ArticleCureus2022
Predictors of Retinal Nerve Fiber Layer Parameters Following Scleral Buckling Surgery in Primary Rhegmatogenous Retinal Detachment.
Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Retrospective study of the effectiveness of scleral buckling under direct vision with operating lamp illumination in 17 cases of retinal detachment associated with retinal dialysis.BMC ophthalmology · 2025Article
- Changes in retinal nerve fiber layer and vessel densities after scleral buckling in patients with rhegmatogenous retinal detachment observed by OCTA.Frontiers in medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to examine predictors of retinal nerve fiber layer (RNFL) parameters following scleral buckling (SB) surgery for primary rhegmatogenous retinal detachment (RRD) and to determine the influence of the magnitude of change in qualitative and quantitative parameters on RNFL.
methodsIn an observational prospective study, 40 subjects who underwent successful retinal reattachment with SB surgery done within one month of RRD were evaluated for the parameters of best-corrected visual acuity (BCVA), refractive error, intraocular pressure (IOP), axial length (AL), anterior chamber depth (ACD), angle opening distance (AOD 500 and AOD 750), trabecular iris surface area (TISA 500 and TISA 750), visual fields, and ganglion cell count (GCC) and RNFL before and three months after SB. We additionally noted qualitative factors like extent, location, and type of buckle; phakic status; and grade of proliferative vitreoretinopathy in the affected eye. The change in value of quantitative parameters was found. The influence of baseline values and magnitude of change of quantitative and qualitative parameters on average RNFL thickness and magnitude of change of RNFL thickness after SB was found.
resultsPost-SB, average RNFL thickness reduced from 108.58±20.38 microns to 103.73±17.98 microns (p =0.042). The baseline temporal upper (TU), temporal lower (TL), and nasal lower (NL) RNFL thickness (p=0.01, p=0.02, p=0.01, respectively) and total deviation (TD) values of visual fields (p=0.01) correlated positively while baseline GCC gross loss of volume (p=0.01) correlated negatively with post-operative RNFL thickness. The TU, TL, and NL RNFL thickness (p=0.04, p=0.01, p=0.01, respectively) and average GCC (p=0.04) correlated negatively with the magnitude of change in RNFL. The magnitude of change in baseline parameters after surgery was correlated with the magnitude of change in average RNFL thickness. It was noticed that change in AL (p<0.01), TISA 500 (p=0.02), TISA 750 (p<0.01), GCC focal loss of volume (p=0.02), and temporal RNFL thickness (p<0.01) correlated positively while the change in refractive error correlated negatively (p=0.04). Except for the grade of proliferative vitreoretinopathy (PVR) (p=0.04), none of the qualitative parameters, including extent, type, and location of the buckle; and phakic status, had a significant association with post-operative average RNFL thickness or magnitude of its change.
conclusionsThe predictors of average RNFL thickness following SB include AL; myopic shift; TISA; visual fields TD; average, TU, TL, and NL RNFL thickness; average GCC, gross and focal loss of volume; and grade of PVR. So an early surgery to prevent preoperative ganglion cell and RNFL loss and progression of PVR is recommended.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.