Are There Funduscopic and Optical Coherence Tomography Preoperative Characteristics to Predict Surgical Difficulty of Epiretinal Membrane Removal?


Journal

Current eye research
ISSN: 1460-2202
Titre abrégé: Curr Eye Res
Pays: England
ID NLM: 8104312

Informations de publication

Date de publication:
08 2020
Historique:
pubmed: 15 1 2020
medline: 21 8 2021
entrez: 15 1 2020
Statut: ppublish

Résumé

To identify the correlation between preoperative funduscopic and optical coherence tomography characteristics of epiretinal membranes and the difficulty of surgical removal. Fifty eyes of 50 patients with an idiopathic epiretinal membrane (ERM) scheduled for pars plana vitrectomy (PPV) and ERM peeling were included. Surgical videos were analyzed and the difficulty of ERM removal (easy or difficult) determined subjectively by an independent surgeon. Furthermore, the peeling time and amount of grasping were measured on the videos to provide an objective factor of surgery difficulty. Preoperative spectral-domain optical coherence tomography (OCT) images and color fundus photos were analyzed to identify predictive factors for membrane removal difficulty. The subjective surgical difficulty of ERM removal was strongly associated with fibrillary changes between the ERM and retinal nerve fiber layer (RNFL), severe retinal vascular tortuosity and severe glinting fundus reflex. Higher fundus pigmentation was associated with fewer and cystoid spaces with more attempts of grasping ERM. No other preoperative signs were found to be associated with the duration of the peeling time. The presence of fibrillary changes between the ERM and RNFL, severe retinal vascular tortuosity and severe glinting reflex are preoperative findings that have a strong association with the difficulty in ERM peeling.

Identifiants

pubmed: 31935135
doi: 10.1080/02713683.2020.1712425
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1012-1016

Auteurs

Georgios Mylonas (G)

Department of Ophthalmology, Medical University of Vienna , Vienna, Austria.

Markus Schranz (M)

Department of Ophthalmology, Medical University of Vienna , Vienna, Austria.

Michael Georgopoulos (M)

Department of Ophthalmology, Medical University of Vienna , Vienna, Austria.

Stefan Sacu (S)

Department of Ophthalmology, Medical University of Vienna , Vienna, Austria.

Gabor Deak (G)

Department of Ophthalmology, Medical University of Vienna , Vienna, Austria.

Adrian Reumueller (A)

Department of Ophthalmology, Medical University of Vienna , Vienna, Austria.

Lukas Baumann (L)

Center for Medical Statistics, Informatics and Intelligent Systems, Section for Medical Statistics, Medical University of Vienna , Vienna, Austria.

Ursula Schmidt-Erfurth (U)

Department of Ophthalmology, Medical University of Vienna , Vienna, Austria.

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Classifications MeSH