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Olivieri C, Salato M, Campanella A, Marolo P, Parisi G, Neri G, Toro MD, Scarmozzino A, Broglio F, Borrelli E, Reibaldi M. Comparison of diabetic retinopathy screening between hospital-based multidisciplinary and general practice-based settings: insights from a regional study in Italy. Acta Diabetol 2024:10.1007/s00592-024-02354-6. [PMID: 39160371 DOI: 10.1007/s00592-024-02354-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 08/04/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE To compare diabetic retinopathy screening among patients with type 1 or type 2 diabetes under care in two distinct setups: hospital-based multidisciplinary and general practice-based. MATERIALS AND METHODS In this retrospective observational case series, we collected data from a total of 133 diabetic patients: subjects from the hospital-based multidisciplinary setting were referred by the diabetologist and screened by an ophthalmologist using the Optomed Aurora IQ fundus camera. These patients were compared with those who underwent DR screening arranged through a general practice-based setting. RESULTS The proportion of patients treated with insulin was higher in the hospital-based multidisciplinary group, both considering the totality patients and those affected by type 2 diabetes (71.6% vs. 32.2%; p < 0.001, and 58.8% vs. 31.0%; p = 0.004 respectively). Patients from the hospital-based multidisciplinary group had a longer mean diabetes duration (19.6 vs 14.9 years, p < 0.001), underwent DR screening more frequently in the previous three years (2.9 vs 1.4, p < 0.001), the mean time between two DR screenings was shorter (14.6 vs 77.9 weeks, p < 0.001), and DR was detected more frequently (32,4% vs 13.5%; p = 0.011). CONCLUSION We were able to demonstrate that patients screened in the multidisciplinary center, which had characteristics predisposing to a higher risk of DR, were more likely to be diagnosed with DR on time, with a higher mean number of DR screenings and a shorted interval between diabetic and ophthalmological assessments.
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Affiliation(s)
- Chiara Olivieri
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Department of Ophthalmology, "City of Health and Science" Hospital, Turin, Italy
| | | | | | - Paola Marolo
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Department of Ophthalmology, "City of Health and Science" Hospital, Turin, Italy
| | - Guglielmo Parisi
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Department of Ophthalmology, "City of Health and Science" Hospital, Turin, Italy
| | - Giovanni Neri
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Department of Ophthalmology, "City of Health and Science" Hospital, Turin, Italy
| | - Mario Damiano Toro
- Public Health Department, Eye Clinic, University of Naples Federico II, 80138, Naples, Italy
| | - Antonio Scarmozzino
- Department of Quality and Cure Safety, Città Della Salute E Della Scienza, Turin, Italy
| | - Fabio Broglio
- Division of Endocrinology, Diabetes and Metabolism, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy
| | - Enrico Borrelli
- Department of Surgical Sciences, University of Turin, Turin, Italy.
- Department of Ophthalmology, "City of Health and Science" Hospital, Turin, Italy.
| | - Michele Reibaldi
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Department of Ophthalmology, "City of Health and Science" Hospital, Turin, Italy
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Romero-Aroca P, Verges R, Pascual-Fontanilles J, Valls A, Franch-Nadal J, Mundet X, Moreno A, Basora J, Garcia-Curto E, Baget-Bernaldiz M. Referable Diabetic Retinopathy Prediction Algorithm Applied to a Population of 120,389 Type 2 Diabetics over 11 Years Follow-Up. Diagnostics (Basel) 2024; 14:833. [PMID: 38667478 PMCID: PMC11049383 DOI: 10.3390/diagnostics14080833] [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: 02/28/2024] [Revised: 04/11/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
(1) Background: Although DR screening is effective, one of its most significant problems is a lack of attendance. The aim of the present study was to demonstrate the effectiveness of our algorithm in predicting the development of any type of DR and referable DR. (2) Methods: A retrospective study with an 11-year follow-up of a population of 120,389 T2DM patients was undertaken. (3) Results: Applying the results of the algorithm showed an AUC of 0.93 (95% CI, 0.92-0.94) for any DR and 0.90 (95% CI, 0.89-0.91) for referable DR. Therefore, we achieved a promising level of agreement when applying our algorithm. (4) Conclusions: The algorithm is useful for predicting which patients may develop referable forms of DR and also any type of DR. This would allow a personalized screening plan to be drawn up for each patient.
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Affiliation(s)
- Pedro Romero-Aroca
- Ophthalmology Service, University Hospital Sant Joan, Pere Virgili Health Research Institute (IISPV), 43204 Reus, Spain; (R.V.); (E.G.-C.); (M.B.-B.)
| | - Raquel Verges
- Ophthalmology Service, University Hospital Sant Joan, Pere Virgili Health Research Institute (IISPV), 43204 Reus, Spain; (R.V.); (E.G.-C.); (M.B.-B.)
| | - Jordi Pascual-Fontanilles
- ITAKA Research Group, Department of Computer Science and Mathematics, Pere Virgili Health Research Institute (IISPV), Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.P.-F.); (A.M.)
| | - Aida Valls
- ITAKA Research Group, Department of Computer Science and Mathematics, Pere Virgili Health Research Institute (IISPV), Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.P.-F.); (A.M.)
| | - Josep Franch-Nadal
- Diabetis des de l’Atenció Primária (DAP)-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGOL), 08007 Barcelona, Spain; (J.F.-N.); (X.M.); (J.B.)
| | - Xavier Mundet
- Diabetis des de l’Atenció Primária (DAP)-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGOL), 08007 Barcelona, Spain; (J.F.-N.); (X.M.); (J.B.)
| | - Antonio Moreno
- ITAKA Research Group, Department of Computer Science and Mathematics, Pere Virgili Health Research Institute (IISPV), Universitat Rovira i Virgili, 43007 Tarragona, Spain; (J.P.-F.); (A.M.)
| | - Josep Basora
- Diabetis des de l’Atenció Primária (DAP)-Cat Group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGOL), 08007 Barcelona, Spain; (J.F.-N.); (X.M.); (J.B.)
| | - Eugeni Garcia-Curto
- Ophthalmology Service, University Hospital Sant Joan, Pere Virgili Health Research Institute (IISPV), 43204 Reus, Spain; (R.V.); (E.G.-C.); (M.B.-B.)
| | - Marc Baget-Bernaldiz
- Ophthalmology Service, University Hospital Sant Joan, Pere Virgili Health Research Institute (IISPV), 43204 Reus, Spain; (R.V.); (E.G.-C.); (M.B.-B.)
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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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4
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Dai L, Sheng B, Chen T, Wu Q, Liu R, Cai C, Wu L, Yang D, Hamzah H, Liu Y, Wang X, Guan Z, Yu S, Li T, Tang Z, Ran A, Che H, Chen H, Zheng Y, Shu J, Huang S, Wu C, Lin S, Liu D, Li J, Wang Z, Meng Z, Shen J, Hou X, Deng C, Ruan L, Lu F, Chee M, Quek TC, Srinivasan R, Raman R, Sun X, Wang YX, Wu J, Jin H, Dai R, Shen D, Yang X, Guo M, Zhang C, Cheung CY, Tan GSW, Tham YC, Cheng CY, Li H, Wong TY, Jia W. A deep learning system for predicting time to progression of diabetic retinopathy. Nat Med 2024; 30:584-594. [PMID: 38177850 PMCID: PMC10878973 DOI: 10.1038/s41591-023-02702-z] [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: 04/27/2023] [Accepted: 11/10/2023] [Indexed: 01/06/2024]
Abstract
Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.
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Grants
- the National Key Research and Development Program of China (2022YFA1004804), the Shanghai Municipal Key Clinical Specialty, Shanghai Research Center for Endocrine and Metabolic Diseases (2022ZZ01002), and the Chinese Academy of Engineering (2022-XY-08)
- the General Program of NSFC (62272298), the National Key Research and Development Program of China (2022YFC2407000), the Interdisciplinary Program of Shanghai Jiao Tong University (YG2023LC11 and YG2022ZD007), National Natural Science Foundation of China (62272298 and 62077037), the College-level Project Fund of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital (ynlc201909), and the Medical-industrial Cross-fund of Shanghai Jiao Tong University (YG2022QN089)
- the Clinical Special Program of Shanghai Municipal Health Commission (20224044) and Three-year action plan to strengthen the construction of public health system in Shanghai (GWVI-11.1-28)
- the National Natural Science Foundation of China (82100879)
- the National Key Research and Development Program of China (2022YFA1004804), Excellent Young Scientists Fund of NSFC (82022012), General Fund of NSFC (81870598), Innovative research team of high-level local universities in Shanghai (SHSMU-ZDCX20212700)
- the National Key R & D Program of China (2022YFC2502800) and National Natural Science Fund of China (8238810007)
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Affiliation(s)
- Ling Dai
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Bin Sheng
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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.
| | - Tingli Chen
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Qiang Wu
- 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 for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Chun Cai
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Liang Wu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Yuexing Liu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhouyu Guan
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Shujie Yu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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 for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Ziqi Tang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Anran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Haoxuan Che
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Jia Shu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Shan Huang
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Chan Wu
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shiqun Lin
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Dan Liu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Jiajia Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Zheyuan Wang
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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 for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Jie Shen
- Medical Records and Statistics Office, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuhong Hou
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - 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
| | - Feng Lu
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Miaoli Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ramyaa Srinivasan
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Laboratory, Beijing, China
| | - Jiarui Wu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
- Center for Excellence in Molecular Science, Chinese Academy of Sciences, Shanghai, China
| | - Hai Jin
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Rongping Dai
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Dinggang Shen
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
- Shanghai United Imaging Intelligence, Shanghai, China
- Shanghai Clinical Research and Trial Center, Shanghai, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Minyi Guo
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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
| | - Cuntai Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
- Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Huating Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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.
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Tsinghua Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
| | - Weiping Jia
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, 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.
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5
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Drinkwater JJ, Kalantary A, Turner AW. A systematic review of diabetic retinopathy screening intervals. Acta Ophthalmol 2023. [PMID: 37915115 DOI: 10.1111/aos.15788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 11/03/2023]
Abstract
The current evidence on whether annual diabetic retinopathy (DR) screening intervals can be extended was reviewed. A systematic review protocol was followed (PROSPERO ID: CRD42022359590). Original longitudinal articles that specifically assessed DR screening intervals were in English and collected data after 2000 were included. Two reviewers independently conducted the search and reviewed the articles for quality and relevant information. The heterogeneity of the data meant that a meta-analysis was not appropriate. Twelve publications were included. Studies were of good quality and many used data from DR screening programs. Studies fit into three categories; those that assessed specific DR screening intervals, those that determined optimal DR screening intervals and those that developed/assessed DR screening risk equations. For those with type 2 diabetes, extending screening intervals to 3- to 4-yearly in those with no baseline DR appeared safe. DR risk equations considered clinical factors and allocated those at lower risk of DR progression screening intervals of up to five years. Those with baseline DR or type 1 diabetes appeared to have a higher risk of progression to STDR and needed more frequent screening. DR screening intervals can be extended to 3-5 yearly in certain circumstances. These include patients with type 2 diabetes and no current DR, and those who have optimal management of other risk factors such as glucose and blood pressure.
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Affiliation(s)
- Jocelyn J Drinkwater
- Center for Ophthalmology and Visual Science, The University of Western Australia, Nedlands, Western Australia, Australia
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia
| | - Amy Kalantary
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia
| | - Angus W Turner
- Center for Ophthalmology and Visual Science, The University of Western Australia, Nedlands, Western Australia, Australia
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia
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Lalithadevi B, Krishnaveni S, Gnanadurai JSC. A Feasibility Study of Diabetic Retinopathy Detection in Type II Diabetic Patients Based on Explainable Artificial Intelligence. J Med Syst 2023; 47:85. [PMID: 37552340 DOI: 10.1007/s10916-023-01976-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/12/2023] [Indexed: 08/09/2023]
Abstract
Diabetic retinopathy (DR) is vision impairment and a life-threatening condition for diabetic patients. Especially type II diabetic people have higher chances of getting retinal problems. Hence, early prediction of DR is necessary for preventing the diabetic patients from vision impairment. The main aim of this feasibility study is to identify the most critical risk features that could lead to diabetic retinopathy. This study investigated type II diabetic patients' socio-analytical, diabetes, behavioral, and clinical risk factors. We conducted a self-individual questionnaire session for all participants. Our questionnaire asked about the reliability of results, feeling comfortable during the screening test, willingness to participate in future screenings, overall perspective, and satisfaction with the DR screening test. We proposed a random forest model for predicting the prevalence of DR risk among diabetics. Further explanations of the model were conducted using more robust SHAP eXplainable Artificial Intelligence (XAI) tools. The SHAP method makes it possible to understand how input variables interact with their representative output records, as well as how input variables are ranked. In addition, various descriptive and inferential statistical analyses were performed on the data and evaluated the significant relationship between the factors discussed above via hypothesis testing. This feasibility study involved 172 type II diabetic patients (73 males and 99 females). Therefore, we found that 81 (47.09%) out of 172 participants had referable DR. The average age of the patients was determined as 55.08, with a standard deviation of ± 9.770 (ranging from 40 to 79). Type II patients were affected by mild, moderate, severe, and advanced proliferative diabetic retinopathy (PDR) stages with 23.83%, 13.95%, 5.81%, and 3.48%, respectively, of the total samples. The developed RF model obtained high accuracy of 94.9% using clinical dataset. Our results showed that the formation of tiny microminiature lesions was noticeable in type II diabetic patients with aged people, abnormal blood glucose levels, and prolonged diabetes duration.
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Affiliation(s)
- B Lalithadevi
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, TN, India.
| | - S Krishnaveni
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, TN, India
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Haider S, Adderley N, Tallouzi MO, Sadiq SN, Steel DH, Chavan R, Sheikh I, Nirantharakumar K, Snell KIE. Diabetic retinopathy progression in patients under monitoring for treatment or vision loss: external validation and update of a multivariable prediction model. BMJ Open 2023; 13:e073015. [PMID: 37012014 PMCID: PMC10083856 DOI: 10.1136/bmjopen-2023-073015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
INTRODUCTION The number of people with diabetes mellitus is increasing globally and consequently so too is diabetic retinopathy (DR). Most patients with diabetes are monitored through the diabetic eye screening programme (DESP) until they have signs of retinopathy and these changes progress, requiring referral into hospital eye services (HES). Here, they continue to be monitored until they require treatment. Due to current pressures on HES, delays can occur, leading to harm. There is a need to triage patients based on their individual risk. At present, patients are stratified according to retinopathy stage alone, yet other risk factors like glycated haemoglobin (HbA1c) may be useful. Therefore, a prediction model that combines multiple prognostic factors to predict progression will be useful for triage in this setting to improve care.We previously developed a Diabetic Retinopathy Progression model to Treatment or Vision Loss (DRPTVL-UK) using a large primary care database. The aim of the present study is to externally validate the DRPTVL-UK model in a secondary care setting, specifically in a population under care by HES. This study will also provide an opportunity to update the model by considering additional predictors not previously available. METHODS AND ANALYSIS We will use a retrospective cohort of 2400 patients with diabetes aged 12 years and over, referred from DESP to the NHS hospital trusts with referable DR between 2013 and 2016, with follow-up information recorded until December 2021.We will evaluate the external validity of the DRPTVL-UK model using measures of discrimination, calibration and net benefit. In addition, consensus meetings will be held to agree on acceptable risk thresholds for triage within the HES system. ETHICS AND DISSEMINATION This study was approved by REC (ref 22/SC/0425, 05/12/2022, Hampshire A Research Ethics Committee). The results of the study will be published in a peer-reviewed journal, presented at clinical conferences. TRIAL REGISTRATION NUMBER ISRCTN 10956293.
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Affiliation(s)
- Sajjad Haider
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Nicola Adderley
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | | | - Salman Naveed Sadiq
- Ophthalmology, Royal Victoria Infirmary, Newcastle upon Tyne, UK
- Sunderland Eye Infirmary, Sunderland, UK
| | - David H Steel
- Sunderland Eye Infirmary, Sunderland, UK
- Newcastle University Biosciences Institute, Newcastle upon Tyne, UK
| | - Randhir Chavan
- Sandwell and West Birmingham Hospitals NHS Trust, Birmingham, UK
| | - Ijaz Sheikh
- Eye Department, Surrey and Sussex Healthcare NHS Trust, Redhill, UK
| | | | - Kym I E Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
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Almulhim AN, Hartley H, Norman P, Caton SJ, Doğru OC, Goyder E. Behavioural Change Techniques in Health Coaching-Based Interventions for Type 2 Diabetes: A Systematic Review and Meta-Analysis. BMC Public Health 2023; 23:95. [PMID: 36639632 PMCID: PMC9837922 DOI: 10.1186/s12889-022-14874-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 12/15/2022] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Given the high rates globally of Type 2 Diabetes Mellitus (T2DM), there is a clear need to target health behaviours through person-centred interventions. Health coaching is one strategy that has been widely recognised as a tool to foster positive behaviour change. However, it has been used inconsistently and has produced mixed results. This systematic review sought to explore the use of behaviour change techniques (BCTs) in health coaching interventions and identify which BCTs are linked with increased effectiveness in relation to HbA1C reductions. METHODS In line with the PICO framework, the review focused on people with T2DM, who received health coaching and were compared with a usual care or active control group on HbA1c levels. Studies were systematically identified through different databases including Medline, Web of science, and PsycINFO searches for relevant randomised controlled trials (RCTs) in papers published between January 1950 and April 2022. The Cochrane collaboration tool was used to evaluate the quality of the studies. Included papers were screened on the reported use of BCTs based on the BCT taxonomy. The effect sizes obtained in included interventions were assessed by using Cohen's d and meta-analysis was used to estimate sample-weighted average effect sizes (Hedges' g). RESULTS Twenty RCTs with a total sample size of 3222 were identified. Random effects meta-analysis estimated a small-sized statistically significant effect of health coaching interventions on HbA1c reduction (g+ = 0.29, 95% CI: 0.18 to 0.40). A clinically significant HbA1c decrease of ≥5 mmol/mol was seen in eight studies. Twenty-three unique BCTs were identified in the reported interventions, with a mean of 4.5 (SD = 2.4) BCTs used in each study. Of these, Goal setting (behaviour) and Problem solving were the most frequently identified BCTs. The number of BCTs used was not related to intervention effectiveness. In addition, there was little evidence to link the use of specific BCTs to larger reductions in HbA1c across the studies included in the review; instead, the use of Credible source and Social reward in interventions were associated with smaller reductions in HbA1c. CONCLUSION A relatively small number of BCTs have been used in RCTs of health coaching interventions for T2DM. Inadequate, imprecise descriptions of interventions and the lack of theory were the main limitations of the studies included in this review. Moreover, other possible BCTs directly related to the theoretical underpinnings of health coaching were absent. It is recommended that key BCTs are identified at an early stage of intervention development, although further research is needed to examine the most effective BCTs to use in health coaching interventions. TRIAL REGISTRATION https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021228567 .
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Affiliation(s)
- Abdullah N. Almulhim
- grid.11835.3e0000 0004 1936 9262School of Health and Related Research, The University of Sheffield, 30 Regent St, Sheffield, S1 4DA UK ,grid.449598.d0000 0004 4659 9645Public Health Department, College of Health Sciences, Saudi Electronic University, Riyadh, 13316 Saudi Arabia
| | - Hannah Hartley
- grid.418447.a0000 0004 0391 9047Yorkshire Quality and Safety Research Group, Bradford Institute for Health Research, Temple Bank House, Bradford Royal Infirmary, Bradford, BD9 6RJ UK
| | - Paul Norman
- grid.11835.3e0000 0004 1936 9262Department of Psychology, The University of Sheffield, Cathedral Court, The University of Sheffield, Vicar Ln, Sheffield, S1 2LT UK
| | - Samantha J. Caton
- grid.11835.3e0000 0004 1936 9262School of Health and Related Research, The University of Sheffield, 30 Regent St, Sheffield, S1 4DA UK
| | - Onur Cem Doğru
- grid.411108.d0000 0001 0740 4815Department of Psychology, Afyon Kocatepe University, Gazlıgöl St, 03200 Afyonkarahisar, Turkey
| | - Elizabeth Goyder
- grid.11835.3e0000 0004 1936 9262School of Health and Related Research, The University of Sheffield, 30 Regent St, Sheffield, S1 4DA UK
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Automatic Diagnosis of Diabetic Retinopathy Stage Focusing Exclusively on Retinal Hemorrhage. Medicina (B Aires) 2022; 58:medicina58111681. [DOI: 10.3390/medicina58111681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/22/2022] Open
Abstract
Background and Objectives: The present study evaluated the detection of diabetic retinopathy (DR) using an automated fundus camera focusing exclusively on retinal hemorrhage (RH) using a deep convolutional neural network, which is a machine-learning technology. Materials and Methods: This investigation was conducted via a prospective and observational study. The study included 89 fundus ophthalmoscopy images. Seventy images passed an image quality review and were graded as showing no apparent DR (n = 51), mild nonproliferative DR (NPDR; n = 16), moderate NPDR (n = 1), severe NPDR (n = 1), and proliferative DR (n = 1) by three retinal experts according to the International Clinical Diabetic Retinopathy Severity scale. The RH numbers and areas were automatically detected and the results of two tests—the detection of mild-or-worse NPDR and the detection of moderate-or-worse NPDR—were examined. Results: The detection of mild-or-worse DR showed a sensitivity of 0.812 (95% confidence interval: 0.680–0.945), specificity of 0.888, and area under the curve (AUC) of 0.884, whereas the detection of moderate-or-worse DR showed a sensitivity of 1.0, specificity of 1.0, and AUC of 1.0. Conclusions: Automated diagnosis using artificial intelligence focusing exclusively on RH could be used to diagnose DR requiring ophthalmologist intervention.
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Almulhim AN, Goyder E, Caton SJ. Assessing the Feasibility and Acceptability of Health Coaching as a New Diabetes Management Approach for the People with Type 2 Diabetes in Saudi Arabia: A Protocol for a Mixed Methods Feasibility Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15089. [PMID: 36429807 PMCID: PMC9690217 DOI: 10.3390/ijerph192215089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Over recent years, the Middle East, and especially Saudi Arabia, has faced multiple changes, including structural-demographic and economic shifts. This has led to massive changes in the population's lifestyle, including more unhealthy diets and increases in physical inactivity. As a result, accelerating rates of chronic diseases, including type 2 diabetes mellitus (T2DM) are a major public health concern. Current diabetes care in Saudi Arabia focuses on increasing the awareness of patients through various approaches, mainly based on health education, which is found to be suboptimal and ineffective for improving long-term outcomes. This study aims to assess the feasibility and acceptability of using a client-centred approach called health coaching that supports, enables, and engages T2DM patients to take the central role of controlling their own conditions by developing new crucial skills. METHODS A mixed methods randomised controlled feasibility study of health coaching will be used. Participants (n = 30) are adults with T2DM with poorly controlled diabetes (A1C ≥7) who can read and write in Arabic. Eligible participants are randomly allocated to either an intervention or control group for 12 weeks. COM-B model and Behaviour Change Technique Taxonomy version 1 (BCTTv1) guide the intervention curriculum. Predetermined progression criteria will be used to determine whether to proceed to a larger trial or not. Outcomes will be measured at baseline and 3 months. The study's primary aim is to assess the process of eligibility, recruitment, retention and completion rates, acceptability and suitability of intervention and the time to complete each procedure. The preliminary efficacy of health coaching is the secondary outcome that includes different measurements, such as HbA1c, blood pressure, body mass index (BMI), waist circumference, weight, patients' self-efficacy, and diabetes self-management. DISCUSSION This is the first study to explore the feasibility, acceptability, and preliminary efficacy of health coaching that used the Capability, Opportunity, Motivation, Behaviour (COM-B) model and BCTTv1 as guidance to develop the intervention for adults with T2DM in Saudi Arabia. The findings of this study will be used to inform the larger RCT trial if it is shown to be feasible and acceptable.
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Affiliation(s)
- Abdullah N. Almulhim
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield S1 4DA, UK
- Public Health Department, College of Health Sciences, Saudi Electronic University, Riyadh 13316, Saudi Arabia
| | - Elizabeth Goyder
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield S1 4DA, UK
| | - Samantha J. Caton
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield S1 4DA, UK
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11
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Blonde L, Umpierrez GE, Reddy SS, McGill JB, Berga SL, Bush M, Chandrasekaran S, DeFronzo RA, Einhorn D, Galindo RJ, Gardner TW, Garg R, Garvey WT, Hirsch IB, Hurley DL, Izuora K, Kosiborod M, Olson D, Patel SB, Pop-Busui R, Sadhu AR, Samson SL, Stec C, Tamborlane WV, Tuttle KR, Twining C, Vella A, Vellanki P, Weber SL. American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. Endocr Pract 2022; 28:923-1049. [PMID: 35963508 PMCID: PMC10200071 DOI: 10.1016/j.eprac.2022.08.002] [Citation(s) in RCA: 152] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 02/06/2023]
Abstract
OBJECTIVE The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers. METHODS The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development. RESULTS This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes. CONCLUSIONS This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.
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Affiliation(s)
| | | | - S Sethu Reddy
- Central Michigan University, Mount Pleasant, Michigan
| | | | | | | | | | | | - Daniel Einhorn
- Scripps Whittier Diabetes Institute, La Jolla, California
| | | | | | - Rajesh Garg
- Lundquist Institute/Harbor-UCLA Medical Center, Torrance, California
| | | | | | | | | | | | - Darin Olson
- Colorado Mountain Medical, LLC, Avon, Colorado
| | | | | | - Archana R Sadhu
- Houston Methodist; Weill Cornell Medicine; Texas A&M College of Medicine; Houston, Texas
| | | | - Carla Stec
- American Association of Clinical Endocrinology, Jacksonville, Florida
| | | | - Katherine R Tuttle
- University of Washington and Providence Health Care, Seattle and Spokane, Washington
| | | | | | | | - Sandra L Weber
- University of South Carolina School of Medicine-Greenville, Prisma Health System, Greenville, South Carolina
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Ventura-Abreu N, Biarnés M, Batlle-Ferrando S, Carrión-Donderis MT, Castro-Domínguez R, Muniesa MJ, Millá E, Moreno-Montañés J, Pazos M. External Validation and Clinical Applicability of Two Optical Coherence Tomography-Based Risk Calculators for Detecting Glaucoma. Transl Vis Sci Technol 2022; 11:14. [PMID: 35848905 PMCID: PMC9308015 DOI: 10.1167/tvst.11.7.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Purpose To clinically validate the diagnostic ability of two optical coherence tomography (OCT)-based glaucoma diagnostic calculators (GDCs). Methods We conducted a retrospective, consecutive sampling of 76 patients with primary open-angle glaucoma, 107 glaucoma suspects, and 67 controls. Demographics, reliable visual field testing, and macular and optic disc OCT were collected. The reference diagnosis was compared against the probability of having glaucoma obtained from two GDCs derived from multivariate logistic regressions using quantitative and qualitative (GDC1) or only quantitative (GDC2) OCT data. The discrimination (area under the curve [AUC]) and calibration (calibration plots) were compared for both calculators and the best OCT parameters. Results GDC2 was able to identify 46.9% more suspects and 14.7% more glaucomatous eyes than GDC1. Both GDCs obtained the highest discriminative ability in glaucomatous eyes (GDC1 AUC = 0.949; GDC2 = 0.943 vs inferior peripapillary retinal nerve fiber layer [pRNFL] = 0.931; P = 0.43). The discriminating ability was not as good for glaucoma suspects, but the GDCs were not inferior to pRNFL (GDC 1 AUC = 0.739; GDC2 = 0.730; inferior pRNFL = 0.760; P = 0.54) and GDC2 was still able to correctly identify up to 30.8% more cases than the conventional OCT classification. Calibration showed risk underestimation for both groups and calculators, but it was better in GDC2 and in patients with glaucoma. Conclusions OCT-based calculators showed an excellent diagnostic performance in glaucomatous eyes. GDC2 was able to identify approximately 30% more cases than the conventional pRNFL inferior OCT classification in both groups, suggesting a potential role of these composite scores in clinical practice. Translational Relevance These OCT-based calculators may improve glaucoma diagnosis in clinical care.
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Affiliation(s)
| | - Marc Biarnés
- Hospital Quirón-Teknon, Institut de la Màcula, Barcelona, Spain
| | | | | | | | | | - Elena Millá
- Ophthalmology Institute, Hospital Clínic Barcelona, Barcelona, Spain
| | | | - Marta Pazos
- Ophthalmology Institute, Hospital Clínic Barcelona, Barcelona, Spain
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13
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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: 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: 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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Rao BN, Quinn N, Januszewski AS, Peto T, Brazionis L, Aryal N, O'Connell RL, Li L, Summanen P, Scott R, O'Day J, Keech AC, Jenkins AJ. Retinopathy risk calculators in the prediction of sight-threatening diabetic retinopathy in type 2 diabetes: A FIELD substudy. Diabetes Res Clin Pract 2022; 186:109835. [PMID: 35314259 DOI: 10.1016/j.diabres.2022.109835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/08/2022] [Accepted: 03/16/2022] [Indexed: 01/09/2023]
Abstract
AIMS To evaluate the risk algorithm by Aspelund et al. for predicting sight-threatening diabetic retinopathy (STDR) in Type 2 diabetes (T2D), and to develop a new STDR prediction model. METHODS The Aspelund et al. algorithm was used to calculate STDR risk from baseline variables in 1012 participants in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) ophthalmological substudy, compared to on-trial STDR status, and receiver operating characteristic analysis performed. Using multivariable logistic regression, traditional risk factors and fenofibrate allocation as STDR predictors were evaluated, with bootstrap-based optimism-adjusted estimates of predictive performance calculated. RESULTS STDR developed in 28 participants. The Aspelund et al. algorithm predicted STDR at 2- and 5-years with area under the curve (AUC) 0.86 (95% CI 0.77-0.94) and 0.86 (0.81-0.92), respectively. In the second model STDR risk factors were any DR at baseline (OR 24.0 [95% CI 5.53-104]), HbA1c (OR 1.95 [1.43-2.64]) and male sex (OR 4.34 [1.32-14.3]), while fenofibrate (OR 0.13 [0.05-0.38]) was protective. This model had excellent discriminatory ability (AUC = 0.89). CONCLUSIONS The algorithm by Aspelund et al. predicts STDR well in the FIELD ophthalmology substudy. Logistic regression analysis found DR at baseline, male sex, and HbA1c were predictive of STDR and, fenofibrate was protective.
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Affiliation(s)
- Benjamin N Rao
- NHMRC Clinical Trials Centre, The University of Sydney, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia
| | - Nicola Quinn
- NHMRC Clinical Trials Centre, The University of Sydney, Australia; Centre for Public Health, Queens University, Belfast, Northern Ireland, United Kingdom
| | | | - Tunde Peto
- Centre for Public Health, Queens University, Belfast, Northern Ireland, United Kingdom
| | - Laima Brazionis
- Department of Medicine, St. Vincent's Hospital Campus, The University of Melbourne, Australia
| | - Nanda Aryal
- NHMRC Clinical Trials Centre, The University of Sydney, Australia
| | | | - Liping Li
- NHMRC Clinical Trials Centre, The University of Sydney, Australia
| | - Paula Summanen
- Department of Ophthalmology, Helsinki University Hospital, University of Helsinki, Finland
| | - Russell Scott
- Lipid and Diabetes Research Group, Christchurch Hospital, Christchurch, New Zealand
| | - Justin O'Day
- Department of Ophthalmology, Royal Victorian Eye and Ear Hospital, The University of Melbourne, Australia
| | - Anthony C Keech
- NHMRC Clinical Trials Centre, The University of Sydney, Australia
| | - Alicia J Jenkins
- NHMRC Clinical Trials Centre, The University of Sydney, Australia; Centre for Public Health, Queens University, Belfast, Northern Ireland, United Kingdom; Department of Medicine, St. Vincent's Hospital Campus, The University of Melbourne, Australia.
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Romero-Aroca P, Verges R, Maarof N, Vallas-Mateu A, Latorre A, Moreno-Ribas A, Sagarra-Alamo R, Basora-Gallisa J, Cristiano J, Baget-Bernaldiz M. Real-world outcomes of a clinical decision support system for diabetic retinopathy in Spain. BMJ Open Ophthalmol 2022; 7:e000974. [PMID: 35415265 PMCID: PMC8961111 DOI: 10.1136/bmjophth-2022-000974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/16/2022] [Indexed: 11/04/2022] Open
Abstract
ObjectiveThe aim of present study was to evaluate our clinical decision support system (CDSS) for predicting risk of diabetic retinopathy (DR). We selected randomly a real population of patients with type 2 diabetes (T2DM) who were attending our screening programme.Methods and analysisThe sample size was 602 patients with T2DM randomly selected from those who attended the DR screening programme. The algorithm developed uses nine risk factors: current age, sex, body mass index (BMI), duration and treatment of diabetes mellitus (DM), arterial hypertension, Glicated hemoglobine (HbA1c), urine–albumin ratio and glomerular filtration.ResultsThe mean current age of 67.03±10.91, and 272 were male (53.2%), and DM duration was 10.12±6.4 years, 222 had DR (35.8%). The CDSS was employed for 1 year. The prediction algorithm that the CDSS uses included nine risk factors: current age, sex, BMI, DM duration and treatment, arterial hypertension, HbA1c, urine–albumin ratio and glomerular filtration. The area under the curve (AUC) for predicting the presence of any DR achieved a value of 0.9884, the sensitivity of 98.21%, specificity of 99.21%, positive predictive value of 98.65%, negative predictive value of 98.95%, α error of 0.0079 and β error of 0.0179.ConclusionOur CDSS for predicting DR was successful when applied to a real population.
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Affiliation(s)
- Pedro Romero-Aroca
- Ophtalmology, Universitat Rovira i Virgili, Tarragona, Spain
- Ophthalmology, Hospital Universitario Sant Joan de Reus, Reus, Spain
- Ophthalmologhy, Institut de Investigacions Sanitaries Pere Virgili, Tarragona, Spain
| | - Raquel Verges
- Ophthalmology, Hospital Universitario Sant Joan de Reus, Reus, Spain
| | - Najlaa Maarof
- Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain
| | - Aida Vallas-Mateu
- Mathematics, Universitat Rovira i Virgili Escola Tecnica Superior Enginyeria, Tarragona, Catalunya, Spain
| | - Alex Latorre
- Informatics, Hospital Universitario Sant Joan de Reus, Reus, Catalunya, Spain
| | - Antonio Moreno-Ribas
- Mathematics, Universitat Rovira i Virgili Escola Tecnica Superior Enginyeria, Tarragona, Catalunya, Spain
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16
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Romero-Aroca P, Baget-Bernaldiz M, Navarro-Gil R, Feliu A, Maarof N, Moreno A, Cristiano J, Valls A. Validation of an Algorithm for the Prediction of Diabetic Retinopathy in Type 1 Diabetic Patients in a Spanish Population. Clin Ophthalmol 2022; 16:715-722. [PMID: 35300029 PMCID: PMC8921832 DOI: 10.2147/opth.s351790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 03/02/2022] [Indexed: 11/23/2022] Open
Affiliation(s)
- Pedro Romero-Aroca
- Ophthalmology Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain
- Correspondence: Pedro Romero-Aroca, Hospital Universitario Sant Joan de Reus, Avda. Dr. Josep Laporte 2, Reus, 43204, Spain, Email
| | - Marc Baget-Bernaldiz
- Ophthalmology Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain
| | - Raul Navarro-Gil
- Ophthalmology Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain
| | - Albert Feliu
- Pediatric Department, University Hospital Sant Joan, Institute of Health Research Pere Virgili (IISPV), Universitat Rovira & Virgili, Tarragona, Spain
| | - Najla Maarof
- Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain
| | - Antonio Moreno
- Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain
| | - Julian Cristiano
- Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain
| | - Aida Valls
- Department of Computer Engineering and Mathematics, Universitat Rovira & Virgili, ITAKA-Intelligent Technologies for Advanced Knowledge Acquisition, Tarragona, Spain
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17
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Zhu X, Xu Y, Lu L, Zou H. Telescreening satisfaction: disparities between individuals with diabetic retinopathy and community health center staff. BMC Health Serv Res 2022; 22:160. [PMID: 35130901 PMCID: PMC8822836 DOI: 10.1186/s12913-022-07500-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 01/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background The success of telescreening and the management of diabetic retinopathy (DR) in communities depends on stakeholder satisfaction, including both individuals with diabetes and community health center (CHC) staff. In this study, we investigated the satisfaction of both individuals with vision-threatening DR (VTDR) and CHC staff within the Shanghai Eye Disease Study (SEDS) comprehensive system for managing diabetic eye diseases at the primary care level. Methods The cross-sectional survey of patients receiving the service included 3,817 respondents with VTDR and focused on their satisfaction with the SEDS system, including the telescreening process, speed of feedback, interpretation of results, increased awareness of related diseases, and eye care services. The survey of the providers included 234 CHC staff respondents and focused on their satisfaction and the main barriers encountered during the implementation of the system. Sociodemographic characteristics and perceived barriers related to satisfaction were identified by conducting univariate and multivariate logistic regression analyses. Results The overall satisfaction of service recipients was 96.0%, and 75.8% of them were willing to undergo future telescreening for DR. The convenience of telescreening, organization of telescreening, and improvement in related disease awareness significantly correlated with satisfaction. Only 48.3% of the providers were satisfied with the SEDS system. The most frequently mentioned barriers to the development of the system were the inadequate levels of staffing (particularly technical staff), insufficient funding, and incomplete information transmission systems. Conclusions Disparities between high patient satisfaction and low provider satisfaction with the SEDS system were mainly related to the current weak level of ophthalmic expertise in the CHCs and the low awareness of screening for diabetic eye diseases among both patients and providers. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-07500-w.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.,National Clinical Research Center for Eye Diseases, Shanghai, China.,Shanghai Key Laboratory of Fundus Diseases, Shanghai, China
| | - Yi Xu
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China.,Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lina Lu
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China. .,Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Haidong Zou
- Department of Preventative Ophthalmology, Shanghai Eye Disease Prevention and Treatment Center/Shanghai Eye Hospital, Shanghai, China. .,Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China. .,National Clinical Research Center for Eye Diseases, Shanghai, China. .,Shanghai Key Laboratory of Fundus Diseases, Shanghai, China.
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18
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Agarwal D, Kumar A, Kumar A. Commentary: Targeted high-risk screening for diabetic retinopathy in India: Feasible short-term strategy. Indian J Ophthalmol 2021; 69:3165. [PMID: 34708763 PMCID: PMC8725084 DOI: 10.4103/ijo.ijo_2556_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Divya Agarwal
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, Ansari Nagar, India
| | - Aman Kumar
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, Ansari Nagar, India
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19
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Ramasamy K, Mishra C, Kannan NB, Namperumalsamy P, Sen S. Telemedicine in diabetic retinopathy screening in India. Indian J Ophthalmol 2021; 69:2977-2986. [PMID: 34708732 PMCID: PMC8725153 DOI: 10.4103/ijo.ijo_1442_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
With ever-growing prevalence of diabetes mellitus and its most common microvascular complication diabetic retinopathy (DR) in Indian population, screening for DR early for prevention of development of vision-threatening stages of the disease is becoming increasingly important. Most of the programs in India for DR screening are opportunistic and a universal screening program does not exist. Globally, telemedicine programs have demonstrated accuracy in classification of DR into referable disease, as well as into stages, with accuracies reaching that of human graders, in a cost-effective manner and with sufficient patient satisfaction. In this major review, we have summarized the global experience of telemedicine in DR screening and the way ahead toward planning a national integrated DR screening program based on telemedicine.
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Affiliation(s)
- Kim Ramasamy
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Chitaranjan Mishra
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Naresh B Kannan
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - P Namperumalsamy
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
| | - Sagnik Sen
- Department of Retina and Vitreous Services, Aravind Eye Hospital, Madurai, Tamil Nadu, India
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20
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Sivaprasad S, Netuveli G, Wittenberg R, Khobragade R, Sadanandan R, Gopal B, Premnazir L, Conroy D, Srinath J, Ramakrishnan R, George S, Sahasranamam VI. Complex interventions to implement a diabetic retinopathy care pathway in the public health system in Kerala: the Nayanamritham study protocol. BMJ Open 2021; 11:e040577. [PMID: 34183333 PMCID: PMC8240569 DOI: 10.1136/bmjopen-2020-040577] [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] [Indexed: 11/03/2022] Open
Abstract
INTRODUCTION Using a type 2 hybrid effectiveness-implementation design, we aim to pilot a diabetic retinopathy (DR) care pathway in the public health system in Kerala to understand how it can be scaled up to and sustained in the whole state. METHODS AND ANALYSIS Currently, there is no systematic DR screening programme in Kerala. Our intervention is a teleophthalmology pathway for people with diabetes in the non-communicable disease registers in 16 family health centres. The planned implementation strategy of the pathway will be developed based on the discrete Expert Recommendations for Implementing Change taxonomy. We will use both quantitative data from a cross-sectional study and qualitative data obtained from structured interviews, surveys and group discussions with stakeholders to report the effectiveness of the DR care pathway and evaluation of the implementation strategy.We will use logistic regression models to assess crude associations DR and sight-threatening diabetic retinopathy and fractional polynomials to account for the form of continuous covariates to predict uptake of DR screening. The primary effectiveness outcome is the proportion of patients in the non-communicable disease register with diabetes screened for DR over 12 months. Other outcomes include cost-effectiveness, safety, efficiency, patient satisfaction, timeliness and equity. The outcomes of evaluation of the implementation strategies include acceptability, feasibility, adoption, appropriateness, fidelity, penetration, costs and sustainability. Addition of more family health centres during the staggered initial phase of the programme will be considered as a sign of acceptability and feasibility. In the long term, the state-wide adoption of the DR care pathway will be considered as a successful outcome of the Nayanamritham study. ETHICS AND DISSEMINATION The study was approved by Indian Medical Research Council (2018-0551) dated 13 March 2019. Study findings will be disseminated through scientific publications and the report will inform adoption of the DR care pathway by Kerala state in future. TRIAL REGISTRATION NUMBER ISRCTN28942696.
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Affiliation(s)
- Sobha Sivaprasad
- Medical Retina Department, NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital, London, UK
- Vision Sciences, UCL, London, UK
| | - Gopalakrishnan Netuveli
- Institute of Connected Communities, University of East London-Duncan House Campus, London, UK
| | - Raphael Wittenberg
- Nuffield Department of Primary Health Care Sciences, Oxford University, Oxford, Oxfordshire, UK
| | - Rajan Khobragade
- Directorate of Health, Government of Kerala, Thiruvananthapuram, India
| | - Rajeev Sadanandan
- Directorate of Health, Government of Kerala, Thiruvananthapuram, India
| | - Bipin Gopal
- Non-Communicable Diseases Department, Directorate of Health Services, Government Medical College Thiruvananthapuram, Thiruvananthapuram, India
| | - Lakshmi Premnazir
- Directorate of Health Services, Government Medical College Thiruvananthapuram, Thiruvananthapuram, India
| | | | - Jyotsna Srinath
- Institute of Connected Communities, University of East London-Duncan House Campus, London, UK
| | | | - Simon George
- Ophthalmology Department, Regional Institute of Ophthalmology, Government Medical College, Thiruvananthapuram, India
| | - Vasudeva Iyer Sahasranamam
- Ophthalmology Department, Regional Institute of Ophthalmology, Government Medical College, Thiruvananthapuram, India
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21
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McKay AJ, Gunn LH, Nugawela MD, Sathish T, Majeed A, P. Vamos E, Molina G, Sivaprasad S. Associations between attainment of incentivized primary care indicators and incident sight-threatening diabetic retinopathy in England: A population-based historical cohort study. Diabetes Obes Metab 2021; 23:1322-1330. [PMID: 33565708 PMCID: PMC8350793 DOI: 10.1111/dom.14344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 01/27/2021] [Accepted: 02/04/2021] [Indexed: 12/16/2022]
Abstract
AIM To examine the impact of attainment of primary care diabetes clinical indicators on progression to sight-threatening diabetic retinopathy (STDR) among those with mild non-proliferative diabetic retinopathy (NPDR). MATERIALS AND METHODS An historical cohort study of 18,978 adults (43.63% female) diagnosed with type 2 diabetes before 1 April 2010 and mild NPDR before 1 April 2011 was conducted. The data were obtained from the UK Clinical Practice Research Datalink during 2010-2017, provided by 330 primary care practices in England. Exposures included attainment of the Quality and Outcomes Framework HbA1c (≤59 mmol/mol [≤7.5%]), blood pressure (≤140/80 mmHg) and cholesterol (≤5 mmol/L) indicators in the financial year 2010-2011, as well as the number of National Diabetes Audit processes completed in 2010-2011. The outcome was time to incident STDR. Nearest neighbour propensity score matching was undertaken, and univariable and multivariable Cox proportional hazards models were then fitted using the matched samples. Concordance statistics were calculated for each model. RESULTS A total of 1037 (5.5%) STDR diagnoses were observed over a mean follow-up of 3.6 (SD 2.0) years. HbA1c, blood pressure and cholesterol indicator attainment were associated with lower rates of STDR (adjusted hazard ratios [95% CI] 0.64 [0.55-0.74; p < .001], 0.83 [0.72-0.94; p = .005] and 0.80 [0.66-0.96; p = .015], respectively). CONCLUSIONS Our findings provide support for meeting appropriate indicators for the management of type 2 diabetes in primary care to bring a range of benefits, including improved health outcomes-such as a reduction in the risk of STDR-for people with type 2 diabetes.
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Affiliation(s)
- Ailsa J. McKay
- Department of Primary Care and Public HealthImperial College LondonLondonUK
| | - Laura H. Gunn
- Department of Primary Care and Public HealthImperial College LondonLondonUK
- Department of Public Health Sciences and School of Data ScienceUniversity of North Carolina (UNC) at CharlotteCharlotteNorth CarolinaUSA
| | - Manjula D. Nugawela
- Institute of OphthalmologyUCL and NIHR Moorfields Biomedical Research CentreLondonUK
| | - Thirunavukkarasu Sathish
- Department of Primary Care and Public HealthImperial College LondonLondonUK
- Centre for Population Health Sciences (CePHaS), Lee Kong Chian School of MedicineNanyang Technological UniversitySingaporeSingapore
- Population Health Research Institute (PHRI)McMaster UniversityHamiltonOntarioCanada
| | - Azeem Majeed
- Department of Primary Care and Public HealthImperial College LondonLondonUK
| | - Eszter P. Vamos
- Department of Primary Care and Public HealthImperial College LondonLondonUK
| | | | - Sobha Sivaprasad
- Institute of OphthalmologyUCL and NIHR Moorfields Biomedical Research CentreLondonUK
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22
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Smith JJ, Wright DM, Stratton IM, Scanlon PH, Lois N. Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort. Br J Ophthalmol 2021; 106:1051-1056. [PMID: 33903145 PMCID: PMC9340042 DOI: 10.1136/bjophthalmol-2020-318570] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/19/2021] [Accepted: 02/11/2021] [Indexed: 12/17/2022]
Abstract
Background /Aims To evaluate the performance of existing prediction models to determine risk of progression to referable diabetic retinopathy (RDR) using data from a prospective Irish cohort of people with type 2 diabetes (T2D). Methods A cohort of 939 people with T2D followed prospectively was used to test the performance of risk prediction models developed in Gloucester, UK, and Iceland. Observed risk of progression to RDR in the Irish cohort was compared with that derived from each of the prediction models evaluated. Receiver operating characteristic curves assessed models’ performance. Results The cohort was followed for a total of 2929 person years during which 2906 screening episodes occurred. Among 939 individuals followed, there were 40 referrals (4%) for diabetic maculopathy, pre-proliferative DR and proliferative DR. The original Gloucester model, which includes results of two consecutive retinal screenings; a model incorporating, in addition, systemic biomarkers (HbA1c and serum cholesterol); and a model including results of one retinopathy screening, HbA1c, total cholesterol and duration of diabetes, had acceptable discriminatory power (area under the curve (AUC) of 0.69, 0.76 and 0.77, respectively). The Icelandic model, which combined retinopathy grading, duration and type of diabetes, HbA1c and systolic blood pressure, performed very similarly (AUC of 0.74). Conclusion In an Irish cohort of people with T2D, the prediction models tested had an acceptable performance identifying those at risk of progression to RDR. These risk models would be useful in establishing more personalised screening intervals for people with T2D.
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Affiliation(s)
- John J Smith
- Ophthalmology, Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, Northern Ireland, UK
| | - David M Wright
- Centre for Public Health, Queen's University Belfast, Belfast, Northern Ireland, UK
| | | | - Peter Henry Scanlon
- Ophthalmology, Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK
| | - Noemi Lois
- Ophthalmology, Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, Northern Ireland, UK
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23
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Jacoba CMP, Celi LA, Silva PS. Biomarkers for Progression in Diabetic Retinopathy: Expanding Personalized Medicine through Integration of AI with Electronic Health Records. Semin Ophthalmol 2021; 36:250-257. [PMID: 33734908 DOI: 10.1080/08820538.2021.1893351] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The goal of personalized diabetes eye care is to accurately predict in real-time the risk of diabetic retinopathy (DR) progression and visual loss. The use of electronic health records (EHR) provides a platform for artificial intelligence (AI) algorithms that predict DR progression to be incorporated into clinical decision-making. By implementing an algorithm on data points from each patient, their risk for retinopathy progression and visual loss can be modeled, allowing them to receive timely treatment. Data can guide algorithms to create models for disease and treatment that may pave the way for more personalized care. Currently, there exist numerous challenges that need to be addressed before reliably building and deploying AI algorithms, including issues with data quality, privacy, intellectual property, and informed consent.
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Affiliation(s)
- Cris Martin P Jacoba
- Joslin Diabetes Centre, Beetham Eye Institute, Boston, MA, USA.,Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Leo Anthony Celi
- Division of Pulmonary, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.,Laboratory for Computational Physiology, Harvard-MIT Health Sciences and Technology Division, Cambridge, MA, USA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Paolo S Silva
- Joslin Diabetes Centre, Beetham Eye Institute, Boston, MA, USA.,Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
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24
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Sharif A, Jendle J, Hellgren KJ. Screening for Diabetic Retinopathy with Extended Intervals, Safe and Without Compromising Adherence: A Retrospective Cohort Study. Diabetes Ther 2021; 12:223-234. [PMID: 33165837 PMCID: PMC7649703 DOI: 10.1007/s13300-020-00957-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 10/21/2020] [Indexed: 12/31/2022] Open
Abstract
INTRODUCTION Screening for diabetic retinopathy (DR) prevents blindness through the early detection of sight-threatening retinal microvascular lesions that respond to timely local treatment. However, the provision of easy and regular access to DR screening programs is currently being challenged by the increasing prevalence of diabetes. One proposed solution is to extend the screening interval for patients at low risk for progression of retinopathy. To date, most providers of screening programs have hesitated to implement a strategy of extended intervals due to the lack of data on whether adherence and safety are compromised when retinal examinations occur less frequently. In the study reported here, we investigated adherence to the screening program and progression of retinopathy in patients with type 2 diabetes participating in a DR screening program with extended intervals. METHODS This was a retrospective study that included 1000 patients with type 2 diabetes mellitus who attended a screening program for DR. The patients were consecutively placed into a low-risk patient cohort with no retinopathy or into an intermediate-risk patient cohort with mild retinopathy (each cohort n = 500). Screening intervals were 36 months for the low-risk cohort and 18 months for the intermediate-risk cohort. RESULTS The 1000 subjects enrolled in the study had a median age of 68 (interquartile range 12) years and 60.4% were men. At the follow-up screening visit, data on 102 subjects were not included in the analysis of adherence rate due to death, severe systemic illness, other concurrent eye disease or migration. Among the 898 remaining subjects, adherence to the screening program was 93.7% (413/443) in the 36-month group and 98.3% (449/455) in the 18-month group (p < 0.0001). Non-adherence decreased with increasing age (odds ratio 0.92, 95% confidence interval 0.888-0.954, p = 0.0005). At follow-up, 65 subjects showed progression of retinopathy; none had worse than moderate retinopathy. Risk factors for DR and treatment for hyperglycemia, hypertension and hyperlipidemia were compared among subjects in the low-risk cohort: non-adherent subjects did not differ from their adherent counterparts without progression of DR, but the former had a shorter duration of diabetes and higher diastolic blood pressure than adherent subjects with progression of DR (4.5 vs. 7.5 years, p = 0.007; and 80 vs. 75 mmHg, p = 0.02, respectively). CONCLUSION The results suggest that screening DR at extended intervals can be achieved with high adherence rates without compromising patient safety. However, younger subjects and those at higher risk of progression may require extra attention.
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Affiliation(s)
- Ali Sharif
- Department of Ophthalmology, Karlstad Hospital, Region Värmland, Karlstad, Sweden.
- Diabetes Endocrinology and Metabolism Research Center, Örebro University, Örebro, Sweden.
- Institute of Medical Sciences, Örebro University, Örebro, Sweden.
| | - Johan Jendle
- Diabetes Endocrinology and Metabolism Research Center, Örebro University, Örebro, Sweden
- Institute of Medical Sciences, Örebro University, Örebro, Sweden
| | - Karl-Johan Hellgren
- Department of Ophthalmology, Karlstad Hospital, Region Värmland, Karlstad, Sweden
- Diabetes Endocrinology and Metabolism Research Center, Örebro University, Örebro, Sweden
- Institute of Medical Sciences, Örebro University, Örebro, Sweden
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25
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Limwattanayingyong J, Nganthavee V, Seresirikachorn K, Singalavanija T, Soonthornworasiri N, Ruamviboonsuk V, Rao C, Raman R, Grzybowski A, Schaekermann M, Peng LH, Webster DR, Semturs C, Krause J, Sayres R, Hersch F, Tiwari R, Liu Y, Ruamviboonsuk P. Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders. J Diabetes Res 2020; 2020:8839376. [PMID: 33381600 PMCID: PMC7758133 DOI: 10.1155/2020/8839376] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/06/2020] [Accepted: 11/30/2020] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR. METHODS We randomly selected patients with diabetes screened twice, two years apart within a nationwide screening program. The reference standard was established via adjudication by retina specialists. Each patient's color fundus photographs were graded, and a patient was considered as having sight-threatening DR (STDR) if the worse eye had severe nonproliferative DR, proliferative DR, or diabetic macular edema. We compared DR screening via two modalities: DL and HG. For each modality, we simulated treatment referral by excluding patients with detected STDR from the second screening using that modality. RESULTS There were 5,738 patients (12.3% STDR) in the first screening. DL and HG captured different numbers of STDR cases, and after simulated referral and excluding ungradable cases, 4,148 and 4,263 patients remained in the second screening, respectively. The STDR prevalence at the second screening was 5.1% and 6.8% for DL- and HG-based screening, respectively. Along with the prevalence decrease, the sensitivity for both modalities decreased from the first to the second screening (DL: from 95% to 90%, p = 0.008; HG: from 74% to 57%, p < 0.001). At both the first and second screenings, the rate of false negatives for the DL was a fifth that of HG (0.5-0.6% vs. 2.9-3.2%). CONCLUSION On 2-year longitudinal follow-up of a DR screening cohort, STDR prevalence decreased for both DL- and HG-based screening. Follow-up screenings in longitudinal DR screening can be more difficult and induce lower sensitivity for both DL and HG, though the false negative rate was substantially lower for DL. Our data may be useful for health-economics analyses of longitudinal screening settings.
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Affiliation(s)
- Jirawut Limwattanayingyong
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Variya Nganthavee
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Kasem Seresirikachorn
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
| | - Tassapol Singalavanija
- Department of Ophthalmology, Chulabhorn Hospital, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | | | - Varis Ruamviboonsuk
- Department of Biochemistry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Chetan Rao
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland
| | | | | | | | | | | | | | | | - Richa Tiwari
- Work done at Google via Optimum Solutions Pte Ltd, Singapore
| | - Yun Liu
- Google Health, Palo Alto, CA, USA
| | - Paisan Ruamviboonsuk
- Department of Ophthalmology, College of Medicine, Rangsit University, Rajavithi Hospital, Bangkok, Thailand
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Emamipour S, van der Heijden AAWA, Nijpels G, Elders P, Beulens JWJ, Postma MJ, van Boven JFM, Feenstra TL. A personalised screening strategy for diabetic retinopathy: a cost-effectiveness perspective. Diabetologia 2020; 63:2452-2461. [PMID: 32734441 PMCID: PMC7527375 DOI: 10.1007/s00125-020-05239-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 06/10/2020] [Indexed: 01/06/2023]
Abstract
AIMS/HYPOTHESIS In this study we examined the cost-effectiveness of three different screening strategies for diabetic retinopathy: using a personalised adaptive model, annual screening (fixed intervals), and the current Dutch guideline (stratified based on previous retinopathy grade). METHODS For each individual, optimal diabetic retinopathy screening intervals were determined, using a validated risk prediction model. Observational data (1998-2017) from the Hoorn Diabetes Care System cohort of people with type 2 diabetes were used (n = 5514). The missing values of retinopathy grades were imputed using two scenarios of slow and fast sight-threatening retinopathy (STR) progression. By comparing the model-based screening intervals to observed time to develop STR, the number of delayed STR diagnoses was determined. Costs were calculated using the healthcare perspective and the societal perspective. Finally, outcomes and costs were compared for the different screening strategies. RESULTS For the fast STR progression scenario, personalised screening resulted in 11.6% more delayed STR diagnoses and €11.4 less costs per patient compared to annual screening from a healthcare perspective. The personalised screening model performed better in terms of timely diagnosis of STR (8.8% less delayed STR diagnosis) but it was slightly more expensive (€1.8 per patient from a healthcare perspective) than the Dutch guideline strategy. CONCLUSIONS/INTERPRETATION The personalised diabetic retinopathy screening model is more cost-effective than the Dutch guideline screening strategy. Although the personalised screening strategy was less effective, in terms of timely diagnosis of STR patients, than annual screening, the number of delayed STR diagnoses is low and the cost saving is considerable. With around one million people with type 2 diabetes in the Netherlands, implementing this personalised model could save €11.4 million per year compared with annual screening, at the cost of 658 delayed STR diagnoses with a maximum delayed time to diagnosis of 48 months.
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Affiliation(s)
- Sajad Emamipour
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands.
| | - Amber A W A van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, location VU, Amsterdam, the Netherlands
| | - Giel Nijpels
- Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, location VU, Amsterdam, the Netherlands
| | - Petra Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, location VU, Amsterdam, the Netherlands
| | - Joline W J Beulens
- Department of General Practice and Elderly Care Medicine, Amsterdam University Medical Center, location VU, Amsterdam, the Netherlands
| | - Maarten J Postma
- Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, the Netherlands
- Department of Economics, Econometrics & Finance, Faculty of Economics & Business, University of Groningen, Groningen, the Netherlands
| | - Job F M van Boven
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, the Netherlands
| | - Talitha L Feenstra
- Faculty of Science and Engineering, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, the Netherlands
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
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Bek T. Systemic risk factors contribute differently to the development of proliferative diabetic retinopathy and clinically significant macular oedema. Diabetologia 2020; 63:2462-2470. [PMID: 32696115 DOI: 10.1007/s00125-020-05234-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 06/03/2020] [Indexed: 02/06/2023]
Abstract
AIMS/HYPOTHESIS The purpose of screening for diabetic retinopathy is to detect either of the two sight-threatening complications: proliferative diabetic retinopathy (PDR) or clinically significant diabetic macular oedema (DME). The aim of the study was to evaluate whether systemic risk factors affect the risk of developing these two complications differently. METHODS Survival analysis with death as a competing risk was used to describe the effect of sex, age and time of onset of diabetes, systolic (SBP) and diastolic (DBP) BPs, and the weighted exposure and CV of HbA1c for the development of PDR and DME from all 2773 patients treated for diabetic retinopathy in a defined population from the Aarhus area, Denmark, between 1 July 1994 and 1 July 2019. RESULTS Increasing HbA1c above normal increased the risk of developing both PDR and DME (p < 0.04), and values below normal increased the risk of developing PDR (p < 0.013). Increasing DBP increased the risk of developing both PDR and DME (p < 0.0001), whereas increasing SBP increased the risk of developing DME (p < 0.0001), but not PDR (p > 0.08). The risk of developing PDR increased with decreasing age of onset of diabetes (p < 0.0001), whereas the risk of developing DME was maximal for a known onset of diabetes at about 30 years of age and decreased significantly for both lower and higher ages of onset (p < 0.0001). The risk of developing both PDR and DME was lower in women than in men (p < 0.004) and was reduced with lower variability of repeated HbA1c measurements (p < 0.0001). CONCLUSIONS/INTERPRETATION Systemic risk factors such as metabolic regulation, arterial BP and the age of onset of diabetes contribute differently to the development of PDR and DME. The overall risk of developing treatment-requiring diabetic retinopathy should be calculated from the risks of reaching each of the two complications separately.
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Affiliation(s)
- Toke Bek
- Department of Ophthalmology, Aarhus University Hospital, DK-8200, Aarhus N, Denmark.
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28
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Thomas RL, Winfield TG, Prettyjohns M, Dunstan FD, Cheung WY, Anderson PM, Peter R, Luzio SD, Owens DR. Cost-effectiveness of biennial screening for diabetes related retinopathy in people with type 1 and type 2 diabetes compared to annual screening. THE EUROPEAN JOURNAL OF HEALTH ECONOMICS : HEPAC : HEALTH ECONOMICS IN PREVENTION AND CARE 2020; 21:993-1002. [PMID: 32385543 PMCID: PMC7423794 DOI: 10.1007/s10198-020-01191-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 04/21/2020] [Indexed: 05/05/2023]
Abstract
OBJECTIVE Examine the health and economic impact of extending screening intervals in people with Type 2 diabetes (T2DM) and Type 1 diabetes (T1DM) without diabetes-related retinopathy (DR). SETTING Diabetic Eye Screening Wales (DESW). STUDY DESIGN Retrospective observational study with cost-utility analysis (CUA) and Decremental Cost-Effectiveness Ratios (DCER) study. INTERVENTION Biennial screening versus usual care (annual screening). INPUTS Anonymised data from DESW were linked to primary care data for people with two prior screening events with no DR. Transition probabilities for progression to DR were estimated based on a subset of 26,812 and 1232 people with T2DM and T1DM, respectively. DCER above £20,000 per QALY was considered cost-effective. RESULTS The base case analysis DCER results of £71,243 and £23,446 per QALY for T2DM and T1DM respectively at a 3.5% discount rate and £56,822 and £14,221 respectively when discounted at 1.5%. Diabetes management represented by the mean HbA1c was 7.5% for those with T2DM and 8.7% for T1DM. SENSITIVITY ANALYSIS Extending screening to biennial based on HbA1c, being the strongest predictor of progression of DR, at three levels of HbA1c 6.5%, 8.0% and 9.5% lost one QALY saving the NHS £106,075; £58,653 and £31,626 respectively for T2DM and £94,696, £37,646 and £11,089 respectively for T1DM. In addition, extending screening to biennial based on the duration of diabetes > 6 years for T2DM per QALY lost, saving the NHS £54,106 and for 6-12 and > 12 years for T1DM saving £83,856, £23,446 and £13,340 respectively. CONCLUSIONS Base case and sensitivity analyses indicate biennial screening to be cost-effective for T2DM irrespective of HbA1c and duration of diabetes. However, the uncertainty around the DCER indicates that annual screening should be maintained for those with T1DM especially when the HbA1c exceeds 80 mmol/mol (9.5%) and duration of diabetes is greater than 12 years.
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Affiliation(s)
- Rebecca L Thomas
- Diabetes Research Unit Cymru, Swansea University Medical School, Singleton Park, Swansea, SA2 8PP, UK.
| | | | | | - Frank D Dunstan
- Institute of Primary Care and Public Health, Cardiff University, Heath Park, Cardiff, CF14 4XN, UK
| | - Wai-Yee Cheung
- Diabetes Research Unit Cymru, Swansea University Medical School, Singleton Park, Swansea, SA2 8PP, UK
| | - Philippa M Anderson
- Swansea Centre for Health Economics, College of Human and Health Sciences, Swansea University, Singleton Park, Swansea, SA2 8PP, UK
| | - Rajesh Peter
- Swansea Bay University Health Board, Neath Port Talbot Hospital, Baglan Way, Port Talbot, West Glamorgan, SA12 7BX, UK
| | - Stephen D Luzio
- Diabetes Research Unit Cymru, Swansea University Medical School, Singleton Park, Swansea, SA2 8PP, UK
| | - David R Owens
- Diabetes Research Unit Cymru, Swansea University Medical School, Singleton Park, Swansea, SA2 8PP, UK
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Huemer J, Wagner SK, Sim DA. The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence. Clin Ophthalmol 2020; 14:2021-2035. [PMID: 32764868 PMCID: PMC7381763 DOI: 10.2147/opth.s261629] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 07/01/2020] [Indexed: 12/14/2022] Open
Abstract
As a third of people with diabetes mellitus (DM) will suffer the microvascular complications of diabetic retinopathy (DR) and therapeutic options can effectively prevent visual impairment, systematic screening has substantially reduced disease burden in developed countries. In an effort to tackle the rising incidence of DM, screening programmes have modernized in synchrony with technical and infrastructural advancements. Patient evaluation has shifted from face-to-face ophthalmologist-based review delivered through community grassroots to asynchronous store-and-forward modern telemedicine platforms commissioned on a nationwide scale. First pioneered with primitive 35-mm slide film retinal photography, the last decade has seen an emergence of high resolution and widefield imaging devices, which may reveal extents of DR indiscernible to the clinician but with implications of potential earlier identification. Similar progress has been seen in image analysis approaches - automated image analysis of retinal photographs of DR has evolved from qualitative feature detection to rules-based algorithms to autonomous artificial intelligence-powered classification. Such models have, relatively rapidly, been validated and are now receiving approval from health regulation authorities with deployment into the clinical sphere. In this review, we chart the evolution of global DR screening programmes since their inception highlighting major milestones in healthcare infrastructure, telemedicine approaches and imaging devices that have shaped the robust and effective frameworks recognised today. We also provide an outlook for the future of DR screening in the context of recent technological advancements with respect to their limitations in current times.
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Affiliation(s)
- Josef Huemer
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Vienna Institute for Research in Ocular Surgery, A Karl Landsteiner Institute, Hanusch Hospital, Vienna, Austria
| | - Siegfried K Wagner
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Dawn A Sim
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
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Lanzetta P, Sarao V, Scanlon PH, Barratt J, Porta M, Bandello F, Loewenstein A. Fundamental principles of an effective diabetic retinopathy screening program. Acta Diabetol 2020; 57:785-798. [PMID: 32222818 PMCID: PMC7311555 DOI: 10.1007/s00592-020-01506-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 02/14/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Diabetic retinopathy (DR) is the leading cause of blindness among working-age adults worldwide. Early detection and treatment are necessary to forestall vision loss from DR. METHODS A working group of ophthalmic and diabetes experts was established to develop a consensus on the key principles of an effective DR screening program. Recommendations are based on analysis of a structured literature review. RESULTS The recommendations for implementing an effective DR screening program are: (1) Examination methods must be suitable for the screening region, and DR classification/grading systems must be systematic and uniformly applied. Two-field retinal imaging is sufficient for DR screening and is preferable to seven-field imaging, and referable DR should be well defined and reliably identifiable by qualified screening staff; (2) in many countries/regions, screening can and should take place outside the ophthalmology clinic; (3) screening staff should be accredited and show evidence of ongoing training; (4) screening programs should adhere to relevant national quality assurance standards; (5) studies that use uniform definitions of risk to determine optimum risk-based screening intervals are required; (6) technology infrastructure should be in place to ensure that high-quality images can be stored securely to protect patient information; (7) although screening for diabetic macular edema (DME) in conjunction with DR evaluations may have merit, there is currently insufficient evidence to support implementation of programs solely for DME screening. CONCLUSION Use of these recommendations may yield more effective DR screening programs that reduce the risk of vision loss worldwide.
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Affiliation(s)
- Paolo Lanzetta
- Department of Medicine - Ophthalmology, University of Udine, Piazzale S. Maria della Misericordia, 33100, Udine, Italy.
- Istituto Europeo di Microchirurgia Oculare (IEMO), Udine, Italy.
| | - Valentina Sarao
- Department of Medicine - Ophthalmology, University of Udine, Piazzale S. Maria della Misericordia, 33100, Udine, Italy
- Istituto Europeo di Microchirurgia Oculare (IEMO), Udine, Italy
| | - Peter H Scanlon
- Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK
| | - Jane Barratt
- International Federation on Ageing, Toronto, Canada
| | - Massimo Porta
- Department of Medical Sciences, University of Turin, Turin, Italy
| | | | - Anat Loewenstein
- Department of Ophthalmology Tel Aviv Medical Center, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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van der Heijden AA, Nijpels G, Badloe F, Lovejoy HL, Peelen LM, Feenstra TL, Moons KGM, Slieker RC, Herings RMC, Elders PJM, Beulens JW. Prediction models for development of retinopathy in people with type 2 diabetes: systematic review and external validation in a Dutch primary care setting. Diabetologia 2020; 63:1110-1119. [PMID: 32246157 PMCID: PMC7228897 DOI: 10.1007/s00125-020-05134-3] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Accepted: 02/21/2020] [Indexed: 12/14/2022]
Abstract
AIMS/HYPOTHESIS The aims of this study were to identify all published prognostic models predicting retinopathy risk applicable to people with type 2 diabetes, to assess their quality and accuracy, and to validate their predictive accuracy in a head-to-head comparison using an independent type 2 diabetes cohort. METHODS A systematic search was performed in PubMed and Embase in December 2019. Studies that met the following criteria were included: (1) the model was applicable in type 2 diabetes; (2) the outcome was retinopathy; and (3) follow-up was more than 1 year. Screening, data extraction (using the checklist for critical appraisal and data extraction for systemic reviews of prediction modelling studies [CHARMS]) and risk of bias assessment (by prediction model risk of bias assessment tool [PROBAST]) were performed independently by two reviewers. Selected models were externally validated in the large Hoorn Diabetes Care System (DCS) cohort in the Netherlands. Retinopathy risk was calculated using baseline data and compared with retinopathy incidence over 5 years. Calibration after intercept adjustment and discrimination (Harrell's C statistic) were assessed. RESULTS Twelve studies were included in the systematic review, reporting on 16 models. Outcomes ranged from referable retinopathy to blindness. Discrimination was reported in seven studies with C statistics ranging from 0.55 (95% CI 0.54, 0.56) to 0.84 (95% CI 0.78, 0.88). Five studies reported on calibration. Eight models could be compared head-to-head in the DCS cohort (N = 10,715). Most of the models underestimated retinopathy risk. Validating the models against different severities of retinopathy, C statistics ranged from 0.51 (95% CI 0.49, 0.53) to 0.89 (95% CI 0.88, 0.91). CONCLUSIONS/INTERPRETATION Several prognostic models can accurately predict retinopathy risk in a population-based type 2 diabetes cohort. Most of the models include easy-to-measure predictors enhancing their applicability. Tailoring retinopathy screening frequency based on accurate risk predictions may increase the efficiency and cost-effectiveness of diabetic retinopathy care. REGISTRATION PROSPERO registration ID CRD42018089122.
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Affiliation(s)
- Amber A van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, van der Boechorststraat 7, 1081 BT, Amsterdam, the Netherlands.
| | - Giel Nijpels
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, van der Boechorststraat 7, 1081 BT, Amsterdam, the Netherlands
| | - Fariza Badloe
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, van der Boechorststraat 7, 1081 BT, Amsterdam, the Netherlands
| | - Heidi L Lovejoy
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, van der Boechorststraat 7, 1081 BT, Amsterdam, the Netherlands
| | - Linda M Peelen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Talitha L Feenstra
- Groningen Research Institute of Pharmacy, University of Groningen, Groningen, the Netherlands
- Centre for Nutrition, Prevention and Health Services, Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Roderick C Slieker
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, Amsterdam, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ron M C Herings
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, Amsterdam, the Netherlands
- PHARMO Institute for Drug Outcomes Research, Utrecht, the Netherlands
| | - Petra J M Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, van der Boechorststraat 7, 1081 BT, Amsterdam, the Netherlands
| | - Joline W Beulens
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, Amsterdam UMC - location VUmc, Amsterdam, the Netherlands
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Estil S, Steinarsson ÆÞ, Einarsson S, Aspelund T, Stefánsson E. Diabetic eye screening with variable screening intervals based on individual risk factors is safe and effective in ophthalmic practice. Acta Ophthalmol 2020; 98:343-346. [PMID: 32216034 DOI: 10.1111/aos.14425] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 03/09/2020] [Indexed: 01/10/2023]
Abstract
PURPOSE To test in a 'real world' diabetic eye-screening programme, a computer-based personal risk evaluation for progression to sight-threatening diabetic retinopathy. Screening intervals were individualized, and clinical outcomes, safety and cost-effectiveness documented. METHODS The RETINARISK algorithm was used in an ophthalmology clinic in Norway. The diabetes cohort was divided on voluntary basis into two groups: one with variable screening intervals based on their personal risk profile and the other group with conventional fixed interval diabetic eye screening. Compliance, clinical outcomes, safety and health economics were evaluated. A total of 843 diabetic patients participated in the program 2014-2019. A total of 63 had type 1 and 780 type 2 diabetes. A total of 671 patients had no diabetic retinopathy at baseline and 171 had retinopathy. RESULTS A total of 444 (53%) diabetic patients were included in the personal risk profile program and 399 in the fixed interval group. The RETINARISK algorithm calculated 563 screening intervals for the variable interval group, which was 23 ± 16 months (mean ± SD), compared to 14 ± 5 months for the group with fixed screening intervals. Due to selection bias, the two groups could not be directly compared. We did not experience any delay in detecting diabetic retinal changes when using the personal risk profile program. CONCLUSION The RETINARISK algorithm was safe and effective in a diabetic screening program in an ophthalmology clinic over 5 years. The use of the program reduces the mean frequency of screening visits and liberates valuable time in ophthalmic practice to be used on high-risk diabetic patients or other patient groups.
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Affiliation(s)
- Svein Estil
- Robrygga Eye Namsos Medical Center Namsos Norway
| | | | | | - Thor Aspelund
- Risk ehf Reykjavík Iceland
- Landspitali University Hospital University of Iceland Reykjavik Iceland
| | - Einar Stefánsson
- Risk ehf Reykjavík Iceland
- Landspitali University Hospital University of Iceland Reykjavik Iceland
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Vujosevic S, Aldington SJ, Silva P, Hernández C, Scanlon P, Peto T, Simó R. Screening for diabetic retinopathy: new perspectives and challenges. Lancet Diabetes Endocrinol 2020; 8:337-347. [PMID: 32113513 DOI: 10.1016/s2213-8587(19)30411-5] [Citation(s) in RCA: 242] [Impact Index Per Article: 60.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/18/2019] [Accepted: 11/18/2019] [Indexed: 12/15/2022]
Abstract
Although the prevalence of all stages of diabetic retinopathy has been declining since 1980 in populations with improved diabetes control, the crude prevalence of visual impairment and blindness caused by diabetic retinopathy worldwide increased between 1990 and 2015, largely because of the increasing prevalence of type 2 diabetes, particularly in low-income and middle-income countries. Screening for diabetic retinopathy is essential to detect referable cases that need timely full ophthalmic examination and treatment to avoid permanent visual loss. In the past few years, personalised screening intervals that take into account several risk factors have been proposed, with good cost-effectiveness ratios. However, resources for nationwide screening programmes are scarce in many countries. New technologies, such as scanning confocal ophthalmology with ultrawide field imaging and handheld mobile devices, teleophthalmology for remote grading, and artificial intelligence for automated detection and classification of diabetic retinopathy, are changing screening strategies and improving cost-effectiveness. Additionally, emerging evidence suggests that retinal imaging could be useful for identifying individuals at risk of cardiovascular disease or cognitive impairment, which could expand the role of diabetic retinopathy screening beyond the prevention of sight-threatening disease.
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Affiliation(s)
- Stela Vujosevic
- Eye Unit, University Hospital Maggiore della Carità, Novara, Italy
| | - Stephen J Aldington
- Department of Ophthalmology, Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK
| | - Paolo Silva
- Beetham Eye Institute, Joslin Diabetes Centre, Harvard Medical School, Boston, MA, USA; Philippine Eye Research Institute, University of the Philippines, Manila, Philippines
| | - Cristina Hernández
- Diabetes and Metabolism Research Unit, Vall d'Hebron Research Institute, Barcelona, Spain; Department of Medicine and Endocrinology, Autonomous University of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
| | - Peter Scanlon
- Department of Ophthalmology, Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK
| | - Tunde Peto
- Centre for Public Health, Queen's University Belfast, Belfast, UK
| | - Rafael Simó
- Diabetes and Metabolism Research Unit, Vall d'Hebron Research Institute, Barcelona, Spain; Department of Medicine and Endocrinology, Autonomous University of Barcelona, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain.
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Kalavar M, Al-Khersan H, Sridhar J, Gorniak RJ, Lakhani PC, Flanders AE, Kuriyan AE. Applications of Artificial Intelligence for the Detection, Management, and Treatment of Diabetic Retinopathy. Int Ophthalmol Clin 2020; 60:127-145. [PMID: 33093322 PMCID: PMC8514105 DOI: 10.1097/iio.0000000000000333] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Rates of diabetic retinopathy (DR) and diabetic macular edema (DME), a common ocular complication of diabetes mellitus, are increasing worldwide. There is a substantial burden concerning the detection and management of this condition, particularly in low-resource settings, due to limitations such as the time, cost, and labor associated with current screening and treatment methods. Artificial intelligence (AI) is a modality of pattern recognition that has the potential to combat these limitations in a reliable and cost-effective way. This review explores the various applications of AI on the screening, management, and treatment of DR and DME. AI applications for detecting referable DR and DME have been the most thoroughly researched applications for this condition. While some studies exist using AI to stratify DR patients based on the risk of progression, predict treatment outcomes to anti-VEGF therapy, and explore the utilization of AI for clinical trials to develop new treatments for DR, further validation studies on larger datasets are warranted.
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Affiliation(s)
- Meghana Kalavar
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL
| | - Hasenin Al-Khersan
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL
| | - Jayanth Sridhar
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL
| | | | - Paras C. Lakhani
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA
| | - Adam E. Flanders
- Department of Radiology, Thomas Jefferson University, Philadelphia, PA
| | - Ajay E. Kuriyan
- Mid Atlantic Retina, Philadelphia, PA
- The Retina Service, Wills Eye Hospital, Philadelphia, PA
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA
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35
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Nijpels G, Beulens JWJ, van der Heijden AAWA, Elders PJ. Innovations in personalised diabetes care and risk management. Eur J Prev Cardiol 2019; 26:125-132. [DOI: 10.1177/2047487319880043] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Type 2 diabetes is associated with an increased risk of developing macro and microvascular complications. Nevertheless, there is substantial heterogeneity between people with type 2 diabetes in their risk of developing such complications. Personalised medicine for people with type 2 diabetes may aid in efficient and tailored diabetes care for those at increased risk of developing such complications. Recently, progress has been made in the development of personalised diabetes care in several areas. Particularly for the risk prediction of cardiovascular disease, retinopathy and nephropathy, innovative methods have been developed for prediction and tailored monitoring or treatment to prevent such complications. For other complications or subpopulations of people with type 2 diabetes, such as the frail elderly, efforts are currently ongoing to develop such methods. In this review, we discuss the recent developments in innovations of personalised diabetes care for different complications and subpopulations of people with type 2 diabetes, their performance and modes of application in clinical practice.
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Affiliation(s)
- Giel Nijpels
- Department of General Practice and Elderly Care Medicine, Amsterdam UMC – location VUmc, Amsterdam Public Health Research Institute, The Netherlands
| | - Joline WJ Beulens
- Department of Epidemiology and Biostatistics, Amsterdam UMC – location VUmc, Amsterdam Public Health Research Institute, The Netherlands
| | - Amber AWA van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam UMC – location VUmc, Amsterdam Public Health Research Institute, The Netherlands
| | - Petra J Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam UMC – location VUmc, Amsterdam Public Health Research Institute, The Netherlands
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Scanlon PH. Update on Screening for Sight-Threatening Diabetic Retinopathy. Ophthalmic Res 2019; 62:218-224. [PMID: 31132764 DOI: 10.1159/000499539] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 03/06/2019] [Indexed: 01/04/2023]
Abstract
PURPOSE The aim of this article was to describe recent advances in the use of new technology in diabetic retinopathy screening by looking at studies that assessed the effectiveness and cost-effectiveness of these technologies. METHODS The author conducts an ongoing search for articles relating to screening or management of diabetic retinopathy utilising Zetoc with keywords and contents page lists from relevant journals. RESULTS The areas discussed in this article are reference standards, alternatives to digital photography, area of retina covered by the screening method, size of the device and hand-held cameras, mydriasis versus non-mydriasis or a combination, measurement of distance visual acuity, grading of images, use of automated grading analysis and cost-effectiveness of the new technologies. CONCLUSIONS There have been many recent advances in technology that may be adopted in the future by screening programmes for sight-threatening diabetic retinopathy but each device will need to demonstrate effectiveness and cost-effectiveness before more widespread adoption.
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Affiliation(s)
- Peter H Scanlon
- Clinical Director English NHS Diabetic Eye Screening Programme, Cheltenham, United Kingdom, .,Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, United Kingdom, .,Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, United Kingdom, .,University of Gloucestershire, Cheltenham, United Kingdom,
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Sivaprasad S, Pearce E. The unmet need for better risk stratification of non-proliferative diabetic retinopathy. Diabet Med 2019; 36:424-433. [PMID: 30474144 PMCID: PMC6587728 DOI: 10.1111/dme.13868] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/22/2018] [Indexed: 12/14/2022]
Abstract
Diabetic retinopathy is a common microvascular complication of diabetes and remains one of the leading causes of preventable blindness in working-age people. Non-proliferative diabetic retinopathy is the earliest stage of diabetic retinopathy and is typically asymptomatic. Currently, the severity of diabetic retinopathy is assessed using semi-quantitative grading systems based on the presence or absence of retinal lesions. These methods are well validated, but do not predict those at high risk of rapid progression to sight-threatening diabetic retinopathy; therefore, new approaches for identifying these people are a current unmet need. We evaluated published data reporting the lesion characteristics associated with different progression profiles in people with non-proliferative diabetic retinopathy. Based on these findings, we propose that additional assessments of features of non-proliferative diabetic retinopathy lesions may help to stratify people based on the likelihood of rapid progression. In addition to the current classification, the following measurements should be considered: the shape and size of lesions; whether lesions are angiogenic in origin; the location of lesions, including predominantly peripheral lesions; and lesion turnover and dynamics. For lesions commonly seen in hypertensive retinopathy, a detailed assessment of potential concomitant diseases is also recommended. We believe that natural history studies of these changes will help characterize these non-proliferative diabetic retinopathy progression profiles and advance our understanding of the pathogenesis of diabetic retinopathy in order to individualize management of people with diabetic retinopathy.
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Affiliation(s)
- S. Sivaprasad
- Moorfields Eye HospitalLondonUK
- University College LondonLondonUK
| | - E. Pearce
- Moorfields Eye HospitalLondonUK
- University College LondonLondonUK
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Schreur V, Ng H, Nijpels G, Stefánsson E, Tack CJ, Klevering BJ, de Jong EK, Hoyng CB, Keunen JEE, van der Heijden AA. Validation of a model for the prediction of retinopathy in persons with type 1 diabetes. Br J Ophthalmol 2019; 105:1286-1288. [PMID: 30824420 DOI: 10.1136/bjophthalmol-2018-313539] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 01/30/2019] [Accepted: 02/12/2019] [Indexed: 11/03/2022]
Abstract
BACKGROUND/AIM To validate a previously developed model for prediction of diabetic retinopathy (DR) for personalised retinopathy screening in persons with type 1 diabetes. METHODS Retrospective medical data of persons with type 1 diabetes treated in an academic hospital setting were used for analysis. Sight-threatening retinopathy (STR) was defined as the presence of severe non-proliferative DR, proliferative DR or macular oedema. The presence and grade of retinopathy, onset of diabetes, systolic blood pressure, and levels of haemoglobin A1c were used to calculate an individual risk estimate and personalised screening interval. In persons with STR, the occurrence was compared with the calculated date of screening. The model's predictive performance was measured using calibration and discrimination techniques. RESULTS Of the 268 persons included in our study, 24 (9.0%) developed STR during a mean follow-up of 4.6 years. All incidences of STR occurred after the calculated screening date. By applying the model, the mean calculated screening interval was 30.5 months, which is a reduction in screening frequency of 61% compared with annual screening and 21% compared with biennial screening. The discriminatory ability was good (Harrell's C-statistic=0.82, 95% CI 0.74 to 0.90), and calibration showed an overestimation of risk in persons who were assigned to a higher risk for STR. CONCLUSION This validation study suggests that a screening programme based on the previously developed prediction model is safe and efficient. The use of a personalised screening frequency could improve cost-effectiveness of diabetic eye care.
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Affiliation(s)
- Vivian Schreur
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Heijan Ng
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Giels Nijpels
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, VU Medical Centre, Amsterdam, The Netherlands
| | - Einar Stefánsson
- Department of Ophthalmology, University of Iceland, Reykjavík, Iceland
| | - Cees J Tack
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - B Jeroen Klevering
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Eiko K de Jong
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Carel B Hoyng
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jan E E Keunen
- Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Amber A van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, VU Medical Centre, Amsterdam, The Netherlands
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Haider S, Sadiq SN, Moore D, Price MJ, Nirantharakumar K. Prognostic prediction models for diabetic retinopathy progression: a systematic review. Eye (Lond) 2019; 33:702-713. [PMID: 30651592 DOI: 10.1038/s41433-018-0322-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Revised: 11/10/2018] [Accepted: 11/19/2018] [Indexed: 12/25/2022] Open
Abstract
With the increasing incidence of diabetic retinopathy and its improved detection, there is increased demand for diabetic retinopathy treatment services. Prognostic prediction models have been used to optimise services but these were intended for early detection of sight-threatening retinopathy and are mostly used in diabetic retinopathy screening services. We wanted to look into the predictive ability and applicability of the existing models for the higher-risk patients referred into hospitals. We searched MEDLINE, EMBASE, COCHRANE CENTRAL, conference abstracts and reference lists of included publications for studies of any design using search terms related to diabetes, diabetic retinopathy and prognostic models. Search results were screened for relevance to the review question. Included studies had data extracted on model characteristics, predictive ability and validation. They were assessed for quality using criteria specified by PROBAST and CHARMS checklists, independently by two reviewers. Twenty-two articles reporting on 14 prognostic models (including four updates) met the selection criteria. Eleven models had internal validation, eight had external validation and one had neither. Discriminative ability with c-statistics ranged from 0.57 to 0.91. Studies ranged from low to high risk of bias, mostly due to the need for external validation or missing data. Participants, outcomes, predictors handling and modelling methods varied. Most models focussed on lower-risk patients, the majority had high risk of bias and doubtful applicability, but three models had some applicability for higher-risk patients. However, these models will also need updating and external validation in multiple hospital settings before being implemented into clinical practice.
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Affiliation(s)
- Sajjad Haider
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
| | | | - David Moore
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Malcolm James Price
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
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Grauslund J, Andersen N, Andresen J, Flesner P, Haamann P, Heegaard S, Larsen M, Laugesen CS, Schielke K, Skov J, Bek T. Evidence-based Danish guidelines for screening of diabetic retinopathy. Acta Ophthalmol 2018; 96:763-769. [PMID: 30311394 DOI: 10.1111/aos.13936] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 09/14/2018] [Indexed: 01/06/2023]
Abstract
PURPOSE Diabetic retinopathy (DR) is among the leading causes of visual loss in the working-age population. It is generally accepted that screening of DR is cost-effective and can detect DR before it becomes sight-threatening to allow timely treatment. METHODS A group of retinal specialists was formed by the Danish Ophthalmological Society with the aim to formulate contemporary evidence-based guidelines for screening of DR in order to implement these in the Danish screening system. RESULTS We hereby present evidence for DR-screening regarding (1) classification of DR, (2) examination techniques, (3) screening intervals and (4) automated screening. It is our recommendation that the International Clinical Retinopathy Disease Severity Scale should be used to classify DR. As a minimum, mydriatic two-field disc- and macular-centred images are required. In the case of suspected clinically significant diabetic macular oedema, supplementary optical coherence tomography can increase the diagnostic accuracy. There is solid evidence to support a flexible, individualized screening regimen. In particular, it is possible to prolong screening intervals to 24-48 months for patients with no or mild nonproliferative diabetic retinopathy (NPDR), but it is also possible to use extended intervals of 12-24 months for patients with moderate NPDR given that these are well-regulated regarding glycaemic control (HbA1c ≤ 53 mmol/mol) and blood pressure (≤130/80 mmHg). Automated screening of DR is encouraging but is not ready for implementation at present. CONCLUSION Danish evidenced-based guidelines for screening of DR support high-quality imaging and allow flexible, individualized screening intervals with a potential for extension to patients with low risk of DR progression.
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Affiliation(s)
- Jakob Grauslund
- Danish Ophthalmological Society; Copenhagen Denmark
- Department of Ophthalmology; Odense University Hospital; Odense Denmark
- Department of Clinical Research; University of Southern Denmark; Odense Denmark
| | - Nis Andersen
- Danish Ophthalmological Society; Copenhagen Denmark
- Organization of Danish Ophthalmologists; Copenhagen Denmark
| | - Jens Andresen
- Danish Ophthalmological Society; Copenhagen Denmark
- Organization of Danish Ophthalmologists; Copenhagen Denmark
| | - Per Flesner
- Danish Ophthalmological Society; Copenhagen Denmark
- Organization of Danish Ophthalmologists; Copenhagen Denmark
| | - Per Haamann
- Danish Ophthalmological Society; Copenhagen Denmark
- Organization of Danish Ophthalmologists; Copenhagen Denmark
| | - Steffen Heegaard
- Danish Ophthalmological Society; Copenhagen Denmark
- Department of Ophthalmology; Rigshospitalet-Glostrup; Copenhagen Denmark
| | - Michael Larsen
- Danish Ophthalmological Society; Copenhagen Denmark
- Department of Ophthalmology; Rigshospitalet-Glostrup; Copenhagen Denmark
| | - Caroline Schmidt Laugesen
- Danish Ophthalmological Society; Copenhagen Denmark
- Department of Ophthalmology; Zealand University Hospital Roskilde; Roskilde Denmark
| | - Katja Schielke
- Danish Ophthalmological Society; Copenhagen Denmark
- Department of Ophthalmology; Aalborg University Hospital; Aalborg Denmark
| | - Jesper Skov
- Danish Ophthalmological Society; Copenhagen Denmark
- Organization of Danish Ophthalmologists; Copenhagen Denmark
| | - Toke Bek
- Danish Ophthalmological Society; Copenhagen Denmark
- Department of Ophthalmology; Aarhus University Hospital; Aarhus Denmark
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Lawrenson JG, Graham‐Rowe E, Lorencatto F, Burr J, Bunce C, Francis JJ, Aluko P, Rice S, Vale L, Peto T, Presseau J, Ivers N, Grimshaw JM. Interventions to increase attendance for diabetic retinopathy screening. Cochrane Database Syst Rev 2018; 1:CD012054. [PMID: 29333660 PMCID: PMC6491139 DOI: 10.1002/14651858.cd012054.pub2] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Despite evidence supporting the effectiveness of diabetic retinopathy screening (DRS) in reducing the risk of sight loss, attendance for screening is consistently below recommended levels. OBJECTIVES The primary objective of the review was to assess the effectiveness of quality improvement (QI) interventions that seek to increase attendance for DRS in people with type 1 and type 2 diabetes.Secondary objectives were:To use validated taxonomies of QI intervention strategies and behaviour change techniques (BCTs) to code the description of interventions in the included studies and determine whether interventions that include particular QI strategies or component BCTs are more effective in increasing screening attendance;To explore heterogeneity in effect size within and between studies to identify potential explanatory factors for variability in effect size;To explore differential effects in subgroups to provide information on how equity of screening attendance could be improved;To critically appraise and summarise current evidence on the resource use, costs and cost effectiveness. SEARCH METHODS We searched the Cochrane Library, MEDLINE, Embase, PsycINFO, Web of Science, ProQuest Family Health, OpenGrey, the ISRCTN, ClinicalTrials.gov, and the WHO ICTRP to identify randomised controlled trials (RCTs) that were designed to improve attendance for DRS or were evaluating general quality improvement (QI) strategies for diabetes care and reported the effect of the intervention on DRS attendance. We searched the resources on 13 February 2017. We did not use any date or language restrictions in the searches. SELECTION CRITERIA We included RCTs that compared any QI intervention to usual care or a more intensive (stepped) intervention versus a less intensive intervention. DATA COLLECTION AND ANALYSIS We coded the QI strategy using a modification of the taxonomy developed by Cochrane Effective Practice and Organisation of Care (EPOC) and BCTs using the BCT Taxonomy version 1 (BCTTv1). We used Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital (PROGRESS) elements to describe the characteristics of participants in the included studies that could have an impact on equity of access to health services.Two review authors independently extracted data. One review author entered the data into Review Manager 5 and a second review author checked them. Two review authors independently assessed risks of bias in the included studies and extracted data. We rated certainty of evidence using GRADE. MAIN RESULTS We included 66 RCTs conducted predominantly (62%) in the USA. Overall we judged the trials to be at low or unclear risk of bias. QI strategies were multifaceted and targeted patients, healthcare professionals or healthcare systems. Fifty-six studies (329,164 participants) compared intervention versus usual care (median duration of follow-up 12 months). Overall, DRS attendance increased by 12% (risk difference (RD) 0.12, 95% confidence interval (CI) 0.10 to 0.14; low-certainty evidence) compared with usual care, with substantial heterogeneity in effect size. Both DRS-targeted (RD 0.17, 95% CI 0.11 to 0.22) and general QI interventions (RD 0.12, 95% CI 0.09 to 0.15) were effective, particularly where baseline DRS attendance was low. All BCT combinations were associated with significant improvements, particularly in those with poor attendance. We found higher effect estimates in subgroup analyses for the BCTs 'goal setting (outcome)' (RD 0.26, 95% CI 0.16 to 0.36) and 'feedback on outcomes of behaviour' (RD 0.22, 95% CI 0.15 to 0.29) in interventions targeting patients, and 'restructuring the social environment' (RD 0.19, 95% CI 0.12 to 0.26) and 'credible source' (RD 0.16, 95% CI 0.08 to 0.24) in interventions targeting healthcare professionals.Ten studies (23,715 participants) compared a more intensive (stepped) intervention versus a less intensive intervention. In these studies DRS attendance increased by 5% (RD 0.05, 95% CI 0.02 to 0.09; moderate-certainty evidence).Fourteen studies reporting any QI intervention compared to usual care included economic outcomes. However, only five of these were full economic evaluations. Overall, we found that there is insufficient evidence to draw robust conclusions about the relative cost effectiveness of the interventions compared to each other or against usual care.With the exception of gender and ethnicity, the characteristics of participants were poorly described in terms of PROGRESS elements. Seventeen studies (25.8%) were conducted in disadvantaged populations. No studies were carried out in low- or middle-income countries. AUTHORS' CONCLUSIONS The results of this review provide evidence that QI interventions targeting patients, healthcare professionals or the healthcare system are associated with meaningful improvements in DRS attendance compared to usual care. There was no statistically significant difference between interventions specifically aimed at DRS and those which were part of a general QI strategy for improving diabetes care. This is a significant finding, due to the additional benefits of general QI interventions in terms of improving glycaemic control, vascular risk management and screening for other microvascular complications. It is likely that further (but smaller) improvements in DRS attendance can also be achieved by increasing the intensity of a particular QI component or adding further components.
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Affiliation(s)
- John G Lawrenson
- City University of LondonCentre for Applied Vision Research, School of Health SciencesNorthampton SquareLondonUKEC1V 0HB
| | - Ella Graham‐Rowe
- City University LondonSchool of Health Sciences, Centre for Health Services ResearchNorthampton SquareLondonUKEC1V 0HB
| | - Fabiana Lorencatto
- City University LondonSchool of Health Sciences, Centre for Health Services ResearchNorthampton SquareLondonUKEC1V 0HB
| | - Jennifer Burr
- University of St AndrewsSchool of Medicine, Medical and Biological Sciences BuildingFifeUKKY16 9TF
| | - Catey Bunce
- Kings College LondonDepartment of Primary Care & Public Health Sciences4th Floor, Addison HouseGuy's CampusLondonUKSE1 1UL
| | - Jillian J Francis
- City University LondonSchool of Health Sciences, Centre for Health Services ResearchNorthampton SquareLondonUKEC1V 0HB
| | - Patricia Aluko
- Newcastle UniversityNational Institute for Health Research (NIHR) Innovation ObservatoryTimes Central offices, 4th Floor, GallowgateNewcastle upon TyneUKNE1 4BF
| | - Stephen Rice
- Newcastle UniversityInstitute of Health & SocietyNewcastle upon TyneUKNE2 4AX
| | - Luke Vale
- Newcastle UniversityInstitute of Health & SocietyNewcastle upon TyneUKNE2 4AX
| | - Tunde Peto
- Queen's University BelfastCentre for Public HealthBelfastUKBT12 6BA
| | - Justin Presseau
- Ottawa Hospital Research InstituteClinical Epidemiology Program501 Smyth RoadOttawaOntarioCanadaK1H 8L6
| | - Noah Ivers
- Women's College HospitalDepartment of Family and Community Medicine76 Grenville StreetTorontoONCanadaM5S 1B2
| | - Jeremy M Grimshaw
- Ottawa Hospital Research InstituteClinical Epidemiology Program501 Smyth RoadOttawaOntarioCanadaK1H 8L6
- University of OttawaDepartment of MedicineOttawaONCanada
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Abstract
PURPOSE OF REVIEW The purpose of this study is to review the evidence that lower risk groups who could safely be screened less frequently for sight-threatening diabetic retinopathy (DR) than annually. RECENT FINDINGS Data have demonstrated that people with no DR in either eye are at a low risk of progression to sight-threatening DR over a 2-year period (event rate 4.8 per 1000 person years), irrespective of whether the screening method is one-field non-mydriatic or two-field mydriatic digital photography. Low risk has been defined as no retinopathy on two consecutive screening episodes or no retinopathy on one screening episode combined with risk factor data. The risk of an extension to 2 years is less than 5 per 1000 person years in a population with a national screening programme, and the general standard of diabetes care is relatively good, whether low risk is defined as no retinopathy on two consecutive screening episodes or no retinopathy on one screening episode combined with other risk factor data. The definition used in different populations is likely to depend on the availability of data.
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Affiliation(s)
- Peter H Scanlon
- Gloucestershire Retinal Research Group, Gloucestershire Hospitals NHS Foundation Trust, Office above Oakley Ward, Cheltenham General Hospital, Sandford Road, Cheltenham, GL53 7AN, UK.
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK.
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Cheung CYL, Sabanayagam C, Law AKP, Kumari N, Ting DSW, Tan G, Mitchell P, Cheng CY, Wong TY. Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy. Diabetologia 2017. [PMID: 28623387 DOI: 10.1007/s00125-017-4333-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
AIMS/HYPOTHESIS We aimed to examine prospectively the association between a range of retinal vascular geometric variables measured from retinal photographs and the 6 year incidence and progression of diabetic retinopathy. METHODS We conducted a prospective, population-based cohort study of Asian Malay individuals aged 40-80 years at baseline (n = 3280) who returned for a 6 year follow-up. Retinal vascular geometric variables (tortuosity, branching, fractal dimension, calibre) were measured from baseline retinal photographs using a computer-assisted program (Singapore I Vessel Assessment). Diabetic retinopathy was graded from baseline and follow-up photographs using the modified Airlie House classification system. Incidence of diabetic retinopathy was defined as a severity of ≥15 at follow-up among those without diabetic retinopathy at baseline. Incidence of referable diabetic retinopathy was defined as moderate or severe non-proliferative diabetic retinopathy, proliferative diabetic retinopathy or diabetic macular oedema at follow-up in participants who had had no or mild non-proliferative diabetic retinopathy at baseline. Progression of diabetic retinopathy was defined as an increase in severity of ≥2 steps at follow-up. Log-binomial models with an expectation-maximisation algorithm were used to estimate RR adjusting for age, sex, diabetes duration, HbA1c level, BP, BMI, estimated GFR and total and HDL-cholesterol at baseline. RESULTS A total of 427 individuals with diabetes participated in the baseline and 6 year follow-up examinations. Of these, 19.2%, 7.57% and 19.2% developed incidence of diabetic retinopathy, incidence of referable diabetic retinopathy and diabetic retinopathy progression, respectively. After multivariate adjustment, greater arteriolar simple tortuosity (mean RR [95% CI], 1.34 [1.04, 1.74]), larger venular branching angle (RR 1.26 [1.00, 1.59]) and larger venular branching coefficient (RR 1.26 [1.03, 1.56]) were associated with incidence of diabetic retinopathy. Greater arteriolar simple tortuosity (RR 1.82 [1.32, 2.52]), larger venular branching coefficient (RR 1.46 [1.03, 2.07]), higher arteriolar fractal dimension (RR 1.59 [1.08, 2.36]) and larger arteriolar calibre (RR 1.83 [1.15, 2.90]) were associated with incidence of referable diabetic retinopathy. Greater arteriolar simple tortuosity (RR 1.34 [1.12, 1.61]) was associated with diabetic retinopathy progression. Addition of retinal vascular variables improved discrimination (C-statistic 0.796 vs 0.733, p = 0.031) and overall reclassification (net reclassification improvement 18.8%, p = 0.025) of any diabetic retinopathy risk beyond established risk factors. CONCLUSIONS/INTERPRETATION Retinal vascular geometry measured from fundus photographs predicted the incidence and progression of diabetic retinopathy in adults with diabetes, beyond established risk factors.
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Affiliation(s)
- Carol Yim-Lui Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, 11 Third Hospital Avenue, 168751, Singapore, Republic of Singapore
- Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong, Special Administrative Region, People's Republic of China
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, 11 Third Hospital Avenue, 168751, Singapore, Republic of Singapore
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Republic of Singapore
| | - Antony Kwan-Pui Law
- Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong, Special Administrative Region, People's Republic of China
| | - Neelam Kumari
- Singapore Eye Research Institute, Singapore National Eye Centre, 11 Third Hospital Avenue, 168751, Singapore, Republic of Singapore
| | - Daniel Shu-Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, 11 Third Hospital Avenue, 168751, Singapore, Republic of Singapore
| | - Gavin Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, 11 Third Hospital Avenue, 168751, Singapore, Republic of Singapore
| | - Paul Mitchell
- Centre for Vision Research, University of Sydney, Sydney, NSW, Australia
| | - Ching Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 11 Third Hospital Avenue, 168751, Singapore, Republic of Singapore
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Republic of Singapore
- Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, Singapore, Republic of Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, 11 Third Hospital Avenue, 168751, Singapore, Republic of Singapore.
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Republic of Singapore.
- Ophthalmology and Visual Sciences Academic Clinical Programme, Duke-NUS Medical School, National University of Singapore, Singapore, Republic of Singapore.
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Abstract
The aim of the English NHS Diabetic Eye Screening Programme is to reduce the risk of sight loss amongst people with diabetes by the prompt identification and effective treatment if necessary of sight-threatening diabetic retinopathy, at the appropriate stage during the disease process. In order to achieve the delivery of evidence-based, population-based screening programmes, it was recognised that certain key components were required. It is necessary to identify the eligible population in order to deliver the programme to the maximum number of people with diabetes. The programme is delivered and supported by suitably trained, competent, and qualified, clinical and non-clinical staff who participate in recognised ongoing Continuous Professional Development and Quality Assurance schemes. There is an appropriate referral route for those with screen-positive disease for ophthalmology treatment and for assessment of the retinal status in those with poor-quality images. Appropriate assessment of control of their diabetes is also important in those who are screen positive. Audit and internal and external quality assurance schemes are embedded in the service. In England, two-field mydriatic digital photographic screening is offered annually to all people with diabetes aged 12 years and over. The programme commenced in 2003 and reached population coverage across the whole of England by 2008. Increasing uptake has been achieved and the current annual uptake of the programme in 2015-16 is 82.8% when 2.59 million people with diabetes were offered screening and 2.14 million were screened. The benefit of the programme is that, in England, diabetic retinopathy/maculopathy is no longer the leading cause of certifiable blindness in the working age group.
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Affiliation(s)
- Peter H Scanlon
- The English NHS Diabetic Eye Screening Programme, Gloucestershire Diabetic Retinopathy Research Group, Office above Oakley Ward, Cheltenham General Hospital, Sandford Road, Cheltenham, GL53 7AN, UK.
- Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, UK.
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Buzzetti R, Prudente S, Copetti M, Dauriz M, Zampetti S, Garofolo M, Penno G, Trischitta V. Clinical worthlessness of genetic prediction of common forms of diabetes mellitus and related chronic complications: A position statement of the Italian Society of Diabetology. Nutr Metab Cardiovasc Dis 2017; 27:99-114. [PMID: 28063875 DOI: 10.1016/j.numecd.2016.08.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 08/01/2016] [Accepted: 08/13/2016] [Indexed: 02/08/2023]
Abstract
AIM We are currently facing several attempts aimed at marketing genetic data for predicting multifactorial diseases, among which diabetes mellitus is one of the more prevalent. The present document primarily aims at providing to practicing physicians a summary of available data regarding the role of genetic information in predicting diabetes and its chronic complications. DATA SYNTHESIS Firstly, general information about characteristics and performance of risk prediction tools will be presented in order to help clinicians to get acquainted with basic methodological information related to the subject at issue. Then, as far as type 1 diabetes is concerned, available data indicate that genetic information and counseling may be useful only in families with many affected individuals. However, since no disease prevention is possible, the utility of predicting this form of diabetes is at question. In the case of type 2 diabetes, available data really question the utility of adding genetic information on top of well performing, easy available and inexpensive non-genetic markers. Finally, the possibility of using the few available genetic data on diabetic complications for improving our ability to predict them will also be presented and discussed. For cardiovascular complication, the addition of genetic information to models based on clinical features does not translate in a substantial improvement in risk discrimination. For all other diabetic complications genetic information are currently very poor and cannot, therefore, be used for improving risk stratification. CONCLUSIONS In all, nowadays the use of genetic testing for predicting diabetes and its chronic complications is definitively of little value in clinical practice.
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Affiliation(s)
- R Buzzetti
- Department of Experimental Medicine, "Sapienza" University of Rome, Rome, Italy; UOC Diabetology, Polo Pontino, "Sapienza" University of Rome, Rome, Italy
| | - S Prudente
- Mendel Laboratory, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - M Copetti
- Unit of Biostatistics, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - M Dauriz
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Verona School of Medicine and Hospital Trust of Verona, Verona, Italy
| | - S Zampetti
- Department of Experimental Medicine, "Sapienza" University of Rome, Rome, Italy; UOC Diabetology, Polo Pontino, "Sapienza" University of Rome, Rome, Italy
| | - M Garofolo
- Section of Diabetes and Metabolic Disease, Department of Clinical and Experimental Medicine, University of Pisa and Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - G Penno
- Section of Diabetes and Metabolic Disease, Department of Clinical and Experimental Medicine, University of Pisa and Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - V Trischitta
- Department of Experimental Medicine, "Sapienza" University of Rome, Rome, Italy; Mendel Laboratory, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy; Research Unit of Diabetes and Endocrine Diseases, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
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Liew G, Wong VW, Ho IV. Mini Review: Changes in the Incidence of and Progression to Proliferative and Sight-Threatening Diabetic Retinopathy Over the Last 30 Years. Ophthalmic Epidemiol 2017; 24:73-80. [DOI: 10.1080/09286586.2016.1259638] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Gerald Liew
- Centre for Vision Research, Westmead Millennium Institute of Medical Research, University of Sydney, Sydney, Australia
| | - Vincent W. Wong
- Liverpool Diabetes Collaborative Research Unit, Ingham Institute of Applied Science, Sydney, Australia
| | - I-Van Ho
- Save Sight Institute, University of Sydney, Sydney, Australia
- Australian School of Advanced Medicine, Macquarie University, Sydney, Australia
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Vujosevic S, Midena E. Diabetic Retinopathy in Italy: Epidemiology Data and Telemedicine Screening Programs. J Diabetes Res 2016; 2016:3627465. [PMID: 27990441 PMCID: PMC5136623 DOI: 10.1155/2016/3627465] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 10/26/2016] [Accepted: 10/30/2016] [Indexed: 11/18/2022] Open
Abstract
In Italy, the number of people living with diabetes is about 3.5 million (5.5% of the population), with an increase by about 60% in the last 20 years and with 1 person out of 3 older than 65 years. The Italian Health Service system estimates that 10 billion euros is spent annually on caring for patients with diabetes, a figure that increases yearly. No national data on prevalence and incidence of legal blindness in patients with diabetes and no national registry of patients with diabetic retinopathy (DR) are currently available. However, the available epidemiological data (in several locations throughout the country) are consistent with those reported in other European countries. The use of telemedicine for the screening of DR in Italy is confined to geographically limited locations. The available data in the literature on implementation and use of telematic screening proved to be successful from patient, caregiver, and authorities point of view. This review addresses the available epidemiological data on DR and telematic screening realities in Italy and thus may help in establishing a national screening program.
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Affiliation(s)
- Stela Vujosevic
- Department of Ophthalmology, University of Padova, Padova, Italy
| | - Edoardo Midena
- Department of Ophthalmology, University of Padova, Padova, Italy
- Fondazione G. B. Bietti, IRCCS, Roma, Italy
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Eszes DJ, Szabó DJ, Russell G, Kirby P, Paulik E, Nagymajtényi L, Facskó A, Moe MC, Petrovski BÉ. Diabetic Retinopathy Screening Using Telemedicine Tools: Pilot Study in Hungary. J Diabetes Res 2016; 2016:4529824. [PMID: 28078306 PMCID: PMC5204085 DOI: 10.1155/2016/4529824] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 10/27/2016] [Accepted: 11/03/2016] [Indexed: 11/17/2022] Open
Abstract
Introduction. Diabetic retinopathy (DR) is a sight-threatening complication of diabetes. Telemedicine tools can prevent blindness. We aimed to investigate the patients' satisfaction when using such tools (fundus camera examination) and the effect of demographic and socioeconomic factors on participation in screening. Methods. Pilot study involving fundus camera screening and self-administered questionnaire on participants' experience during fundus examination (comfort, reliability, and future interest in participation), as well as demographic and socioeconomic factors was performed on 89 patients with known diabetes in Csongrád County, a southeastern region of Hungary. Results. Thirty percent of the patients had never participated in any ophthalmological screening, while 25.7% had DR of some grade based upon a standard fundus camera examination and UK-based DR grading protocol (Spectra™ software). Large majority of the patients were satisfied with the screening and found it reliable and acceptable to undertake examination under pupil dilation; 67.3% were willing to undergo nonmydriatic fundus camera examination again. There was a statistically significant relationship between economic activity, education and marital status, and future interest in participation. Discussion. Participants found digital retinal screening to be reliable and satisfactory. Telemedicine can be a strong tool, supporting eye care professionals and allowing for faster and more comfortable DR screening.
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Affiliation(s)
- Dóra J. Eszes
- Department of Public Health, University of Szeged, Szeged, Hungary
| | - Dóra J. Szabó
- Department of Ophthalmology, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary
| | - Greg Russell
- Health Intelligence, Clinical Development, Chesire, UK
| | - Phil Kirby
- Health Intelligence, Clinical Development, Chesire, UK
| | - Edit Paulik
- Department of Public Health, University of Szeged, Szeged, Hungary
| | | | - Andrea Facskó
- Department of Ophthalmology, Albert Szent-Györgyi Clinical Center, University of Szeged, Szeged, Hungary
| | - Morten C. Moe
- Centre of Eye Research, Department of Ophthalmology, Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Beáta É. Petrovski
- Department of Public Health, University of Szeged, Szeged, Hungary
- Health Services Research Centre, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Campus Ahus, Oslo, Norway
- *Beáta É. Petrovski:
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