Abstract
Purpose::
Quantitative assessment of macular thickening by optical coherent tomography (OCT) is useful in predicting response to treatment of all types of macular edema. Three basic OCT patterns of uveitic macular oedema include sponge-like retinal swelling (diffuse), cystoid macular edema, and serous retinal detachment. However, the usefulness of OCT patterns in predicting response to treatment has not been published to date. We evaluated the therapeutic effects on the different morphological patterns of uveitic macular edema and macular thickness using serial OCT.
Methods::
Fifty consecutive patients with a clinical diagnosis of new or recurrent macular edema due to uveitis were examined with serial OCT for a year. The correlation between different patterns of macular edema (diffuse macular edema, inner cystoid edema, outer cystoid edema, edema involving both inner and outer layers of retina and serous retinal detachment) and change in logMAR visual acuity and the recorded variables including age, gender, systemic disease associated with uveitis, location and duration of uveitis, and duration of macular edema were examined. Main outcome measures were response to treatment measured as change in logMAR visual acuity and evolution of patterns of macular edema.
Results::
Diffuse macular oedema, external cystoid and serous retinal detachment responded well to treatment. Cysts in the inner retinal layers were more resistant to treatment. The cysts in the outer layers disappeared faster than cysts in the inner layers in patients with cysts in both layers at baseline. Multivariate analysis showed that cystoid macular edema (all types) (p=0.03), and inner cystoid edema (p=0.031) were the variables significantly associated with final visual acuity.
Conclusions::
Assessment of patterns of uveitic macular edema by OCT gives more useful information on the prognosis than the central macular thickness. Inner retinal cystoid edema is more resistant to treatment than any other patterns of edema.
Keywords: edema • imaging/image analysis: clinical • inflammation