Abstract
Purpose: :
To evaluate whether macular hole (MH) morphometric data obtained on time-domain optical coherence tomography (StratusOCT) can be used to predict visual outcome in eyes with idiopathic macular holes (MH).
Methods: :
This prospective interventional case series included 10 eyes of 10 patients with stage 2 or 3 MH. Logmar visual acuity (VA), fundus biomicroscopy, lens opacity grading, and OCT imaging were performed preoperatively and at last follow-up visit (at least 6 months after surgery). Included patients had to be pseudophakic or with clear lens before MH surgery. Eyes that developed cataract during follow-up were submitted to phacoemulsification. OCT cross hair scan was obtained three times by the same properly trained operator, and the scan with higher MH diameter was selected for analysis. Height, External MH diameter, interphotoreceptor distance (IPD), and the length of subretinal liquid cuff around MH borders were measured using OCT caliper tool. IPD was defined as the distance between the limits of the hypereflective photoreceptor line in each MH border. Cuff limits was defined as the distance between a perpendicular line passsing in the border of the MH and EPR-photorreceptor separation. Vitrectomy with internal limiting membrane peeling and gas tamponade was performed by one experienced surgeon. MH cuff was measured 3 times by the same operator. Repeatability coefficient (RC), and intraclass correlation coefficient (ICC) were used to assess intra-examiner reproducibility of MH cuff measurements. MH measurements were correlated with VA change before and after surgery, and postoperative VA.
Results: :
MH horizontal cuff measurements demonstrated good intra-observer reproducibility (HA: RC=63micra, ICC=0.944; HB: RC=22micra, ICC=0.961). All eyes attained anatomical MH closure after surgery. Table 1 shows the correlation between VA and MH measurements. Mean MHI was 1.0±0.4, mean IPD was 1325±689 micra, and mean MH cuff was 348±123 micra.
Keywords: macular holes • vitreoretinal surgery • imaging/image analysis: clinical