Geographic variation in diabetic retinopathy screening within the Veterans Health Administration.

AIMS: Diabetic retinopathy (DR) remains the leading cause of vision impairment among working-age adults in the United States. The Veterans Health Administration (VA) supplemented its DR screening efforts with teleretinal imaging in 2006. Despite its scale and longevity, no national data on the VA's screening program exists since 1998. Our objective was to determine the influence of geography on diabetic retinopathy screening adherence. METHODS: Setting: VA national electronic medical records. STUDY POPULATION: A national cohort of 940,654 veterans with diabetes (defined as two or more diabetes ICD-9 codes (250.xx)) without a history of DR. EXPOSURES: 125 VA Medical Center catchment areas, demographics, comorbidity burden, mean HbA1c levels, medication use and adherence, as well as utilization and access metrics. MAIN OUTCOME MEASURE: Screening for diabetic retinopathy within the VA medical system within a 2-year period. RESULTS: Within a 2-year time frame 74 % of veterans without a history of DR received retinal screenings within the VA system. After adjustment for age, gender, race-ethnic group, service-connected disability, marital status, and the van Walraven Elixhauser comorbidity score, the prevalence of DR screening varied by VA catchment area with values ranging from 27 % to 86 %. These differences persisted after further adjusting for mean HbA1c level, medication use and adherence as well as utilization and access metrics. CONCLUSIONS: The wide variability in DR screening across 125 VA catchment areas indicates the presence of unmeasured determinants of DR screening. These results are relevant to clinical decision making in DR screening resource allocation.

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study.

BACKGROUND: Photographs of the external eye were recently shown to reveal signs of diabetic retinal disease and elevated glycated haemoglobin. This study aimed to test the hypothesis that external eye photographs contain information about additional systemic medical conditions. METHODS: We developed a deep learning system (DLS) that takes external eye photographs as input and predicts systemic parameters, such as those related to the liver (albumin, aspartate aminotransferase [AST]); kidney (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [ACR]); bone or mineral (calcium); thyroid (thyroid stimulating hormone); and blood (haemoglobin, white blood cells [WBC], platelets). This DLS was trained using 123 130 images from 38 398 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA, USA. Evaluation focused on nine prespecified systemic parameters and leveraged three validation sets (A, B, C) spanning 25 510 patients with and without diabetes undergoing eye screening in three independent sites in Los Angeles county, CA, and the greater Atlanta area, GA, USA. We compared performance against baseline models incorporating available clinicodemographic variables (eg, age, sex, race and ethnicity, years with diabetes). FINDINGS: Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST >36·0 U/L, calcium <8·6 mg/dL, eGFR <60·0 mL/min/1·73 m2, haemoglobin <11·0 g/dL, platelets <150·0 × 103/µL, ACR ≥300 mg/g, and WBC <4·0 × 103/µL on validation set A (a population resembling the development datasets), with the area under the receiver operating characteristic curve (AUC) of the DLS exceeding that of the baseline by 5·3-19·9% (absolute differences in AUC). On validation sets B and C, with substantial patient population differences compared with the development datasets, the DLS outperformed the baseline for ACR ≥300·0 mg/g and haemoglobin <11·0 g/dL by 7·3-13·2%. INTERPRETATION: We found further evidence that external eye photographs contain biomarkers spanning multiple organ systems. Such biomarkers could enable accessible and non-invasive screening of disease. Further work is needed to understand the translational implications. FUNDING: Google.

Stakeholder perceptions affecting the implementation of teleophthalmology.

PURPOSE: Teleophthalmology has become the subject of heightened interest and scrutiny in the wake of the SARS-CoV-2 global pandemic. A streamlined implementation framework becomes increasingly important as demand grows. This study identified obstacles to teleophthalmology implementation through summative content analysis of key stakeholders' perceptions. DESIGN: Summative content analysis of transcribed interviews with key stakeholders (including patients, technicians, ophthalmic readers, staff, nurses, and administrators at two teleophthalmology clinic sites). METHODS: Keyword Were counted and compared to examine underlying meaning. Two analysts coded text independently using MAXQDA for summative qualitative content analysis to derive themes and hierarchical relationships as a basis for future refinement of TECS implementation. xMind ZEN was used to map conceptual relationships and overarching themes that emerged to identify perceived facilitators and barriers to implementation RESULTS: Analysis revealed two themes common to perceptions: (1) benefits of care, and (2) ease of implementation. Perceived benefits included efficiency, accessibility, and earlier intervention in disease course. The quality and quantity of training was heavily weighted in its influence on stakeholders' commitment to and confidence in the program, as were transparent organizational structure, clear bidirectional communication, and the availability of support staff. CONCLUSION: Using a determinant framework of implementation science, this report highlighted potential hindrances to teleophthalmology implementation and offered solutions in order to increase access to screening, improve the quality of care provided, and facilitate sustainability of the innovation.

Detection of signs of disease in external photographs of the eyes via deep learning.

Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.

Teleophthalmology and Inequities in Diabetic Eye Disease at Safety Net Hospitals.

Introduction: Teleophthalmology has emerged as a convenient and cost-effective intervention to increase access to screening for diabetic retinopathy (DR), a disease that disproportionately affects socially disadvantaged communities. However, a few studies have directly compared the detection of eye disease by teleophthalmology between socially and geographically diverse communities. This study compared the rates and severity of diabetic eye disease, as detected by teleophthalmology, between safety net and non-Safety Net Hospitals (non-SNHs). Methods: Retrospective chart review of patients screened for DR at county Safety Net Hospitals (SNHs) and non-SNHs in 150 cities and 30 states. The rates of DR, macular edema, suspected cataract, suspected glaucoma, and suspected age-related macular degeneration were compared. Relative risk and severity of disease in the county SNH population were calculated. Images were graded by the same group of IRIS readers, who used at least one image per eye with a 45° field centered between the optic disc and the macula. Participants with ungradable screening images were excluded. Results: Ninety-four thousand three hundred twenty-nine participants were screened for eye disease from September 1, 2016 to August 31, 2017. Among the screened participants (54% female; mean [SD] age, 58.7 [12.9] years), overall disease detection was 31% in the county SNH population and 23.6% in the non-SNH population. Compared with the non-SNH population, the county SNH population was twice as likely to screen positive for three or more concurrent eye conditions (1.2% vs. 0.7%) and had increased prevalence of DR (20.2% vs. 16.2%), macular edema (4.9% vs. 3.4%), suspected glaucoma (9.1% vs. 4.3%), suspected cataract (9.6% vs. 4.8%), and proliferative DR (2.1% vs. 1.0%). Conclusions: Increased diabetic eye disease prevalence and severity among people seen at SNHs highlights the need for continued resources to screen, treat, and manage disease. Teleophthalmology continues to be an important tool in efforts to mitigate health inequities and address barriers faced by underserved communities.

Multicenter, Head-to-Head, Real-World Validation Study of Seven Automated Artificial Intelligence Diabetic Retinopathy Screening Systems.

OBJECTIVE: With rising global prevalence of diabetic retinopathy (DR), automated DR screening is needed for primary care settings. Two automated artificial intelligence (AI)-based DR screening algorithms have U.S. Food and Drug Administration (FDA) approval. Several others are under consideration while in clinical use in other countries, but their real-world performance has not been evaluated systematically. We compared the performance of seven automated AI-based DR screening algorithms (including one FDA-approved algorithm) against human graders when analyzing real-world retinal imaging data. RESEARCH DESIGN AND METHODS: This was a multicenter, noninterventional device validation study evaluating a total of 311,604 retinal images from 23,724 veterans who presented for teleretinal DR screening at the Veterans Affairs (VA) Puget Sound Health Care System (HCS) or Atlanta VA HCS from 2006 to 2018. Five companies provided seven algorithms, including one with FDA approval, that independently analyzed all scans, regardless of image quality. The sensitivity/specificity of each algorithm when classifying images as referable DR or not were compared with original VA teleretinal grades and a regraded arbitrated data set. Value per encounter was estimated. RESULTS: Although high negative predictive values (82.72-93.69%) were observed, sensitivities varied widely (50.98-85.90%). Most algorithms performed no better than humans against the arbitrated data set, but two achieved higher sensitivities, and one yielded comparable sensitivity (80.47%, P = 0.441) and specificity (81.28%, P = 0.195). Notably, one had lower sensitivity (74.42%) for proliferative DR (P = 9.77 × 10-4) than the VA teleretinal graders. Value per encounter varied at $15.14-$18.06 for ophthalmologists and $7.74-$9.24 for optometrists. CONCLUSIONS: The DR screening algorithms showed significant performance differences. These results argue for rigorous testing of all such algorithms on real-world data before clinical implementation.

Novel Detection and Restorative Levodopa Treatment for Preclinical Diabetic Retinopathy.

Diabetic retinopathy (DR) is diagnosed clinically by directly viewing retinal vascular changes during ophthalmoscopy or through fundus photographs. However, electroretinography (ERG) studies in humans and rodents have revealed that retinal dysfunction is demonstrable prior to the development of visible vascular defects. Specifically, delays in dark-adapted ERG oscillatory potential (OP) implicit times in response to dim-flash stimuli (<-1.8 log cd · s/m2) occur prior to clinically recognized DR. Animal studies suggest that retinal dopamine deficiency underlies these early functional deficits. In this study, we randomized individuals with diabetes, without clinically detectable retinopathy, to treatment with either low- or high-dose Sinemet (levodopa plus carbidopa) for 2 weeks and compared their ERG findings with those of control subjects (no diabetes). We assessed dim-flash-stimulated OP delays using a novel handheld ERG system (RETeval) at baseline and 2 and 4 weeks. RETeval recordings identified significant OP implicit time delays in individuals with diabetes without retinopathy compared with age-matched control subjects (P < 0.001). After 2 weeks of Sinemet treatment, OP implicit times were restored to control values, and these improvements persisted even after a 2-week washout. We conclude that detection of dim-flash OP delays could provide early detection of DR and that Sinemet treatment may reverse retinal dysfunction.

Diagnostic Accuracy of Technology-based Eye Care Services: The Technology-based Eye Care Services Compare Trial Part I.

PURPOSE: Ophthalmologic telemedicine has the ability to provide eye care for patients remotely, and many countries have used screening tele-ophthalmology programs for several years. One such initiative at the Veterans Affairs (VA) Healthcare System is Technology-based Eye Care Services (TECS). The TECS services are located in primary care clinics and provide basic screening eye care, including vision, refraction, and retinal photography. Eye care providers ("readers") review the clinical data and recommend appropriate follow-up. One of the most common referrals from TECS has been for glaucoma, and this study was powered for glaucoma/glaucoma suspect detection. The current study was undertaken to identify aspects of the protocol that could be refined to enhance accuracy. DESIGN: Prospective comparison between the standard TECS protocol versus a face-to-face (FTF) examination on 256 patients, all of whom had no known history of significant ocular disease. PARTICIPANTS: Patients with no known ocular disease who were scheduled for an in-person eye appointment at the Atlanta VA. Patients underwent screening through the TECS protocol and received an FTF examination on the same day (gold standard). The TECS readers were masked to the results of the FTF examination. MAIN OUTCOME MEASURES: Percent agreement, kappa, sensitivity, and specificity were calculated for the TECS readers' interpretations versus the FTF examination. RESULTS: The TECS readers showed substantial agreement for cataract (κ ≥ 0.71) and diabetic retinopathy (κ ≥ 0.61) and moderate to substantial agreement for glaucoma/glaucoma suspect (κ ≥ 0.52) compared with an FTF examination. Age-related macular degeneration (AMD) showed moderate agreement (κ ≥ 0.34). Percent agreement with the TECS protocol was high (84.3%-98.4%) for each of the disease categories. Overall sensitivity and specificity were ≥75% and ≥55%, respectively, for any diagnosis resulting in referral. Inter-reader and intra-reader agreement was substantial for most diagnoses (κ > 0.61) with percent agreements ranging from 66% to 99%. CONCLUSIONS: Our results indicate that the standard TECS protocol is accurate when compared with an FTF examination for the detection of common eye diseases. The inclusion of additional testing such as OCT could further enhance diagnostic capability.

The Impact of OCT on Diagnostic Accuracy of the Technology-Based Eye Care Services Protocol: Part II of the Technology-Based Eye Care Services Compare Trial.

PURPOSE: Ophthalmologic telemedicine programs help to address the growing demand for eye care and lessen healthcare disparities for patients. One example is Technology-Based Eye Care Services (TECS), implemented in the Veteran Affairs Healthcare System in 2015. Accuracy and quality data for TECS both have been reported, and data suggest that although the TECS examination is comparable with an in-person examination, sensitivity for glaucoma and glaucoma suspect detection is less than that for other diseases, such as macular degeneration. Several articles suggest that OCT can improve disease detection for glaucoma. Therefore, this study was undertaken to test the impact of OCT on the accuracy of the TECS protocol. This article reports the data from part II of the TECS Compare trial; results from part I are discussed in a previous article. DESIGN: Prospective comparison between the TECS protocol with OCT versus a face-to-face (FTF) examination for 256 patients. PARTICIPANTS: An eligible patient was defined as a patient with no known ocular disease who desired a routine eye examination. METHODS: Patient underwent the TECS protocol workup and OCT nerve, OCT macula, and FTF examination on the same day. MAIN OUTCOME MEASURES: Percent agreement, κ values, sensitivity, and specificity were calculated for nonexpert readers after OCT interpretation of the TECS protocol using the FTF examination as the clinical gold standard. RESULTS: OCT did not improve the diagnostic accuracy of the TECS protocol when compared with an FTF examination. In most cases, OCT had no impact, and in the case of reader 2, OCT actually reduced the κ value from moderate agreement to agreement equal to chance while lowering the percent agreement by 10%. OCT also did not impact inter- or intrareader variability parameters. CONCLUSIONS: In this study, OCT did not seem to improve the accuracy of glaucoma or retinal disease detection when added to the standard TECS protocol. In one case, OCT worsened the agreement of the reader compared with the FTF. Further study is necessary to confirm these findings, and results may change if the readers are glaucoma or retina specialists instead of nonexpert OCT readers, comprehensive and anterior segment specialists.

Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.

PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referral decisions by glaucoma specialists (GSs) and the algorithm, and to compare the performance of the algorithm with eye care providers. DESIGN: Development and validation of an algorithm. PARTICIPANTS: Fundus images from screening programs, studies, and a glaucoma clinic. METHODS: A DL algorithm was trained using a retrospective dataset of 86 618 images, assessed for glaucomatous ONH features and referable GON (defined as ONH appearance worrisome enough to justify referral for comprehensive examination) by 43 graders. The algorithm was validated using 3 datasets: dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of GSs; dataset B (9642 images, 1 image/patient; 9.2% referable), images from a diabetic teleretinal screening program; and dataset C (346 images, 1 image/patient; 81.7% referable), images from a glaucoma clinic. MAIN OUTCOME MEASURES: The algorithm was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for referable GON and glaucomatous ONH features. RESULTS: The algorithm's AUC for referable GON was 0.945 (95% confidence interval [CI], 0.929-0.960) in dataset A, 0.855 (95% CI, 0.841-0.870) in dataset B, and 0.881 (95% CI, 0.838-0.918) in dataset C. Algorithm AUCs ranged between 0.661 and 0.973 for glaucomatous ONH features. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders (including 1 GS), while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels. CONCLUSIONS: A DL algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.

Diagnosis of retinal detachments by a tele-ophthalmology screening program.

In 2015, a tele-ophthalmology program was undertaken at the Atlanta Veterans Affairs Medical Center to provide screening eye care for veterans in their primary care clinics. Though this program was developed as a screening tool, the availability of these services in primary care clinics has enabled triage of certain acute eye complaints. These case reports describe two patients who were diagnosed with retinal detachments through this program, although their primary care providers had triaged them as requiring non-urgent referrals to the eye clinic. Although many patients are seen for acute ocular complaints in primary care clinics and emergency departments, providers in such settings may lack the ability to adequately examine eyes and thus triage ocular complaints. These cases demonstrate the ability of tele-ophthalmology to assist in diagnosing urgent ocular conditions in primary care clinics. Though tele-ophthalmology has been accepted in some parts of the world, in the United States of America it remains widely underutilized. These cases highlight the ability of tele-ophthalmology to close the gap in acute eye care coverage that exists in the USA, most prominently in rural regions.

Differences in Tertiary Glaucoma Care in the Veterans Affairs Health Care System.

Importance: Glaucoma is a common cause of visual impairment in the Veterans Affairs (VA) health care system, but to our knowledge, no data exist concerning tertiary glaucoma care (ie, laser and filtering surgery). Objective: To determine whether the rate of tertiary glaucoma care differs among veterans cared for through the 4 different eye care delivery models that are present in the VA: optometry-only clinics, ophthalmology-only clinics, clinics with optometry and ophthalmology functioning as a single integrated clinic with ophthalmology as the lead, and clinics with optometry and ophthalmology functioning as separate clinics. Design, Setting, and Participants: In this retrospective review of the Veterans Health Administration Support Service Center database, 490 926 veterans with a glaucoma-related diagnosis received care from 136 VA medical centers during fiscal year 2016. Demographic and baseline clinical factors, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, and Current Procedural Terminology codes, and the rates of glaucoma surgery procedures were extracted from the database. The organizational structure of each VA eye clinic was obtained. Univariate and multivariate regression analyses were performed for log percent for laser peripheral iridotomy (LPI), laser trabeculoplasty (LTP), and filtering surgery. Main Outcomes and Measures: Rates of LPI, LTP, and filtering surgery. Results: Of the 490 926 veterans with a glaucoma-related diagnosis, 465 842 (94.9%) were male, 309 677 (63.1%) were white, and 203 243 (41.4%) were aged 65 to 74 years. The rate of LPI was 0.30%, 0.28%, 0.67%, and 0.69% in optometry-only clinics, ophthalmology-only clinics, integrated clinics, and separated clinics, respectively (P < .001). The rate of LTP was 0.31%, 1.06%, 0.93%, and 0.92% in care delivery models that included optometry-only clinics, ophthalmology-only clinics, integrated clinics, and separated clinics, respectively (P < .001). The rate of filtering surgery was 0.32%, 0.51%, 0.69%, and 0.60% in optometry-only clinics, ophthalmology-only clinics, integrated clinics, and separated clinics, respectively (P < .001). Multivariate regression analyses showed that these differences remained significantly different even after adjusting for potential confounders. Conclusions and Relevance: Disparities exist in the use of tertiary glaucoma services within the VA, and different care delivery models may play a role. Outcomes of glaucoma care for the different models of eye care delivery were not analyzed in this study.

Remote eye care screening for rural veterans with Technology-based Eye Care Services: a quality improvement project.

CONTEXT: Veterans are at high risk for eye disease because of age and comorbid conditions. Access to eye care is challenging within the entire Veterans Hospital Administration's network of hospitals and clinics in the USA because it is the third busiest outpatient clinical service and growing at a rate of 9% per year. ISSUE: Rural and highly rural veterans face many more barriers to accessing eye care because of distance, cost to travel, and difficulty finding care in the community as many live in medically underserved areas. Also, rural veterans may be diagnosed in later stages of eye disease than their non-rural counterparts due to lack of access to specialty care. In March 2015, Technology-based Eye Care Services (TECS) was launched from the Atlanta Veterans Affairs (VA) as a quality improvement project to provide eye screening services for rural veterans. LESSONS LEARNED: By tracking multiple measures including demographic and access to care metrics, data shows that TECS significantly improved access to care, with 33% of veterans receiving same-day access and >98% of veterans receiving an appointment within 30 days of request. TECS also provided care to a significant percentage of homeless veterans, 10.6% of the patients screened. Finally, TECS reduced healthcare costs, saving the VA up to US$148 per visit and approximately US$52 per patient in round trip travel reimbursements when compared to completing a face-to-face exam at the medical center. Overall savings to the VA system in this early phase of TECS totaled US$288,400, about US$41,200 per month. Other healthcare facilities may be able to use a similar protocol to extend care to at-risk patients.

Early Experience with Technology-Based Eye Care Services (TECS): A Novel Ophthalmologic Telemedicine Initiative.

PURPOSE: The aging population is at risk of common eye diseases, and routine eye examinations are recommended to prevent visual impairment. Unfortunately, patients are less likely to seek care as they age, which may be the result of significant travel and time burdens associated with going to an eye clinic in person. A new method of eye-care delivery that mitigates distance barriers and improves access was developed to improve screening for potentially blinding conditions. We present the quality data from the early experience (first 13 months) of Technology-Based Eye Care Services (TECS), a novel ophthalmologic telemedicine program. DESIGN: With TECS, a trained ophthalmology technician is stationed in a primary care clinic away from the main hospital. The ophthalmology technician follows a detailed protocol that collects information about the patient's eyes. The information then is interpreted remotely. Patients with possible abnormal findings are scheduled for a face-to-face examination in the eye clinic. PARTICIPANTS: Any patient with no known ocular disease who desires a routine eye screening examination is eligible. METHODS: Technology-Based Eye Care Services was established in 5 primary care clinics in Georgia surrounding the Atlanta Veterans Affairs hospital. MAIN OUTCOME MEASURES: Four program operation metrics (patient satisfaction, eyeglass remakes, disease detection, and visit length) and 2 access-to-care metrics (appointment wait time and no-show rate) were tracked. RESULTS: Care was rendered to 2690 patients over the first 13 months of TECS. The program has been met with high patient satisfaction (4.95 of 5). Eyeglass remake rate was 0.59%. Abnormal findings were noted in 36.8% of patients and there was >90% agreement between the TECS reading and the face-to-face findings of the physician. TECS saved both patient (25% less) and physician time (50% less), and access to care substantially improved with 99% of patients seen within 14 days of contacting the eye clinic, with a TECS no-show rate of 5.2%. CONCLUSIONS: The early experience with TECS has been promising. Tele-ophthalmology has the potential to improve operational efficiency, reduce cost, and significantly improve access to care. Although further study is necessary, TECS shows potential to help prevent avoidable vision loss.

Retrospective Evaluation of a Teleretinal Screening Program in Detecting Multiple Nondiabetic Eye Diseases.

BACKGROUND: Diabetic teleretinal screening programs have been utilized successfully across the world to detect diabetic retinopathy (DR) and are well validated. Less information, however, exists on the ability of teleretinal imaging to detect nondiabetic ocular pathology. INTRODUCTION: This study performed a retrospective evaluation to assess the ability of a community-based diabetic teleretinal screening program to detect common ocular disease other than DR. MATERIALS AND METHODS: A retrospective chart review of 1,774 patients who underwent diabetic teleretinal screening was performed. Eye clinic notes from the Veterans Health Administration's electronic medical record, Computerized Patient Record System, were searched for each of the patients screened through teleretinal imaging. When a face-to-face examination note was present, the physical findings were compared to those obtained through teleretinal imaging. Sensitivity, specificity, and positive and negative predictive values were calculated for suspicious nerve, cataract, and age-related macular degeneration. RESULTS: A total of 903 patients underwent a clinical examination. The positive predictive value was highest for cataract (100%), suspicious nerve (93%), and macular degeneration (90%). The negative predictive value and the percent agreement between teleretinal imaging and a clinical examination were over 90% for each disease category. DISCUSSION: A teleretinal imaging protocol may be used to screen for other common ocular diseases. CONCLUSION: It may be feasible to use diabetic teleretinal photographs to screen patients for other potential eye diseases. Additional elements of the eye workup may be added to enhance accuracy of disease detection. Further study is necessary to confirm this initial retrospective review.

A novel device for accurate and efficient testing for vision-threatening diabetic retinopathy.

AIMS: To evaluate the performance of the RETeval device, a handheld instrument using flicker electroretinography (ERG) and pupillography on undilated subjects with diabetes, to detect vision-threatening diabetic retinopathy (VTDR). METHODS: Performance was measured using a cross-sectional, single armed, non-interventional, multi-site study with Early Treatment Diabetic Retinopathy Study 7-standard field, stereo, color fundus photography as the gold standard. The 468 subjects were randomized to a calibration phase (80%), whose ERG and pupillary waveforms were used to formulate an equation correlating with the presence of VTDR, and a validation phase (20%), used to independently validate that equation. The primary outcome was the prevalence-corrected area under the receiver operating characteristic (ROC) curve for the detection of VTDR. RESULTS: The area under the ROC curve was 0.86 for VTDR. With a sensitivity of 83%, the specificity was 78% and the negative predictive value was 99%. The average testing time was 2.3 min. CONCLUSIONS: With a VTDR prevalence similar to that in the U.S., the RETeval device will identify about 75% of the population as not having VTDR with 99% accuracy. The device is simple to use, does not require pupil dilation, and has a short testing time.