Implementation of medical retina virtual clinics in a tertiary eye care referral centre.

BACKGROUND: The increasing incidence of medical retinal diseases has created capacity issues across UK. In this study, we describe the implementation and outcomes of virtual medical retina clinics (VMRCs) at Moorfields Eye Hospital, South Division, London. It represents a promising solution to ensure that patients are seen and treated in a timely fashion METHODS: First attendances in the VMRC (September 2016-May 2017) were included. It was open to non-urgent external referrals and to existing patients in a face-to-face clinic (F2FC). All patients received visual acuity testing, dilated fundus photography and optical coherence tomography scans. Grading was performed by consultants, fellows and allied healthcare professionals. Outcomes of these virtual consultations and reasons for F2FC referrals were assessed. RESULTS: A total number of 1729 patients were included (1543 were internal and 186 external referrals). The majority were diagnosed with diabetic retinopathy (75.1% of internal and 46.8% of external referrals). Of the internal referrals, 14.6% were discharged, 54.5% continued in VMRC and 30.9% were brought to a F2FC. Of the external referrals, 45.5% were discharged, 37.1% continued in VMRC and 17.4% were brought to a F2FC. The main reason for F2FC referrals was image quality (34.7%), followed by detection of potentially treatable disease (20.2%). CONCLUSION: VMRC can be implemented successfully using existing resources within a hospital eye service. It may also serve as a first-line rapid-access clinic for low-risk referrals. This would enable medical retinal services to cope with increasing demand and efficiently allocate resources to those who require treatment.

Maternally inherited diabetes and deafness (MIDD): diagnosis and management.

Maternally inherited diabetes with deafness is rare diabetes caused by a mitochondrial DNA defect. 85% of cases are associated with m.3243A>G mutation. It is important to diagnose this form of diabetes because of the unique management issues and associated comorbidities. A very strong family history of diabetes, deafness and presence of retinal dystrophy should prompt an investigation for MIDD. Microvascular complications out of keeping with duration of diabetes are another clue to the diagnosis. Retinal and renal manifestations of mitochondrial disease may be confused for diabetic complications. Glutamic acid decarboxylase (GAD) autoantibody negativity in a nonobese diabetic is another clue. Cardiac conduction defects and GDM may also raise suspicion as to the diagnosis. Recognizing this etiology of DM should promote family screening, genetic counseling, screening of associated comorbidities, avoidance of metformin, and cautious use of statins. We report a 77 years old lady with MIDD who was being followed up as insulin requiring type 2 diabetes. We then identified 5 more patients with MIDD in the same clinic. They all had A3243 mutation with characteristic clinical presentation. The pharmacological approaches discussed in the paper are unlikely to work in these patients as they were diagnosed late.

Assessment of automated disease detection in diabetic retinopathy screening using two-field photography.

AIM: To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic photography. METHODS: Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening service were processed by the Medalytix iGrading™ automated grading system. For each screening episode macular-centred and disc-centred images of both eyes were acquired and independently graded according to the English national grading scheme. Where discrepancies were found between the automated result and original manual grade, internal and external arbitration was used to determine the final study grades. Two versions of the software were used: one that detected microaneurysms alone, and one that detected blot haemorrhages and exudates in addition to microaneurysms. Results for each version were calculated once using both fields and once using the macula-centred field alone. RESULTS: Of the 8,271 episodes, 346 (4.2%) were considered unassessable. Referable disease was detected in 587 episodes (7.1%). The sensitivity of the automated system for detecting unassessable images ranged from 97.4% to 99.1% depending on configuration. The sensitivity of the automated system for referable episodes ranged from 98.3% to 99.3%. All the episodes that included proliferative or pre-proliferative retinopathy were detected by the automated system regardless of configuration (192/192, 95% confidence interval 98.0% to 100%). If implemented as the first step in grading, the automated system would have reduced the manual grading effort by between 2,183 and 3,147 patient episodes (26.4% to 38.1%). CONCLUSION: Automated grading can safely reduce the workload of manual grading using two field, mydriatic photography in a routine screening service.

SDOCT imaging to identify macular pathology in patients diagnosed with diabetic maculopathy by a digital photographic retinal screening programme.

INTRODUCTION: Diabetic macular edema (DME) is an important cause of vision loss. England has a national systematic photographic retinal screening programme to identify patients with diabetic eye disease. Grading retinal photographs according to this national protocol identifies surrogate markers for DME. We audited a care pathway using a spectral-domain optical coherence tomography (SDOCT) clinic to identify macular pathology in this subset of patients. METHODS: A prospective audit was performed of patients referred from screening with mild to moderate non-proliferative diabetic retinopathy (R1) and surrogate markers for diabetic macular edema (M1) attending an SDOCT clinic. The SDOCT images were graded by an ophthalmologist as SDOCT positive, borderline or negative. SDOCT positive patients were referred to the medical retina clinic. SDOCT negative and borderline patients were further reviewed in the SDOCT clinic in 6 months. RESULTS: From a registered screening population of 17 551 patients with diabetes mellitus, 311 patients met the inclusion criteria between (March 2008 and September 2009). We analyzed images from 311 patients' SDOCT clinic episodes. There were 131 SDOCT negative and 12 borderline patients booked for revisit in the OCT clinic. Twenty-four were referred back to photographic screening for a variety of reasons. A total of 144 were referred to ophthalmology with OCT evidence of definite macular pathology requiring review by an ophthalmologist. DISCUSSION: This analysis shows that patients with diabetes, mild to moderate non-proliferative diabetic retinopathy (R1) and evidence of diabetic maculopathy on non-stereoscopic retinal photographs (M1) have a 42.1% chance of having no macular edema on SDOCT imaging as defined by standard OCT definitions of DME when graded by a retinal specialist. SDOCT imaging is a useful adjunct to colour fundus photography in screening for referable diabetic maculopathy in our screening population.