ArticleInvestigative ophthalmology & visual science2024
Quantitative Optical Coherence Tomography Detection of New Neovascular Branches and Association With Exudation Recurrence in Neovascular Age-Related Macular Degeneration.
Article in Investigative ophthalmology & visual science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: The purpose of this study was to investigate the clinical role of multi-signal quantitative optical coherence tomography angiography (OCTA) perfusion sampling in neovascular age-related macular degeneration (AMD). Methods: The study was designed as a cross-sectional case series. We collected data from already treated macular neovascularization (MNV), characterized by (I) clinically relevant recurrent exudation, (II) nonclinically relevant recurrent exudation, and (III) inactive lesion. We proposed a new OCTA metric, calculating the gap between high-resolution (HR) and high-speed (HS) OCTA samplings, hypothesizing that this gap might improve the detection of new secondary MNV branches, being also associated with exudation recurrence. Main outcome measures were the HR-HS gap-based categorization of MNV lesions and the assessment of its association with exudative, minimally exudative, and inactive lesions. Results: Our cohort (which consisted of 32 MNV eyes; 32 patients; mean disease duration 5 years) was classified as type 1 (17; 53%), type 2 (11; 34%), or mixed type (4; 13%) MNV. Subretinal fibrosis was found in 17 out of 32 eyes (53%), whereas outer retinal atrophy involved 22 of 32 eyes (69%). HR-HS MNV gap was significantly different among MNV subgroups: 18% for the exudative subgroup, 12% for the minimally exudative subgroup, and 4% for the inactive subgroup. HR-HS gap significantly correlated with best corrected visual acuity (BCVA), disease duration, fibrosis, and outer retinal atrophy. Conclusions: HR-HS gap is a novel quantitative metric to detect the secondary novel branches of AMD-related MNV. This parameter is clinically relevant because it is associated with fluid recurrence. The integration of HR-HS gap in artificial intelligence models might help to predict MNV reactivation and to optimize treatment strategies.
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