Autosomal dominant neovascular inflammatory vitreoretinopathy with CAPN5 c.731T > C gene mutation; clinical management of a family cohort and review of the literature.

BACKGROUND: To report a cohort of patients with clinically and genetically diagnosed autosomal dominant neovascular inflammatory vitreoretinopathy (ADNIV) and showcase the spectrum of the disease utilizing multimodal imaging and genetic testing. Additionally, the utility of multimodal imaging in guiding treatment will also be illustrated. MATERIALS/METHODS: Five patients from a single-family pedigree in Ohio with clinical signs of ADNIV were evaluated. Medical history, family history, and complete ocular examinations were obtained during regular clinic visits. Multimodal imaging including ocular coherence tomography, fluorescein angiography, wide-field fundus photographs, and Humphrey visual field testing was obtained for all five patients. Additionally, genetic testing for the Calpain-5 (CAPN5) gene was conducted on all patients. RESULTS: All five patients were noted to have a CAPN5 c.731T > C (p.L244P) mutation on genetic testing. Using multimodal imaging to supplement the clinical examination, pathologic changes such as retinal vascular inflammation, macular edema, and tractional retinal membranes were well illustrated and monitored over time. This allowed for earlier intervention when appropriate such as with intraocular steroid or systemic anti-inflammatory treatments. CONCLUSION: Phenotypic presentation varied among patients in this series, but is consistent with the spectrum of pathologic changes previously described in patients with other CAPN5 gene mutations. Monitoring of patients with ADNIV utilizing multimodal imaging can help better assess progression of this disease and guide treatment decisions. Additionally, increased genetic testing in patients with inherited retinal diseases may reveal novel gene mutations that could serve as potential targets for future genetic treatment regimens.

Diabetic Retinopathy Telemedicine Outcomes With Artificial Intelligence-Based Image Analysis, Reflex Dilation, and Image Overread.

PURPOSE: To examine real-world telemedicine outcomes of diabetic retinopathy (DR) screening with artificial intelligence (AI)-based image analysis, reflex dilation, and secondary image overread in a primary care setting. DESIGN: Validity and reliability analysis. METHODS: Single institution review of 1052 consecutive adult patients who received diabetic retinopathy photoscreening in the primary care setting over an 18-month period. Nonmydriatic fundus photographs were acquired and analyzed by the IDx-DR AI-based system. When nonmydriatic images were ungradable, reflex dilation (1% tropicamide) and mydriatic photography were performed for repeat AI-based analysis. Manual overread was performed on all images. Patient demographics, clinical characteristics, and screening outcomes were recorded. RESULTS: A total of 965 of 1052 patients (91.7%) had AI-gradable fundus photographs: 580 had gradable nonmydriatic imaging (55.1%) and 440 of 472 patients with ungradable nonmydriatic photographs had reflex dilation (93.2%). One hundred thirty-eight of 965 patients (14.3%) were AI-graded as "positive" (greater than mild NPDR) and 827 of 965 were "negative" (85.7%), with 100% sensitivity (95% CI 90.8-100%), 89.2% specificity (95% CI 87.0-91.1%), 27.5% positive predictive value (95% CI 24.0-31.4%), and 100% negative predictive value (95% CI 99.6-100%) compared with manual overread assessment of greater than mild NPDR requiring further evaluation with a comprehensive dilated examination. Image gradeability was inversely related to patient age: 93.5% gradable (61.9% nonmydriatic) for patients aged <70 years vs 85.3% (31.0% nonmydriatic) for patients aged 70+ years (P < .001). CONCLUSION: Incorporation of AI-based image analysis into real-world primary care diabetic retinopathy screening yielded no false negative results and offered excellent image gradeability within a protocol combining nonmydriatic fundus photography and pharmacologic dilation, as needed. Image gradeability was lower with increasing patient age.