrai, P., Hlowchitsieng, T. (2018). The diagnostic accuracy of single- and five-field fundus photography in diabetic retinopathy screening by primary care physicians. Indian journal of ophthalmology, 66, 94-97. https://doi.org/10.4103/ijo.IJO_657_17

Styles, C. J. (2019). Introducing automated diabetic retinopathy systems: it’s not just about sensitivity and specificity. Eye, 33(9), 1357–1358. https://doi.org/10.1038/s41433-019-0535-7

Tomita, Y., Lee, D., Tsubota, K., et al. (2021). Updates on the Current Treatments for Diabetic Retinopathy and Possibility of Future Oral Therapy. Journal of Clinical Medicine, 10(20), 4666. https://doi.org/10.3390/jcm10204666

Tymchenko, B., Marchenko, P., & Spodarets, D. (2020). Deep Learning Approach to Diabetic Retinopathy Detection. Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods. https://doi.org/10.5220/0008970805010509

Valpuesta Martin, Y., Pacheco Callirgos, G. E., Maroto Martín, T. M., et al. (2020). Satisfaction of patients and primary care professionals with a teleophthalmology-based screening programme for diabetic retinopathy in a rural area in Castilla y León, Spain. Rural and Remote Health. https://doi.org/10.22605/rrh5180

Wang, W., & Lo, A. C. Y. (2018). Diabetic Retinopathy: Pathophysiology and Treatments. International Journal of Molecular Sciences, 19(6), 1816. https://doi.org/10.3390/ijms19061816

Wong, T. Y., Sun, J., Kawasaki, R., et al. (2018). Guidelines on Diabetic Eye Care. Ophthalmology, 125(10), 1608–1622. https://doi.org/10.1016/j.ophtha.2018.04.007

Zago, G. T., Andreão, R. V., Dorizzi, B., et al. (2020). Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Computers in Biology and Medicine, 116, 103537. https://doi.org/10.1016/j.compbiomed.2019.103537




DOI: https://doi.org/10.24294/jipd.v8i10.6668

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