A classification model for predicting eye disease in newly diagnosed people with type 2 diabetes. Issue 2 (May 2015)

• Record Type: Journal Article
• Authors: Cichosz, Simon Lebech; Johansen, Mette Dencker; Knudsen, Søren Tang; Hansen, Troels Krarup; Hejlesen, Ole
• Is Part Of: Diabetes research and clinical practice. Volume 108:Issue 2(2015)
• Page Start: 210
• Page End: 215
• Publication Date: 2015-05
• Subjects: Diabetic retinopathy -- Risk factors -- Screening -- Prediction model; Diabetes -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462
• DOI: 10.1016/j.diabres.2015.02.020
• Languages: English
• ISSNs: 0168-8227

Abstract

Highlights: Proper screening for retinopathy may be challenging in many parts of the world. Commonly available information is usable to identify patients at high risk. The model may be used to identify high-risk patients for retinopathy screening. Abstract: Diabetic retinopathy may be present at the time type 2 diabetes is diagnosed, and initial screening encompassing an eye examination performed by an ophthalmologist or optometrist is therefore recommended. However, proper screening for retinopathy may be challenging in many parts of the world. We hypothesized that simple, commonly available patient characteristics can be used to identify patients at high risk for having retinopathy. We investigated data from multiple years extracted from the National Health and Nutrition Examination Survey which holds information about blood glucose and eye examinations. Individuals with hitherto undiagnosed diabetes were classified according to the presence or absence of retinopathy. Linear classification was used to predict which patients had retinopathy at the time of diagnosis. A total of 266 individuals with undiagnosed diabetes were identified from the cohorts. Of these, 222 individuals had no sign of retinopathy, whereas 44 had mild or moderate non-proliferative retinopathy. Using information regarding HbA1c, BMI, waist circumference, age, systolic blood pressure, urinary albumin, and urinary creatinine, we were able to construct a model that predicts the presence of retinopathy with a positive predictive value of 22% and a negative predictive value of 99%. Only one true positive (1/44) with mild non-proliferative retinopathy was falsely classified. A classification model using readily available patient information and routine biochemical measures can be used to identify patients at high risk of having retinopathy at the time their diabetes is diagnosed. The model may be used to identify high-risk patients for retinopathy screening.