Model simulation of the patient flow through a screening centre for diabetic retinopathy.

PURPOSE: To construct a quantitative, flexible and simplified mathematical model of the patient flow through the Eye Clinic at the Steno Diabetes Centre (SDC) in order to enable rational dimensioning and assess the effects of modifications. METHODS: Patient data were drawn from the Eye Care database at the SDC. A simple patient flow model was constructed, allowing simultaneous adjustments of all variables, and the model was tested. Two scenarios were simulated: (1) adjusting the algorithm that assigns the follow-up intervals, and (2) increasing the population size to include all patients with diabetes in Copenhagen County. RESULTS: The model can describe the patient flow under steady state conditions, but is less precise in predicting transient changes with the present set-up. Accordingly all simulations were run for a substantial number of iterations. The two scenarios illustrate the usefulness of the model by calculating the required photographic examination capacity for the specific population, thereby allowing better estimations of future dimensioning of the organization. CONCLUSION: The study presents a patient flow model that can be used to illustrate the effects of proposed changes prior to their implementation, specifically with respect to the capacity of the system.

Screening for diabetic retinopathy using a digital non-mydriatic camera compared with standard 35-mm stereo colour transparencies.

PURPOSE: To investigate the use of a digital non-mydriatic camera for determining the ETDRS clinical level of diabetic retinopathy, and to evaluate its use in a screening setting to appropriately determine the need for referral to an ophthalmologist (ETDRS level > or = 35). METHODS: A total of 83 patients with diabetes were photographed with and without pharmacological pupil dilation at an ophthalmology department using a digital non-mydriatic camera, obtaining two sets of five non-stereoscopic, 45 degree field images of each eye. ETDRS seven standard field, 35-mm stereoscopic colour fundus photographs were also obtained. A subgroup of 59 patients was photographed at an optician's shop using the digital non-mydriatic camera without pupil dilation. RESULTS: There was substantial agreement between the clinical level of diabetic retinopathy assessed from the 35-mm photographs and the digital images: the ophthalmology department (kappa = 0.76) with pupil dilation and (kappa = 0.66) without pupil dilation, respectively, and at the optician's (kappa = 0.60 without pupil dilation). With respect to the need for referral to an ophthalmologist, there was almost perfect agreement in the ophthalmology department (kappa = 0.88) with pupil dilation and (kappa = 0.84) without pupil dilation, respectively, and those taken at the optician's (kappa = 0.87 without pupil dilation). CONCLUSION: A digital non-mydriatic camera may be used in a screening situation to appropriately determine the need for referral to an ophthalmologist (ETDRS level > or = 35).

Diabetic retinopathy screening using digital non-mydriatic fundus photography and automated image analysis.

PURPOSE: To investigate the use of automated image analysis for the detection of diabetic retinopathy (DR) in fundus photographs captured with and without pharmacological pupil dilation using a digital non-mydriatic camera. METHODS: A total of 83 patients (165 eyes) with type 1 or type 2 diabetes, representing the full spectrum of DR, were photographed with and without pharmacological pupil dilation using a digital non-mydriatic camera. Two sets of five overlapping, non-stereoscopic, 45-degree field images of each eye were obtained. All images were graded in a masked fashion by two readers according to ETDRS standards and disagreements were settled by an independent adjudicator. Automated detection of red lesions as well as image quality control was made: detection of a single red lesion or insufficient image quality was categorized as possible DR. RESULTS: At patient level, the automated red lesion detection and image quality control combined demonstrated a sensitivity of 89.9% and specificity of 85.7% in detecting DR when used on images captured without pupil dilation, and a sensitivity of 97.0% and specificity of 75.0% when used on images captured with pupil dilation. For moderate non-proliferative or more severe DR the sensitivity was 100% for images captured both with and without pupil dilation. CONCLUSION: Our results demonstrate that the described automated image analysis system, which detects the presence or absence of DR, can be used as a first-step screening tool in DR screening with considerable effectiveness.