Literature DB >> 22913797

Evaluation of peripapillary retinal nerve fiber layer thickness of myopic and hyperopic patients: a controlled study by Stratus optical coherence tomography.

Veysi Öner1, Mehmet Taş, Fatih Mehmet Türkcü, Mehmet Fuat Alakuş, Yalçın Işcan, Ahmet Taylan Yazıcı.   

Abstract

PURPOSE: To investigate the peripapillary retinal nerve fiber layer (RNFL) thickness of myopic and hyperopic eyes in comparison with emmetropic control eyes and to evaluate the correlation between the peripapillary RNFL thickness with axial length and spherical equivalent (SE).
MATERIALS AND METHODS: One hundred fifty-four eyes of 154 subjects were evaluated in this study. Subjects were divided into three groups; myopic group (n = 58 patients), hyperopic group (n = 62 patients) and emmetropic group (control group, n = 34 subjects). The peripapillary RNFL thickness was measured using the Stratus optical coherence tomography (OCT).
RESULTS: The mean peripapillary RNFL was thinner in the myopic group than in the control group (p < 0.05). The RNFLs were thinner in superior, inferior and temporal quadrants (all p < 0.05); whereas it was thicker in nasal quadrant (p < 0.05). The RNFL was thicker only in nasal quadrant (p < 0.05) in the hyperopic group compared with the controls. There were negative correlations between axial length and the mean peripapillary RNFL thickness in the myopic (r = -0.763 p < 0.001) and hyperopic groups (r = -0.266 p < 0.05). However, correction of magnification effect by applying Littmann formula eliminated the relationship between RNFL thickness and axial length/SE.
CONCLUSION: We have shown that peripapillary RNFL thickness profile differed with refractive status and axial length of the eye. In this regard, we would like to caution ophthalmologists when they measure the RNFL thickness in patients with myopic or hyperopic eyes to diagnose glaucoma. Ocular magnification effect should be taken into account by ophthalmologists or Stratus OCT manufacturers.

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Year:  2012        PMID: 22913797     DOI: 10.3109/02713683.2012.715714

Source DB:  PubMed          Journal:  Curr Eye Res        ISSN: 0271-3683            Impact factor:   2.424


  3 in total

1.  Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images.

Authors:  Tae Keun Yoo; Ik Hee Ryu; Jin Kuk Kim; In Sik Lee
Journal:  Eye (Lond)       Date:  2021-10-05       Impact factor: 4.456

2.  The effect of transverse ocular magnification adjustment on macular thickness profile in different refractive errors in community-based adults.

Authors:  Hamed Niyazmand; Gareth Lingham; Paul G Sanfilippo; Magdalena Blaszkowska; Maria Franchina; Seyhan Yazar; David Alonso-Caneiro; David A Mackey; Samantha Sze-Yee Lee
Journal:  PLoS One       Date:  2022-04-13       Impact factor: 3.240

3.  Regression-Based Strategies to Reduce Refractive Error-Associated Glaucoma Diagnostic Bias When Using OCT and OCT Angiography.

Authors:  Keke Liu; Ou Tan; Qi Sheng You; Aiyin Chen; Jonathan C H Chan; Bonnie N K Choy; Kendrick C Shih; Jasper K W Wong; Alex L K Ng; Janice J C Cheung; Michael Y Ni; Jimmy S M Lai; Gabriel M Leung; Liang Liu; David Huang; Ian Y H Wong
Journal:  Transl Vis Sci Technol       Date:  2022-09-01       Impact factor: 3.048

  3 in total

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