Abstract
BackgroundDiabetic retinopathy is the most common cause of blindness in working-age adults. Characteristics of patients with diabetes presenting to a retina subspecialty clinic have not been adequately studied, limiting our ability to risk stratify patients with diabetic retinopathy. Our goal is to describe an innovative program that collects structured, longitudinal data on patients with diabetes in a retina clinic, and identifies population characteristics to define patient risk stratification.MethodsDemographics, body-mass index, blood pressure, hemoglobin A1c, smoking history, diabetes type, diabetes duration, kidney disease history, and diagnosis codes were collected on all patients with diabetes at the Kellogg Eye Center retina clinic. A mixed effects negative binomial regression was then performed to assess visit frequency as a function of these variables. Visit frequency was used as a marker for cost of care. A subgroup of patients was surveyed about knowledge of diabetes management goals and barriers to better self-management.ResultsThere were 2916 patients in the cohort with 1014 in the subgroup. The cohort was predominantly Caucasian (74.5%), with a mean age of 64 years (range 13–99) and a relatively even distribution of sex (53.2% men). The mean maximum hemoglobin A1c was 8.0% (range 4.3–15.7%), and 57.1% had a diagnosis of diabetic retinopathy. Patients averaged 3.9 visits (range 1–27) during the 18-month study period. Blood pressure and duration of diabetes were positively associated with visit frequency (p < 0.0001, p < 0.0001, respectively). Of the surveyed patients, 87.6% knew their goal hemoglobin A1c, while only 45.9% identified the correct blood pressure goal. The most common reported barrier to better self-management was “it’s just not working” (47.1%).ConclusionsImplementation of this program enables the creation of a longitudinal dataset on patients with diabetes. Resulting data can be used to improve quality of care provided to patients at a retina clinic. The findings suggest considerable healthcare resources are being directed to a small patient population. This enhanced understanding of characteristics of patients with diabetes will improve efforts to preserve vision and utilize health system resources efficiently.
Highlights
Diabetic retinopathy is the most common cause of blindness in working-age adults
The purpose of this study is to report on the novel implementation of a sustainable program that combines survey data, electronic health record (EHR) data, and when otherwise unavailable, screening data obtained during routine ophthalmology clinic visits, and to report on initial findings from analysis of this data
Demographics, body mass index (BMI), Blood pressure (BP), hemoglobin A1c (HbA1c), smoking history, diabetes type, diabetes duration, kidney disease history based on both self-report and microalbuminuria (≥ 30 μg albumin/mg creatinine), and diagnosis codes were collected on all patients with diabetes presenting to the Kellogg Eye Center retina clinic for new patient visits and return visits from July 1, 2016 to December 30, 2017 via survey questions and information available in the EHR
Summary
Diabetic retinopathy is the most common cause of blindness in working-age adults. Our goal is to describe an innovative program that collects structured, longitudinal data on patients with diabetes in a retina clinic, and identifies population characteristics to define patient risk stratification. Diabetic retinopathy (DR) is one of the most common complications of diabetes mellitus [1] and is the leading cause of blindness in working-age adults in the world [2], with the number of affected individuals worldwide expected to increase from 126 million in 2010 to 191 million by 2030 [2]. This, is likely an under-estimation of the current direct costs, as treatment of DR has changed significantly since 2004 with the introduction of intravitreal anti-vascular endothelial growth factor agents. We describe a novel program designed to collect systemic health data in the setting of an ophthalmology clinic, with the goal of improving care and resource management in the future
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