Optical coherence tomography predictors of progression of non-exudative age-related macular degeneration to advanced atrophic and exudative disease.

Age-related macular degeneration Complete retinal pigment epithelium and outer retinal atrophy Incomplete retinal pigment epithelium and outer retinal atrophy Optical coherence tomography

Journal

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
ISSN: 1435-702X
Titre abrégé: Graefes Arch Clin Exp Ophthalmol
Pays: Germany
ID NLM: 8205248

Informations de publication

Date de publication:
Mar 2022
Historique:
received: 08 06 2021
accepted: 19 09 2021
revised: 25 08 2021
pubmed: 5 10 2021
medline: 19 2 2022
entrez: 4 10 2021
Statut: ppublish

Résumé

To study the natural history of optical coherence tomography (OCT) imaging-based findings seen in non-exudative age-related macular degeneration (neAMD) and model their relative likelihood in predicting development of incomplete retinal pigment epithelium and outer retinal atrophy (iRORA), complete retinal pigment epithelium and outer retinal atrophy (cRORA), and neovascular AMD (nAMD). Retrospective chart review was performed at two academic practices. Patients diagnosed with neAMD for whom yearly OCT scans were obtained for at least 4 consecutive years were included. Baseline demographic, visual acuity, AREDS staging, and OCT data were collected. OCTs were assessed for the presence or absence of eleven features previously individually associated with progression of neAMD, both at baseline, and on all subsequent follow-up scans. Likewise, charts were reviewed to assess visual acuity and staging of NEAMD at all follow-up visits. A multivariate regression analysis was constructed to determine predictors of iRORA, cRORA, and nAMD. A total of 107 eyes of 88 patients were evaluated. Follow-up included yearly OCTs obtained over at least 4 consecutive years follow-up (range: 50-94 months). During the follow-up period, 17 eyes progressed to iRORA while 25 progressed to cRORA and 16 underwent conversion to nAMD. Predictors of conversion to iRORA and cRORA included integrity of the external limiting membrane (p = 0.02), the ellipsoid zone (p = 0.01), and the cone outer segment line (p = 0.003) and the presence of intraretinal hyporeflective spaces (p = 0.009), drusen ooze (p = 0.05), and drusen collapse (p = 0.001). OCT features predictive of conversion to nAMD included outer nuclear layer (ONL) loss (p = 0.01), presence of intraretinal (p = 0.001) and subretinal (p = 0.005) hyporeflective spaces, and drusen collapse (p = 0.003). Of these multiple factors predictive of progression of neAMD, the OCT feature most strongly correlated to progression to iRORA/cRORA was drusen collapse, and the feature most predictive of conversion to nAMD was the presence of intraretinal hyporeflective spaces.

Identifiants

pubmed: 34605954
doi: 10.1007/s00417-021-05419-2
pii: 10.1007/s00417-021-05419-2
doi:

Substances chimiques

Angiogenesis Inhibitors 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

737-746

Informations de copyright

© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Références

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Auteurs

Sohani Amarasekera (S)

University of Pittsburgh Medical Center, 203 Lothrop Street, Pittsburgh, PA, 15213, USA.

Anindya Samanta (A)

Texas Tech University Health Sciences Center School of Medicine, Lubbock, TX, USA.

Mahima Jhingan (M)

Joan and Irwin Jacobs Retina Center, La Jolla, CA, USA.

Supriya Arora (S)

Princess Margaret Hospital, Nassau, Bahamas.

Sumit Singh (S)

Joan and Irwin Jacobs Retina Center, La Jolla, CA, USA.

Davide Tucci (D)

Universita Degli Studi Di Perugia Facolta Di Medicina E Chirurgia, Perugia, Umbria, Italy.

Marco Lupidi (M)

Universita Degli Studi Di Perugia Facolta Di Medicina E Chirurgia, Perugia, Umbria, Italy.

Jay Chhablani (J)

University of Pittsburgh Medical Center, 203 Lothrop Street, Pittsburgh, PA, 15213, USA. jay.chhablani@gmail.com.

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Classifications MeSH