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Chen D, Geevarghese A, Lee S, Plovnick C, Elgin C, Zhou R, Oermann E, Aphinyonaphongs Y, Al-Aswad LA. Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review. OPHTHALMOLOGY SCIENCE 2024; 4:100471. [PMID: 38591048 PMCID: PMC11000111 DOI: 10.1016/j.xops.2024.100471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/18/2023] [Accepted: 01/12/2024] [Indexed: 04/10/2024]
Abstract
Topic This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification. Clinical Relevance Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting. This review highlights the need for transparency to facilitate the critical appraisal of models prior to clinical implementation, to minimize bias and inappropriate use. Transparent reporting can improve effective and equitable use in clinical settings. Methods Eligible articles (as of January 2022) from PubMed, Embase, Web of Science, and CINAHL were independently screened by 2 reviewers. All observational and clinical trial studies evaluating the performance of an AI model for disease classification of ophthalmic conditions were included. Studies were evaluated for reporting of parameters derived from reporting guidelines (CONSORT-AI, MI-CLAIM) and our previously published editorial on model cards. The reporting of these factors, which included basic model and dataset details (source, demographics), and prospective validation outcomes, were summarized. Results Thirty-seven prospective validation studies were included in the scoping review. Eleven additional associated training and/or retrospective validation studies were included if this information could not be determined from the primary articles. These 37 studies validated 27 unique AI models; multiple studies evaluated the same algorithms (EyeArt, IDx-DR, and Medios AI). Details of model development were variably reported; 18 of 27 models described training dataset annotation and 10 of 27 studies reported training data distribution. Demographic information of training data was rarely reported; 7 of the 27 unique models reported age and gender and only 2 reported race and/or ethnicity. At the level of prospective clinical validation, age and gender of populations was more consistently reported (29 and 28 of 37 studies, respectively), but only 9 studies reported race and/or ethnicity data. Scope of use was difficult to discern for the majority of models. Fifteen studies did not state or imply primary users. Conclusion Our scoping review demonstrates variable reporting of information related to both model development and validation. The intention of our study was not to assess the quality of the factors we examined, but to characterize what information is, and is not, regularly reported. Our results suggest the need for greater transparency in the reporting of information necessary to determine the appropriateness and fairness of these tools prior to clinical use. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Dinah Chen
- Department of Ophthalmology, NYU Langone Health, New York, New York
| | | | - Samuel Lee
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
| | | | - Cansu Elgin
- Department of Ophthalmology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Raymond Zhou
- Department of Neurosurgery, Vanderbilt School of Medicine, Nashville, Tennessee
| | - Eric Oermann
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
- Department of Neurosurgery, NYU Langone Health, New York, New York
| | - Yindalon Aphinyonaphongs
- Department of Medicine, NYU Langone Health, New York, New York
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
| | - Lama A. Al-Aswad
- Department of Ophthalmology, NYU Langone Health, New York, New York
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
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Dimitri P, Savage MO. Artificial intelligence in paediatric endocrinology: conflict or cooperation. J Pediatr Endocrinol Metab 2024; 37:209-221. [PMID: 38183676 DOI: 10.1515/jpem-2023-0554] [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: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/08/2024]
Abstract
Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.
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Affiliation(s)
- Paul Dimitri
- Department of Paediatric Endocrinology, Sheffield Children's NHS Foundation Trust, Sheffield, UK
| | - Martin O Savage
- Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine & Dentistry, Queen Mary University of London, London, UK
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Shou BL, Venkatesh K, Chen C, Ghidey R, Lee JH, Wang J, Channa R, Wolf RM, Abramoff MD, Liu TYA. Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Healthcare System: Maximizing Workflow Efficiency Through Predictive Dilation. J Diabetes Sci Technol 2024; 18:302-308. [PMID: 37798955 DOI: 10.1177/19322968231201654] [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] [Indexed: 10/07/2023]
Abstract
OBJECTIVE In the pivotal clinical trial that led to Food and Drug Administration De Novo "approval" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction. METHODS Retrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR). P < .05 was considered statistically significant. RESULTS Of 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40, P = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99, P = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77, P < .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation. CONCLUSIONS We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori. This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.
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Affiliation(s)
- Benjamin L Shou
- School of Medicine, The Johns Hopkins University, Baltimore, MD, USA
| | - Kesavan Venkatesh
- Whiting School of Engineering, The Johns Hopkins University, Baltimore, MD, USA
| | - Chang Chen
- Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA
| | - Ronel Ghidey
- Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA
| | - Jae Hyoung Lee
- Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA
| | - Jiangxia Wang
- Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, MD, USA
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Risa M Wolf
- Department of Pediatrics, Division of Pediatric Endocrinology, The Johns Hopkins University, Baltimore, MD, USA
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA
| | - T Y Alvin Liu
- Wilmer Eye Institute, The Johns Hopkins University, Baltimore, MD, USA
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Wolf RM, Channa R, Liu TYA, Zehra A, Bromberger L, Patel D, Ananthakrishnan A, Brown EA, Prichett L, Lehmann HP, Abramoff MD. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial. Nat Commun 2024; 15:421. [PMID: 38212308 PMCID: PMC10784572 DOI: 10.1038/s41467-023-44676-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: 06/16/2023] [Accepted: 12/21/2023] [Indexed: 01/13/2024] Open
Abstract
Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.
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Affiliation(s)
- Risa M Wolf
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - T Y Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Anum Zehra
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Lee Bromberger
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dhruva Patel
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Elizabeth A Brown
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Laura Prichett
- Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core, Baltimore, MD, USA
| | - Harold P Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, USA
- Digital Diagnostics Inc, Coralville, IA, USA
- Iowa City VA Medical Center, Iowa City, IA, USA
- Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA
- Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, USA
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Piotr R, Robert R, Marek N, Michał I. Artificial intelligence enhanced ophthalmological screening in children: insights from a cohort study in Lubelskie Voivodeship. Sci Rep 2024; 14:254. [PMID: 38168543 PMCID: PMC10761970 DOI: 10.1038/s41598-023-50665-5] [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: 09/26/2023] [Accepted: 12/22/2023] [Indexed: 01/05/2024] Open
Abstract
This study aims to investigate the prevalence of visual impairments, such as myopia, hyperopia, and astigmatism, among school-age children (7-9 years) in Lubelskie Voivodeship (Republic of Poland) and apply artificial intelligence (AI) in the detection of severe ocular diseases. A total of 1049 participants (1.7% of the total child population in the region) were examined through a combination of standardized visual acuity tests, autorefraction, and assessment of fundus images by a convolutional neural network (CNN) model. The results from this artificial intelligence (AI) model were juxtaposed with assessments conducted by two experienced ophthalmologists to gauge the model's accuracy. The results demonstrated myopia, hyperopia, and astigmatism prevalences of 3.7%, 16.9%, and 7.8%, respectively, with myopia showing a significant age-related increase and hyperopia decreasing with age. The AI model performance was evaluated using the Dice coefficient, reaching 93.3%, indicating that the CNN model was highly accurate. The study underscores the utility of AI in the early detection and diagnosis of severe ocular diseases, providing a foundation for future research to improve paediatric ophthalmic screening and treatment outcomes.
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Affiliation(s)
- Regulski Piotr
- Laboratory of Digital Imaging and Virtual Reality, Department of Dental and Maxillofacial Radiology, Medical University of Warsaw, Binieckiego 6 St., 02-097, Warsaw, Poland.
| | - Rejdak Robert
- Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, Lublin, Poland
| | | | - Iwański Michał
- Laboratory of Digital Imaging and Virtual Reality, Department of Dental and Maxillofacial Radiology, Medical University of Warsaw, Binieckiego 6 St., 02-097, Warsaw, Poland
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Dow ER, Khan NC, Chen KM, Mishra K, Perera C, Narala R, Basina M, Dang J, Kim M, Levine M, Phadke A, Tan M, Weng K, Do DV, Moshfeghi DM, Mahajan VB, Mruthyunjaya P, Leng T, Myung D. AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy. OPHTHALMOLOGY SCIENCE 2023; 3:100330. [PMID: 37449051 PMCID: PMC10336195 DOI: 10.1016/j.xops.2023.100330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 07/18/2023]
Abstract
Objective Detection of diabetic retinopathy (DR) outside of specialized eye care settings is an important means of access to vision-preserving health maintenance. Remote interpretation of fundus photographs acquired in a primary care or other nonophthalmic setting in a store-and-forward manner is a predominant paradigm of teleophthalmology screening programs. Artificial intelligence (AI)-based image interpretation offers an alternative means of DR detection. IDx-DR (Digital Diagnostics Inc) is a Food and Drug Administration-authorized autonomous testing device for DR. We evaluated the diagnostic performance of IDx-DR compared with human-based teleophthalmology over 2 and a half years. Additionally, we evaluated an AI-human hybrid workflow that combines AI-system evaluation with human expert-based assessment for referable cases. Design Prospective cohort study and retrospective analysis. Participants Diabetic patients ≥ 18 years old without a prior DR diagnosis or DR examination in the past year presenting for routine DR screening in a primary care clinic. Methods Macula-centered and optic nerve-centered fundus photographs were evaluated by an AI algorithm followed by consensus-based overreading by retina specialists at the Stanford Ophthalmic Reading Center. Detection of more-than-mild diabetic retinopathy (MTMDR) was compared with in-person examination by a retina specialist. Main Outcome Measures Sensitivity, specificity, accuracy, positive predictive value, and gradability achieved by the AI algorithm and retina specialists. Results The AI algorithm had higher sensitivity (95.5% sensitivity; 95% confidence interval [CI], 86.7%-100%) but lower specificity (60.3% specificity; 95% CI, 47.7%-72.9%) for detection of MTMDR compared with remote image interpretation by retina specialists (69.5% sensitivity; 95% CI, 50.7%-88.3%; 96.9% specificity; 95% CI, 93.5%-100%). Gradability of encounters was also lower for the AI algorithm (62.5%) compared with retina specialists (93.1%). A 2-step AI-human hybrid workflow in which the AI algorithm initially rendered an assessment followed by overread by a retina specialist of MTMDR-positive encounters resulted in a sensitivity of 95.5% (95% CI, 86.7%-100%) and a specificity of 98.2% (95% CI, 94.6%-100%). Similarly, a 2-step overread by retina specialists of AI-ungradable encounters improved gradability from 63.5% to 95.6% of encounters. Conclusions Implementation of an AI-human hybrid teleophthalmology workflow may both decrease reliance on human specialist effort and improve diagnostic accuracy. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Eliot R. Dow
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
- Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Nergis C. Khan
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Karen M. Chen
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Kapil Mishra
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Chandrashan Perera
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Ramsudha Narala
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Marina Basina
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Jimmy Dang
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Michael Kim
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Marcie Levine
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Anuradha Phadke
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Marilyn Tan
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Kirsti Weng
- Stanford Healthcare, Stanford University, Palo Alto, California
| | - Diana V. Do
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Darius M. Moshfeghi
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Vinit B. Mahajan
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
- Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Prithvi Mruthyunjaya
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - Theodore Leng
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
| | - David Myung
- Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California
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Winkelman J, Nguyen D, vanSonnenberg E, Kirk A, Lieberman S. Artificial Intelligence (AI) in pediatric endocrinology. J Pediatr Endocrinol Metab 2023; 36:903-908. [PMID: 37589444 DOI: 10.1515/jpem-2023-0287] [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: 06/18/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
Artificial Intelligence (AI) is integrating itself throughout the medical community. AI's ability to analyze complex patterns and interpret large amounts of data will have considerable impact on all areas of medicine, including pediatric endocrinology. In this paper, we review and update the current studies of AI in pediatric endocrinology. Specific topics that are addressed include: diabetes management, bone growth, metabolism, obesity, and puberty. Becoming knowledgeable and comfortable with AI will assist pediatric endocrinologists, the goal of the paper.
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Affiliation(s)
| | - Diep Nguyen
- University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Eric vanSonnenberg
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- From the Departments of Radiology, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Alison Kirk
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Pediatrics, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Steven Lieberman
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Internal Medicine (Division of Endocrinology), University of Arizona College of Medicine Phoenix, Phoenix, USA
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Curran K, Whitestone N, Zabeen B, Ahmed M, Husain L, Alauddin M, Hossain MA, Patnaik JL, Lanoutee G, Cherwek DH, Congdon N, Peto T, Jaccard N. CHILDSTAR: CHIldren Living With Diabetes See and Thrive with AI Review. Clin Med Insights Endocrinol Diabetes 2023; 16:11795514231203867. [PMID: 37822362 PMCID: PMC10563496 DOI: 10.1177/11795514231203867] [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: 02/22/2023] [Accepted: 08/23/2023] [Indexed: 10/13/2023] Open
Abstract
Background Artificial intelligence (AI) appears capable of detecting diabetic retinopathy (DR) with a high degree of accuracy in adults; however, there are few studies in children and young adults. Methods Children and young adults (3-26 years) with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) were screened at the Dhaka BIRDEM-2 hospital, Bangladesh. All gradable fundus images were uploaded to Cybersight AI for interpretation. Two main outcomes were considered at a patient level: 1) Any DR, defined as mild non-proliferative diabetic retinopathy (NPDR or more severe; and 2) Referable DR, defined as moderate NPDR or more severe. Diagnostic test performance comparing Orbis International's Cybersight AI with the reference standard, a fully qualified optometrist certified in DR grading, was assessed using the Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC-ROC), area under the precision-recall curve (AUC-PR), sensitivity, specificity, positive and negative predictive values. Results Among 1274 participants (53.1% female, mean age 16.7 years), 19.4% (n = 247) had any DR according to AI. For referable DR, 2.35% (n = 30) were detected by AI. The sensitivity and specificity of AI for any DR were 75.5% (CI 69.7-81.3%) and 91.8% (CI 90.2-93.5%) respectively, and for referable DR, these values were 84.2% (CI 67.8-100%) and 98.9% (CI 98.3%-99.5%). The MCC, AUC-ROC and the AUC-PR for referable DR were 63.4, 91.2 and 76.2% respectively. AI was most successful in accurately classifying younger children with shorter duration of diabetes. Conclusions Cybersight AI accurately detected any DR and referable DR among children and young adults, despite its algorithms having been trained on adults. The observed high specificity is particularly important to avoid over-referral in low-resource settings. AI may be an effective tool to reduce demands on scarce physician resources for the care of children with diabetes in low-resource settings.
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Affiliation(s)
- Katie Curran
- Centre for Public Health, Queens University Belfast, Belfast, UK
| | | | - Bedowra Zabeen
- Department of Paediatrics, Life for a Child & Changing Diabetes in Children Programme, Bangladesh Institute of Research & Rehabilitation in Diabetes, Endocrine & Metabolic Disorders (BIRDEM), Diabetic Association of Bangladesh, Dhaka, Bangladesh
| | | | | | | | | | - Jennifer L Patnaik
- Orbis International, New York, NY, USA
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | | | | | - Nathan Congdon
- Centre for Public Health, Queens University Belfast, Belfast, UK
- Orbis International, New York, NY, USA
- Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Tunde Peto
- Centre for Public Health, Queens University Belfast, Belfast, UK
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Wolf RM, Channa R, Lehmann HP, Abramoff MD, Liu TA. Clinical Implementation of Autonomous Artificial Intelligence Systems for Diabetic Eye Exams: Considerations for Success. Clin Diabetes 2023; 42:142-149. [PMID: 38230333 PMCID: PMC10788651 DOI: 10.2337/cd23-0019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Affiliation(s)
- Risa M. Wolf
- Department of Pediatric Endocrinology and Diabetes, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI
| | - Harold P. Lehmann
- Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD
| | - Michael D. Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA
- Digital Diagnostics, Coralville, IA
| | - T.Y. Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins University School of Medicine, Baltimore, MD
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Abràmoff MD, Tarver ME, Loyo-Berrios N, Trujillo S, Char D, Obermeyer Z, Eydelman MB, Maisel WH. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med 2023; 6:170. [PMID: 37700029 PMCID: PMC10497548 DOI: 10.1038/s41746-023-00913-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 08/21/2023] [Indexed: 09/14/2023] Open
Abstract
Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these "Considerations" is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all.
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Affiliation(s)
- Michael D Abràmoff
- Departments of Ophthalmology and Visual Sciences, and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
| | - Michelle E Tarver
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - Nilsa Loyo-Berrios
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - Danton Char
- Center for Biomedical Ethics, Stanford University School of Medicine, San Francisco, CA, USA
- Department of Anesthesiology, Stanford University School of Medicine, Division of Pediatric Cardiac Anesthesia, San Francisco, CA, USA
| | - Ziad Obermeyer
- School of Public Health, University of California, Berkeley, CA, USA
| | - Malvina B Eydelman
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | - William H Maisel
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
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11
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Urbano F, Farella I, Brunetti G, Faienza MF. Pediatric Type 1 Diabetes: Mechanisms and Impact of Technologies on Comorbidities and Life Expectancy. Int J Mol Sci 2023; 24:11980. [PMID: 37569354 PMCID: PMC10418611 DOI: 10.3390/ijms241511980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
Type 1 diabetes (T1D) is one of the most common chronic diseases in childhood, with a progressively increasing incidence. T1D management requires lifelong insulin treatment and ongoing health care support. The main goal of treatment is to maintain blood glucose levels as close to the physiological range as possible, particularly to avoid blood glucose fluctuations, which have been linked to morbidity and mortality in patients with T1D. Indeed, the guidelines of the International Society for Pediatric and Adolescent Diabetes (ISPAD) recommend a glycated hemoglobin (HbA1c) level < 53 mmol/mol (<7.0%) for young people with T1D to avoid comorbidities. Moreover, diabetic disease strongly influences the quality of life of young patients who must undergo continuous monitoring of glycemic values and the administration of subcutaneous insulin. In recent decades, the development of automated insulin delivery (AID) systems improved the metabolic control and the quality of life of T1D patients. Continuous subcutaneous insulin infusion (CSII) combined with continuous glucose monitoring (CGM) devices connected to smartphones represent a good therapeutic option, especially in young children. In this literature review, we revised the mechanisms of the currently available technologies for T1D in pediatric age and explored their effect on short- and long-term diabetes-related comorbidities, quality of life, and life expectation.
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Affiliation(s)
- Flavia Urbano
- Giovanni XXIII Pediatric Hospital, 70126 Bari, Italy;
| | - Ilaria Farella
- Clinica Medica “A. Murri”, University of Bari “Aldo Moro”, 70124 Bari, Italy;
| | - Giacomina Brunetti
- Department of Biosciences, Biotechnologies, and Environment, University of Bari “Aldo Moro”, 70125 Bari, Italy
| | - Maria Felicia Faienza
- Department of Precision and Regenerative Medicine and Ionian Area, University of Bari “Aldo Moro”, 70124 Bari, Italy;
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12
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Xiao H, Tang J, Zhang F, Liu L, Zhou J, Chen M, Li M, Wu X, Nie Y, Duan J. Global trends and performances in diabetic retinopathy studies: A bibliometric analysis. Front Public Health 2023; 11:1128008. [PMID: 37124794 PMCID: PMC10136779 DOI: 10.3389/fpubh.2023.1128008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/09/2023] [Indexed: 05/02/2023] Open
Abstract
Objective The objective of this study is to conduct a comprehensive bibliometric analysis to identify and evaluate global trends in diabetic retinopathy (DR) research and visualize the focus and frontiers of this field. Methods Diabetic retinopathy-related publications from the establishment of the Web of Science (WOS) through 1 November 2022 were retrieved for qualitative and quantitative analyses. This study analyzed annual publication counts, prolific countries, institutions, journals, and the top 10 most cited literature. The findings were presented through descriptive statistics. VOSviewer 1.6.17 was used to exhibit keywords with high frequency and national cooperation networks, while CiteSpace 5.5.R2 displayed the timeline and burst keywords for each term. Results A total of 10,709 references were analyzed, and the number of publications continuously increased over the investigated period. America had the highest h-index and citation frequency, contributing to the most influence. China was the most prolific country, producing 3,168 articles. The University of London had the highest productivity. The top three productive journals were from America, and Investigative Ophthalmology Visual Science had the highest number of publications. The article from Gulshan et al. (2016; co-citation counts, 2,897) served as the representative and symbolic reference. The main research topics in this area were incidence, pathogenesis, treatment, and artificial intelligence (AI). Deep learning, models, biomarkers, and optical coherence tomography angiography (OCTA) of DR were frontier hotspots. Conclusion Bibliometric analysis in this study provided valuable insights into global trends in DR research frontiers. Four key study directions and three research frontiers were extracted from the extensive DR-related literature. As the incidence of DR continues to increase, DR prevention and treatment have become a pressing public health concern and a significant area of research interest. In addition, the development of AI technologies and telemedicine has emerged as promising research frontiers for balancing the number of doctors and patients.
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Affiliation(s)
- Huan Xiao
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jinfan Tang
- School of Acupuncture-Moxibustion and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Feng Zhang
- School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing, China
| | - Luping Liu
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jing Zhou
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Meiqi Chen
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Mengyue Li
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xiaoxiao Wu
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yingying Nie
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Junguo Duan
- School of Ophthalmology, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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13
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Channa R, Wolf RM, Abràmoff MD, Lehmann HP. Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model. NPJ Digit Med 2023; 6:53. [PMID: 36973403 PMCID: PMC10042864 DOI: 10.1038/s41746-023-00785-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 02/24/2023] [Indexed: 03/29/2023] Open
Abstract
The effectiveness of using artificial intelligence (AI) systems to perform diabetic retinal exams ('screening') on preventing vision loss is not known. We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care autonomous AI-based screening with in-office clinical exam by an eye care provider (ECP), on preventing vision loss among patients with diabetes. The estimated incidence of vision loss at 5 years was 1535 per 100,000 in the AI-screened group compared to 1625 per 100,000 in the ECP group, leading to a modelled risk difference of 90 per 100,000. The base-case CAREVL model estimated that an autonomous AI-based screening strategy would result in 27,000 fewer Americans with vision loss at 5 years compared with ECP. Vision loss at 5 years remained lower in the AI-screened group compared to the ECP group, in a wide range of parameters including optimistic estimates biased toward ECP. Real-world modifiable factors associated with processes of care could further increase its effectiveness. Of these factors, increased adherence with treatment was estimated to have the greatest impact.
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Affiliation(s)
- Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
| | - Risa M Wolf
- Department of Pediatrics, Division of Endocrinology, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Michael D Abràmoff
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA
| | - Harold P Lehmann
- Department of Medicine, Section on Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
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Gurevich E, El Hassan B, El Morr C. Equity within AI systems: What can health leaders expect? Healthc Manage Forum 2023; 36:119-124. [PMID: 36226507 PMCID: PMC9976641 DOI: 10.1177/08404704221125368] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Artificial Intelligence (AI) for health has a great potential; it has already proven to be successful in enhancing patient outcomes, facilitating professional work and benefiting administration. However, AI presents challenges related to health equity defined as an opportunity for people to reach their fullest health potential. This article discusses the opportunities and challenges that AI presents in health and examines ways in which inequities related to AI can be mitigated.
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Affiliation(s)
| | | | - Christo El Morr
- York University, Toronto, Ontario, Canada.,Christo El Morr, York University, Toronto, Ontario, Canada. E-mail:
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15
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Cioana M, Deng J, Nadarajah A, Hou M, Qiu Y, Chen SSJ, Rivas A, Toor PP, Banfield L, Thabane L, Chaudhary V, Samaan MC. Global Prevalence of Diabetic Retinopathy in Pediatric Type 2 Diabetes: A Systematic Review and Meta-analysis. JAMA Netw Open 2023; 6:e231887. [PMID: 36930156 PMCID: PMC10024209 DOI: 10.1001/jamanetworkopen.2023.1887] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
IMPORTANCE Type 2 diabetes (T2D) is increasing globally. Diabetic retinopathy (DR) is a leading cause of blindness in adults with T2D; however, the global burden of DR in pediatric T2D is unknown. This knowledge can inform retinopathy screening and treatments to preserve vision in this population. OBJECTIVE To estimate the global prevalence of DR in pediatric T2D. DATA SOURCES MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, the Web of Science, and the gray literature (ie, literature containing information that is not available through traditional publishing and distribution channels) were searched for relevant records from the date of database inception to April 4, 2021, with updated searches conducted on May 17, 2022. Searches were limited to human studies. No language restrictions were applied. Search terms included diabetic retinopathy; diabetes mellitus, type 2; prevalence studies; and child, adolescent, teenage, youth, and pediatric. STUDY SELECTION Three teams, each with 2 reviewers, independently screened for observational studies with 10 or more participants that reported the prevalence of DR. Among 1989 screened articles, 27 studies met the inclusion criteria for the pooled analysis. DATA EXTRACTION AND SYNTHESIS This systematic review and meta-analysis followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines for systematic reviews and meta-analyses. Two independent reviewers performed the risk of bias and level of evidence analyses. The results were pooled using a random-effects model, and heterogeneity was reported using χ2 and I2 statistics. MAIN OUTCOMES AND MEASURES The main outcome was the estimated pooled global prevalence of DR in pediatric T2D. Other outcomes included DR severity and current DR assessment methods. The association of diabetes duration, sex, race, age, and obesity with DR prevalence was also assessed. RESULTS Among the 27 studies included in the pooled analysis (5924 unique patients; age range at T2D diagnosis, 6.5-21.0 years), the global prevalence of DR in pediatric T2D was 6.99% (95% CI, 3.75%-11.00%; I2 = 95%; 615 patients). Fundoscopy was less sensitive than 7-field stereoscopic fundus photography in detecting retinopathy (0.47% [95% CI, 0%-3.30%; I2 = 0%] vs 13.55% [95% CI, 5.43%-24.29%; I2 = 92%]). The prevalence of DR increased over time and was 1.11% (95% CI, 0.04%-3.06%; I2 = 5%) at less than 2.5 years after T2D diagnosis, 9.04% (95% CI, 2.24%-19.55%; I2 = 88%) at 2.5 to 5.0 years after T2D diagnosis, and 28.14% (95% CI, 12.84%-46.45%; I2 = 96%) at more than 5 years after T2D diagnosis. The prevalence of DR increased with age, and no differences were noted based on sex, race, or obesity. Heterogeneity was high among studies. CONCLUSIONS AND RELEVANCE In this study, DR prevalence in pediatric T2D increased significantly at more than 5 years after diagnosis. These findings suggest that retinal microvasculature is an early target of T2D in children and adolescents, and annual screening with fundus photography beginning at diagnosis offers the best assessment method for early detection of DR in pediatric patients.
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Affiliation(s)
- Milena Cioana
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
| | - Jiawen Deng
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
| | - Ajantha Nadarajah
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
| | - Maggie Hou
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
| | - Yuan Qiu
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
- Michael G. De Groote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sondra Song Jie Chen
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
| | - Angelica Rivas
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
- Michael G. De Groote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Parm Pal Toor
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
| | - Laura Banfield
- Health Sciences Library, McMaster University, Hamilton, Ontario, Canada
| | - Lehana Thabane
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
- Department of Anesthesia, McMaster University, Hamilton, Ontario, Canada
- Centre for Evaluation of Medicines, St Joseph’s Health Care, Hamilton, Ontario, Canada
- Biostatistics Unit, St Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Varun Chaudhary
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
- Division of Ophthalmology, Department of Surgery, McMaster University, Hamilton, Ontario, Canada
| | - M. Constantine Samaan
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
- Division of Pediatric Endocrinology, McMaster Children's Hospital, Hamilton, Ontario, Canada
- Michael G. De Groote School of Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
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Vujosevic S, Limoli C, Luzi L, Nucci P. Digital innovations for retinal care in diabetic retinopathy. Acta Diabetol 2022; 59:1521-1530. [PMID: 35962258 PMCID: PMC9374293 DOI: 10.1007/s00592-022-01941-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022]
Abstract
AIM The purpose of this review is to examine the applications of novel digital technology domains for the screening and management of patients with diabetic retinopathy (DR). METHODS A PubMed engine search was performed, using the terms "Telemedicine", "Digital health", "Telehealth", "Telescreening", "Artificial intelligence", "Deep learning", "Smartphone", "Triage", "Screening", "Home-based", "Monitoring", "Ophthalmology", "Diabetes", "Diabetic Retinopathy", "Retinal imaging". Full-text English language studies from January 1, 2010, to February 1, 2022, and reference lists were considered for the conceptual framework of this review. RESULTS Diabetes mellitus and its eye complications, including DR, are particularly well suited to digital technologies, providing an ideal model for telehealth initiatives and real-world applications. The current development in the adoption of telemedicine, artificial intelligence and remote monitoring as an alternative to or in addition to traditional forms of care will be discussed. CONCLUSIONS Advances in digital health have created an ecosystem ripe for telemedicine in the field of DR to thrive. Stakeholders and policymakers should adopt a participatory approach to ensure sustained implementation of these technologies after the COVID-19 pandemic. This article belongs to the Topical Collection "Diabetic Eye Disease", managed by Giuseppe Querques.
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Affiliation(s)
- Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy.
| | - Celeste Limoli
- Eye Clinic, IRCCS MultiMedica, Via San Vittore 12, 20123, Milan, Italy
- University of Milan, Milan, Italy
| | - Livio Luzi
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
- Department of Endocrinology, Nutrition and Metabolic Diseases, IRCCS MultiMedica, Milan, Italy
| | - Paolo Nucci
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
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17
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Bratina N, Auzanneau M, Birkebæk N, de Beaufort C, Cherubini V, Craig ME, Dabelea D, Dovc K, Hofer SE, Holl RW, Jensen ET, Mul D, Nagl K, Robinson H, Schierloh U, Svensson J, Tiberi V, Veeze HJ, Warner JT, Donaghue KC. Differences in retinopathy prevalence and associated risk factors across 11 countries in three continents: A cross-sectional study of 156,090 children and adolescents with type 1 diabetes. Pediatr Diabetes 2022; 23:1656-1664. [PMID: 36097824 PMCID: PMC9771999 DOI: 10.1111/pedi.13416] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/30/2022] [Accepted: 09/07/2022] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE To examine the prevalence, time trends, and risk factors of diabetic retinopathy (DR) among youth with type 1 diabetes (T1D) from 11 countries (Australia, Austria, Denmark, England, Germany, Italy, Luxemburg, Netherlands, Slovenia, United States, and Wales). SUBJECTS AND METHODS Data on individuals aged 10-21 years with T1D for >1 year during the period 2000-2020 were analyzed. We used a cross-sectional design using the most recent year of visit to investigate the time trend. For datasets with longitudinal data, we aggregated the variables per participant and observational year, using data of the most recent year to take the longest observation period into account. DR screening was performed through quality assured national screening programs. Multiple logistic regression models adjusted for the year of the eye examination, age, gender, minority status, and duration of T1D were used to evaluate clinical characteristics and the risk of DR. RESULTS Data from 156,090 individuals (47.1% female, median age 15.7 years, median duration of diabetes 5.2 years) were included. Overall, the unadjusted prevalence of any DR was 5.8%, varying from 0.0% (0/276) to 16.2% between countries. The probability of DR increased with longer disease duration (aORper-1-year-increase = 1.04, 95% CI: 1.03-1.04, p < 0.0001), and decreased over time (aORper-1-year-increase = 0.99, 95% CI: 0.98-1.00, p = 0.0093). Evaluating possible modifiable risk factors in the exploratory analysis, the probability of DR increased with higher HbA1c (aORper-1-mmol/mol-increase-in-HbA1c = 1.03, 95% CI: 1.03-1.03, p < 0.0001) and was higher among individuals with hypertension (aOR = 1.24, 95% CI: 1.11-1.38, p < 0.0001) and smokers (aOR = 1.30, 95% CI: 1.17-1.44, p < 0.0001). CONCLUSIONS The prevalence of DR in this large cohort of youth with T1D varied among countries, increased with diabetes duration, decreased over time, and was associated with higher HbA1c, hypertension, and smoking.
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Affiliation(s)
- Natasa Bratina
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's HospitalUMC LjubljanaLjubljanaSlovenia
- Faculty of MedicineUniversity of LjubljanaLjubljanaSlovenia
| | - Marie Auzanneau
- Institute of Epidemiology and Medical BiometryZIBMT, University of UlmUlmGermany
- German Center for Diabetes Research (DZD)Munich‐NeuherbergGermany
| | - Niels Birkebæk
- Department of Pediatric and Adolescents and Steno Diabetes Center, AarhusAarhus University HospitalAarhusDenmark
| | - Carine de Beaufort
- Department of Pediatric Diabetes and EndocrinologyCentre HospitalierLuxembourgLuxembourg
- Department of Pediatric EndocrinologyUZ‐VUBBrusselsBelgium
| | | | - Maria E. Craig
- The Children's Hospital at WestmeadSydneyAustralia
- University of SydneySydneyAustralia
- University of New South WalesSydneyAustralia
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public HealthUniversity of ColoradoAuroraColoradoUSA
| | - Klemen Dovc
- Department of Pediatric Endocrinology, Diabetes and Metabolic Diseases, University Children's HospitalUMC LjubljanaLjubljanaSlovenia
- Faculty of MedicineUniversity of LjubljanaLjubljanaSlovenia
| | - Sabine E. Hofer
- Department of Pediatrics 1Medical University of InnsbruckInnsbruckAustria
| | - Reinhard W. Holl
- Institute of Epidemiology and Medical BiometryZIBMT, University of UlmUlmGermany
- German Center for Diabetes Research (DZD)Munich‐NeuherbergGermany
| | - Elizabeth T. Jensen
- Department of Epidemiology and PreventionWake Forest School of MedicineWinston‐SalemNorth CarolinaUSA
| | - Dick Mul
- DiabeterCenter for Pediatric and Adult Diabetes Care and ResearchRotterdamThe Netherlands
| | - Katrin Nagl
- Department of Pediatrics and Adolescent MedicineMedical University of ViennaViennaAustria
| | - Holly Robinson
- Department of Science and ResearchRoyal College of Paediatrics and Child HealthLondonUK
| | - Ulrike Schierloh
- Department of Pediatric Diabetes and EndocrinologyCentre HospitalierLuxembourgLuxembourg
| | - Jannet Svensson
- Department of Pediatric and AdolescentsCopenhagen University HospitalHerlev & GentofteDenmark
- Department of Clinical MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Valentina Tiberi
- Department of Women's and Children's HealthSalesi HospitalAnconaItaly
| | - Henk J. Veeze
- DiabeterCenter for Pediatric and Adult Diabetes Care and ResearchRotterdamThe Netherlands
| | - Justin T. Warner
- Department of Paediatric EndocrinologyChildren's Hospital for WalesCardiffUK
| | - Kim C. Donaghue
- The Children's Hospital at WestmeadSydneyAustralia
- University of SydneySydneyAustralia
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18
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Social Determinants of Health and Impact on Screening, Prevalence, and Management of Diabetic Retinopathy in Adults: A Narrative Review. J Clin Med 2022; 11:jcm11237120. [PMID: 36498694 PMCID: PMC9739502 DOI: 10.3390/jcm11237120] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 12/05/2022] Open
Abstract
Diabetic retinal disease (DRD) is the leading cause of blindness among working-aged individuals with diabetes. In the United States, underserved and minority populations are disproportionately affected by diabetic retinopathy and other diabetes-related health outcomes. In this narrative review, we describe racial disparities in the prevalence and screening of diabetic retinopathy, as well as the wide-range of disparities associated with social determinants of health (SDOH), which include socioeconomic status, geography, health-care access, and education.
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Zhou P, Eltemsah L, Bahrainian M, Prichett L, Liu TYA, Wolf RM, Channa R. Assessment of Trained Image Grader Performance in Screening for Retinopathy Among Youth With Diabetes. J Diabetes Sci Technol 2022; 16:1580-1581. [PMID: 36047654 PMCID: PMC9631538 DOI: 10.1177/19322968221120240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Philip Zhou
- Department of Ophthalmology, Baylor College
of Medicine, Houston, TX, USA
| | - Loaah Eltemsah
- Department of Pediatric Endocrinology, Johns
Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mozhdeh Bahrainian
- Department of Ophthalmology and Visual
Sciences, University of Wisconsin–Madison, Madison, WI, USA
| | - Laura Prichett
- Department of Pediatrics, Johns Hopkins
University School of Medicine, Baltimore, MD, USA
| | - T. Y. Alvin Liu
- Wilmer Eye Institute, Johns Hopkins
University School of Medicine, Baltimore, MD, USA
| | - Risa M. Wolf
- Department of Pediatric Endocrinology, Johns
Hopkins University School of Medicine, Baltimore, MD, USA
| | - Roomasa Channa
- Department of Ophthalmology and Visual
Sciences, University of Wisconsin–Madison, Madison, WI, USA
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20
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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: 136] [Impact Index Per Article: 68.0] [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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Abràmoff MD, Roehrenbeck C, Trujillo S, Goldstein J, Graves AS, Repka MX, Silva Iii EZ. A reimbursement framework for artificial intelligence in healthcare. NPJ Digit Med 2022; 5:72. [PMID: 35681002 PMCID: PMC9184542 DOI: 10.1038/s41746-022-00621-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/25/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Michael D Abràmoff
- Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA. .,AI Healthcare Coalition, Washington, DC, USA. .,Digital Diagnostics, Coralville, IA, USA.
| | - Cybil Roehrenbeck
- AI Healthcare Coalition, Washington, DC, USA.,Hogan Lovells LLP, Washington, DC, USA
| | | | | | | | - Michael X Repka
- Wilmer Eye Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Ezequiel Zeke Silva Iii
- South Texas Radiology, San Antonio, TX, USA.,University of Texas Health, Long School of Medicine, San Antonio, TX, USA
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22
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Zafar S, Mahjoub H, Mehta N, Domalpally A, Channa R. Artificial Intelligence Algorithms in Diabetic Retinopathy Screening. Curr Diab Rep 2022; 22:267-274. [PMID: 35438458 DOI: 10.1007/s11892-022-01467-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/07/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW In this review, we focus on artificial intelligence (AI) algorithms for diabetic retinopathy (DR) screening and risk stratification and factors to consider when implementing AI algorithms in the clinic. RECENT FINDINGS AI algorithms have been adopted, and have received regulatory approval, for automated detection of referable DR with clinically acceptable diagnostic performance. While these metrics are an important first step, performance metrics that go beyond measures of technical accuracy are needed to fully evaluate the impact of AI algorithm on patient outcomes. Recent advances in AI present an exciting opportunity to improve patient care. Using DR as an example, we have reviewed factors to consider in the implementation of AI algorithms in real-world clinical practice. These include real-world evaluation of safety, efficacy, and equity (bias); impact on patient outcomes; ethical, logistical, and regulatory factors.
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Affiliation(s)
- Sidra Zafar
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Heba Mahjoub
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Nitish Mehta
- Department of Ophthalmology, New York University School of Medicine, New York, NY, USA
| | - Amitha Domalpally
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA.
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23
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Potential reduction in healthcare carbon footprint by autonomous artificial intelligence. NPJ Digit Med 2022; 5:62. [PMID: 35551275 PMCID: PMC9098499 DOI: 10.1038/s41746-022-00605-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 04/15/2022] [Indexed: 11/09/2022] Open
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24
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Lin T, Gubitosi-Klug RA, Channa R, Wolf RM. Pediatric Diabetic Retinopathy: Updates in Prevalence, Risk Factors, Screening, and Management. Curr Diab Rep 2021; 21:56. [PMID: 34902076 DOI: 10.1007/s11892-021-01436-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/22/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE OF REVIEW Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus and a major cause of vision loss worldwide. The purpose of this review is to provide an update on the prevalence of diabetic retinopathy in youth, discuss risk factors, and review recent advances in diabetic retinopathy screening. RECENT FINDINGS While DR has long been considered a microvascular complication, recent data suggests that retinal neurodegeneration may precede the vascular changes associated with DR. The prevalence of DR has decreased in type 1 diabetes (T1D) patients following the results of the Diabetes Control and Complications Trial and implementation of intensive insulin therapy, with prevalence ranging from 14-20% before the year 2000 to 3.7-6% after 2000. In contrast, the prevalence of diabetic retinopathy in pediatric type 2 diabetes (T2D) is higher, ranging from 9.1-50%. Risk factors for diabetic retinopathy are well established and include glycemic control, diabetes duration, hypertension, and hyperlipidemia, whereas diabetes technology use including insulin pumps and continuous glucose monitors has been shown to have protective effects. Screening for DR is recommended for youth with T1D once they are aged ≥ 11 years or puberty has started and diabetes duration of 3-5 years. Pediatric T2D patients are advised to undergo screening at or soon after diagnosis, and annually thereafter, due to the insidious nature of T2D. Recent advances in DR screening methods including point of care and artificial intelligence technology have increased access to DR screening, while being cost-saving to patients and cost-effective to healthcare systems. While the prevalence of diabetic retinopathy in youth with T1D has been declining over the last few decades, there has been a significant increase in the prevalence of DR in youth with T2D. Improving access to diabetic retinopathy screening using novel screening methods may help improve detection and early treatment of diabetic retinopathy.
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Affiliation(s)
- Tyger Lin
- Department of Pediatrics, Division of Pediatric Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Rose A Gubitosi-Klug
- Department of Pediatrics, Division of Endocrinology, Case Western Reserve University School of Medicine and Rainbow Babies and Children's Hospital, Cleveland, OH, USA
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, WI, USA
| | - Risa M Wolf
- Department of Pediatrics, Division of Pediatric Endocrinology, Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA.
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25
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Ferm ML, DeSalvo DJ, Prichett LM, Sickler JK, Wolf RM, Channa R. Clinical and Demographic Factors Associated With Diabetic Retinopathy Among Young Patients With Diabetes. JAMA Netw Open 2021; 4:e2126126. [PMID: 34570208 PMCID: PMC8477260 DOI: 10.1001/jamanetworkopen.2021.26126] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
IMPORTANCE Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. As the incidence of both type 1 and type 2 diabetes among youths continues to increase around the world, understanding the factors associated with the development of DR in this age group is important. OBJECTIVE To identify factors associated with DR among children, adolescents, and young adults with type 1 or type 2 diabetes in the US. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study pooled data from 2 large academic pediatric centers in the US (Baylor College of Medicine/Texas Children's Hospital [BCM/TCH] Diabetes and Endocrine Care Center and Johns Hopkins University [JHU] Pediatric Diabetes Center) to form a diverse population for analysis. Data were collected prospectively at the JHU center (via point-of-care screening using fundus photography) from December 3, 2018, to November 1, 2019, and retrospectively at the BCM/TCH center (via electronic health records of patients who received point-of-care screening using retinal cameras between June 1, 2016, and May 31, 2019). A total of 1640 individuals aged 5 to 21 years with type 1 or type 2 diabetes (308 participants from the JHU center and 1332 participants from the BCM/TCH center) completed DR screening and had gradable images. MAIN OUTCOME AND MEASURES Prevalence of DR, as identified on fundus photography, and factors associated with DR. RESULTS Among 1640 participants (mean [SD] age, 15.7 [3.6] years; 867 female individuals [52.9%]), 1216 (74.1%) had type 1 diabetes, and 416 (25.4%) had type 2 diabetes. A total of 506 participants (30.9%) were Hispanic, 384 (23.4%) were non-Hispanic Black or African American, 647 (39.5%) were non-Hispanic White, and 103 (6.3%) were of other races or ethnicities (1 was American Indian or Alaska Native, 50 were Asian, 1 was Native Hawaiian or Pacific Islander, and 51 did not specify race or ethnicity, specified other race or ethnicity, or had unavailable data on race or ethnicity). Overall, 558 of 1216 patients (45.9%) with type 1 diabetes used an insulin pump, and 5 of 416 patients (1.2%) with type 2 diabetes used an insulin pump. Diabetic retinopathy was found in 57 of 1640 patients (3.5%). Patients with DR vs without DR had a greater duration of diabetes (mean [SD], 9.4 [4.4] years vs 6.6 [4.4] years; P < .001) and higher hemoglobin A1c (HbA1c) levels (mean [SD], 10.3% [2.4%] vs 9.2% [2.1%]; P < .001). Among those with type 1 diabetes, insulin pump use was associated with a lower likelihood of DR after adjusting for race and ethnicity, insurance status, diabetes duration, and HbA1c level (odds ratio [OR], 0.43; 95% CI, 0.20-0.93; P = .03). The likelihood of having DR was 2.1 times higher among Black or African American participants compared with White participants (OR, 2.12; 95% CI, 1.12-4.01; P = .02); this difference was no longer significant after adjusting for duration of diabetes, insurance status, insulin pump use (among patients with type 1 diabetes only), and mean HbA1c level (type 1 diabetes: OR, 1.79; 95% CI, 0.83-3.89; P = .14; type 2 diabetes: OR, 1.08; 95% CI, 0.30-3.85; P = .91). CONCLUSIONS AND RELEVANCE This study found that although the duration of diabetes and suboptimal glycemic control have long been associated with DR, insulin pump use (among those with type 1 diabetes) was independently associated with a lower likelihood of DR, which is likely owing to decreased glycemic variability and increased time in range (ie, the percentage of time blood glucose levels remain within the 70-180 mg/dL range). Black or African American race was found to be associated with DR in the univariable analysis but not in the multivariable analysis, which may represent disparities in access to diabetes technologies and care.
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Affiliation(s)
- Michael L. Ferm
- Baylor College of Medicine, Texas Children’s Hospital, Houston
| | - Daniel J. DeSalvo
- Pediatric Endocrinology and Metabolism, Baylor College of Medicine, Texas Children’s Hospital, Houston
| | - Laura M. Prichett
- Biostatistics, Epidemiology, and Data Management Core, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Risa M. Wolf
- Division of Endocrinology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison
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26
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Thomas CG, Channa R, Prichett L, Liu TYA, Abramoff MD, Wolf RM. Racial/Ethnic Disparities and Barriers to Diabetic Retinopathy Screening in Youths. JAMA Ophthalmol 2021; 139:791-795. [PMID: 34042939 DOI: 10.1001/jamaophthalmol.2021.1551] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Importance Diabetic retinopathy is a major complication of diabetes for which regular screening improves visual health outcomes, yet adherence to screening is suboptimal. Objective To assess disparities in diabetic eye examination completion rates and evaluate barriers in those not previously screened. Design, Setting, and Participants In this cohort study at a single academic center (Johns Hopkins Hospital pediatric diabetes center in Baltimore, Maryland) from December 2018 to November 2019, youths with type 1 or type 2 diabetes who met criteria for diabetic retinopathy screening and were enrolled in a prospective observational trial implementing point-of-care diabetic retinopathy screening were asked about prior diabetic retinopathy screening. Main Outcomes and Measures Demographic and clinical characteristics were compared between those who did and did not have a previous diabetic eye examination and stratified according to race/ethnicity, using t tests and χ2 tests. Multivariate logistic regression was used to analyze the association between race/ethnicity, screening, and other social determinants of health. A questionnaire assessing barriers to screening adherence was administered. Results Of 149 participants (76 male patients [51.0%]; mean [SD] age, 14.5 [2.3] years), 51 (34.2%) had not had a prior diabetic eye examination. These individuals were more likely than those who had prior diabetic eye examinations to be non-White youths (38 [75%] vs 31 [32%]; P < .001) and have type 2 diabetes (38 [75%] vs 10 [10%]; P < .001), Medicaid or public insurance (43 [84%] vs 31 [32%]; P < .001), lower household income (annual income ≤$25 000, 21 [41%] vs 9 [9%]; P < .001), and parents with education levels of high school or less (29 [67%] vs 22 [35%]; P < .001). The main barriers reported included not recalling being recommended to obtain a diabetic eye examination (19 [56%]), difficulty finding time for an additional appointment (10 [29%]), and transportation issues (7 [20%]). Minority youths were less likely to have a previous diabetic eye examination (non-White, 34 [46%] vs White, 64 [85%]; P < .001) and more likely to have diabetic retinopathy (11 [15%] v 2 [3%]; P = .008). Minority youths were less likely to get diabetic eye examinations even after adjusting for insurance, household income, and parental education level (odds ratio, 0.29 [95% CI, 0.10-0.79]; P = .02). Conclusions and Relevance In this cohort study, non-White youths were less likely to undergo diabetic eye examinations yet more likely to have diabetic retinopathy compared with White youths. Addressing barriers to diabetic retinopathy screening may improve access to diabetic eye examination and facilitate early detection.
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Affiliation(s)
- Chrystal G Thomas
- Division of Pediatric Endocrinology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Roomasa Channa
- Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison
| | - Laura Prichett
- Johns Hopkins School of Medicine Biostatistics, Epidemiology and Data Management (BEAD) Core, Baltimore, Maryland
| | - T Y Alvin Liu
- Wilmer Eye Institute at the Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Michael D Abramoff
- Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City.,Digital Diagnostics, Coralville, Iowa.,Iowa City VA Medical Center, Iowa City, Iowa
| | - Risa M Wolf
- Division of Pediatric Endocrinology, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, Maryland
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