Cost-effectiveness of Autonomous Point-of-Care Diabetic Retinopathy Screening for Pediatric Patients With Diabetes

Abstract

ouths and adults with diabetes are at risk for diabetic retinopathy (DR), which can lead to vision loss. 1 The prevalence of DR among youths with type 1 diabetes (T1D) and type 2 diabetes (T2D) ranges from 4% to 13%. [2][3][4] Despite recommendations from the American Diabetes Association and American Academy of Ophthalmology for yearly screening, adherence remains low. 5 Although the prevalence is low among youths, the risk of developing DR is high. Diabetic retinopathy is present in up to 50% of patients with T1D 28 years or more after diagnosis 6,7 and already present at the time of diagnosis in 12% to 19% of patients with T2D. 4 Digital teleophthalmology systems that use nonmydriatic cameras have been implemented to improve DR screen-ing rates 8,9 and are cost-effective. 10 Digital fundus photography can be efficiently and safely performed without pupil dilation, including in the pediatric setting. 3,[11][12][13][14] Recently, the US Food and Drug Administration (FDA) approved the first autonomous artificial intelligence (AI) diagnostic system to detect DR in adults. With this system, a minimally trained operator guided by an image-quality AI takes retinal images with a nonmydriatic fundus camera, and these images are subsequently assessed in real time at the point of care (POC) for the presence or absence of DR. 15 In 2020, this form of FDA-validated autonomous AI became part of the American Diabetes Association standard of care for DR screening. 16 IMPORTANCE Screening for diabetic retinopathy is recommended for children with type 1 diabetes (T1D) and type 2 diabetes (T2D), yet screening rates remain low. Point-of-care diabetic retinopathy screening using autonomous artificial intelligence (AI) has become available, providing immediate results in the clinic setting, but the cost-effectiveness of this strategy compared with standard examination is unknown.OBJECTIVE To assess the cost-effectiveness of detecting and treating diabetic retinopathy and its sequelae among children with T1D and T2D using AI diabetic retinopathy screening vs standard screening by an eye care professional (ECP). DESIGN, SETTING, AND PARTICIPANTSIn this economic evaluation, parameter estimates were obtained from the literature from 1994 to 2019 and assessed from March 2019 to January 2020. Parameters included out-of-pocket cost for autonomous AI screening, ophthalmology visits, and treating diabetic retinopathy; probability of undergoing standard retinal examination; relative odds of undergoing screening; and sensitivity, specificity, and diagnosability of the ECP screening examination and autonomous AI screening. MAIN OUTCOMES AND MEASURESCosts or savings to the patient based on mean patient payment for diabetic retinopathy screening examination and cost-effectiveness based on costs or savings associated with the number of true-positive results identified by diabetic retinopathy screening. RESULTSIn this study, the expected true-positive proportions for standard ophthalmologic screening by an ECP were 0.006 for T1D and 0.01 for...

Clinical and Demographic Factors Associated With Diabetic Retinopathy Among Young Patients With Diabetes

“…20 Although a dilated and comprehensive eye examination by an ophthalmologist remains the standard of care for retinal screening, fundus photography with or without artificial intelligence-based techniques for point-of-care detection of DR serves as an accurate screening tool for DR, is feasible for use among young patients with diabetes, increases adherence to recommended screening, and is cost-effective. [23][24][25][26][27][28] Our study sought to estimate the burden of DR among children, adolescents, and young adults in the US at the time of point-of-care DR screening and to identify factors associated with DR by combining data from 2 large pediatric hospitals that have robust DR screening programs. Combining data from these 2 sites allowed us to report findings from analyses that, to our knowledge, include one of the largest and most racially diverse groups of young patients with type 1 and type 2 diabetes in the US.…”

Systematic Review of Health Economic Evaluations Focused on Artificial Intelligence in Healthcare: The Tortoise and the Cheetah

“…The remaining 20 studies were read full text. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] The relevant data were extracted and the 2 reviewers independently further assessed the quality of the articles per the CHEERS and Philips checklists.…”

“…A general overview of the included studies, including the details of the reported AI applications, is provided in Table 2. [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] The majority of studies was published in 2019 or later. The studies were conducted in a range of medical specialties, yet ophthalmology was evidently the dominant field.…”

“…A total of 4 of 20 articles (20%) involved ophthalmology, all of which evaluated the same AI application for diabetic retinopathy screening. 20,21,28,29 The most common type of AI was automated medical image analysis models, as 9 studies (45%) evaluated this type of AI. [19][20][21][22][27][28][29][30]32 A total of 6 studies (30%) evaluated pattern recognition in clinical data for predictive modeling.…”

“…20,21,28,29 The most common type of AI was automated medical image analysis models, as 9 studies (45%) evaluated this type of AI. [19][20][21][22][27][28][29][30]32 A total of 6 studies (30%) evaluated pattern recognition in clinical data for predictive modeling. [24][25][26][36][37][38] Nevertheless, all phases of the care pathway from prevention to follow-up were supported by AI in at least 1 included study; screening and treatment monitoring (intervention) were the most prevalent phases of the care pathway in which AI was applied.…”