Aqueous Chlorhexidine for Intravitreal Injection Antisepsis: A Case Series and Review of the Literature.

PURPOSE: To determine the incidence of endophthalmitis in a large clinical series using aqueous chlorhexidine for antisepsis before intravitreal injection and to review the ophthalmic literature regarding chlorhexidine efficacy and safety. DESIGN: Multicenter retrospective case series. PARTICIPANTS: All patients receiving intravitreal injections from 7 retinal specialists. METHODS: An audit of intravitreal injections performed by retinal specialists who exclusively used aqueous chlorhexidine 0.05% or 0.1% for prophylaxis of infective endophthalmitis was undertaken. The incidence of endophthalmitis was determined from August 1, 2011, to February 28, 2015. A literature review was performed to critically appraise the ocular safety and efficacy of aqueous chlorhexidine. MAIN OUTCOME MEASURES: Incidence of endophthalmitis after intravitreal injections. RESULTS: A total of 40 535 intravitreal injections were performed by 7 retinal specialists across 3 centers. Chlorhexidine was well tolerated, and only 1 patient with a suspected allergic reaction was noted. Three cases of endophthalmitis were identified with 1 culture-positive case. The 0.0074% (1 in 13 512) per-injection rate of endophthalmitis in this series compares favorably with previous series in which povidone-iodine has been used. CONCLUSIONS: Aqueous chlorhexidine was associated with a low rate of postinjection endophthalmitis and was well tolerated by patients.

Automated multi-lesion detection for referable diabetic retinopathy in indigenous health care.

Diabetic Retinopathy (DR) is a complication of diabetes mellitus that affects more than one-quarter of the population with diabetes, and can lead to blindness if not discovered in time. An automated screening enables the identification of patients who need further medical attention. This study aimed to classify retinal images of Aboriginal and Torres Strait Islander peoples utilizing an automated computer-based multi-lesion eye screening program for diabetic retinopathy. The multi-lesion classifier was trained on 1,014 images from the São Paulo Eye Hospital and tested on retinal images containing no DR-related lesion, single lesions, or multiple types of lesions from the Inala Aboriginal and Torres Strait Islander health care centre. The automated multi-lesion classifier has the potential to enhance the efficiency of clinical practice delivering diabetic retinopathy screening. Our program does not necessitate image samples for training from any specific ethnic group or population being assessed and is independent of image pre- or post-processing to identify retinal lesions. In this Aboriginal and Torres Strait Islander population, the program achieved 100% sensitivity and 88.9% specificity in identifying bright lesions, while detection of red lesions achieved a sensitivity of 67% and specificity of 95%. When both bright and red lesions were present, 100% sensitivity with 88.9% specificity was obtained. All results obtained with this automated screening program meet WHO standards for diabetic retinopathy screening.

Automated detection of proliferative retinopathy in clinical practice.

Timely intervention for diabetic retinopathy (DR) lessens the possibility of blindness and can save considerable costs to health systems. To ensure that interventions are timely and effective requires methods of screening and monitoring pathological changes, including assessing outcomes. Fractal analysis, one method that has been studied for assessing DR, is potentially relevant in today's world of telemedicine because it provides objective indices from digital images of complex patterns such as are seen in retinal vasculature, which is affected in DR. We introduce here a protocol to distinguish between nonproliferative (NPDR) and proliferative (PDR) changes in retinal vasculature using a fractal analysis method known as local connected dimension (D(conn)) analysis. The major finding is that compared to other fractal analysis methods, D(conn) analysis better differentiates NPDR from PDR (p = 0.05). In addition, we are the first to show that fractal analysis can be used to differentiate between NPDR and PDR using automated vessel identification. Overall, our results suggest this protocol can complement existing methods by including an automated and objective measure obtainable at a lower level of expertise that experts can then use in screening for and monitoring DR.

An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice.

BACKGROUND: With the increase in the prevalence of diabetes, rural optometric clinics stand to increase their patient load and assessment of diabetic eye disease. This study aimed to assess whether automated identification of diabetic retinopathy based on the presence of microaneurysms is an effective tool in clinical practice. METHODS: We analysed 758 fundal images of 385 patients with diabetes attending the clinic obtained using a Canon CR5 with an EOS10 digital camera through a dilated pupil. Five optometrists employed in the clinic assessed the diabetic retinopathy using binocular indirect ophthalmoscopy. The sensitivity and specificity of the automated system used to analyse the retinal fundal images was determined by comparison with optometric and ophthalmologic assessment. RESULTS: The optometrists achieved 97 per cent sensitivity at 88 per cent specificity with respect to the ophthalmic classification for detecting retinopathy. CONCLUSION: The automated retinopathy detector achieved 85 per cent sensitivity at 90 per cent specificity at detecting retinopathy. The automated microaneurysm detector has a lower sensitivity compared to the optometrists but meets NHMRC guidelines. It may impact on the efficiency of rural optometric practices by early identification of diabetic retinopathy. Automated assessment can save time and be cost-effective, and provide a history of changes in the retinal fundus and the opportunity for instant patient education using the digital images.