Ganglion cell-inner plexiform layer thickness of high definition optical coherence tomography in perimetric and preperimetric glaucoma

Investigative Ophthalmology and Visual Science

Volumn 55, Issue 8, 2014, Pages 4768-4775

  Begum, Viquar U ^a   Addepalli, Uday K ^a   Yadav, Ravi K ^a   Shankar, Kalmuri ^a   Senthil, Sirisha ^a   Garudadri, Chandra S ^a   Rao, Harsha L ^a  

^a L V PRASAD EYE INSTITUTE   (India)

Author keywords

Ganglion cell inner plexiform layer; Glaucoma; High definition optical coherence tomography

Indexed keywords

ADULT; ARTICLE; CONTROLLED STUDY; CROSS-SECTIONAL STUDY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; GANGLION CELL INNER PLEXIFORM LAYER THICKNESS; GLAUCOMA; HUMAN; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; OPTIC NERVE; OPTICAL COHERENCE TOMOGRAPHY; PRIORITY JOURNAL; RETINA GANGLION CELL; RETINAL NERVE FIBER LAYER THICKNESS; SENSITIVITY AND SPECIFICITY; VISUAL SYSTEM PARAMETERS;

GLAUCOMA, HIGH DEFINITION OPTICAL COHERENCE TOMOGRAPHY, GANGLION CELL; INNER PLEXIFORM LAYER;

ADULT; CROSS-SECTIONAL STUDIES; FEMALE; GLAUCOMA; HUMANS; IMAGE ENHANCEMENT; INTRAOCULAR PRESSURE; MALE; MIDDLE AGED; NERVE FIBERS; RETINAL GANGLION CELLS; ROC CURVE; TOMOGRAPHY, OPTICAL COHERENCE; VISUAL FIELD TESTS; VISUAL FIELDS;

EID: 84905237130     PISSN: 01460404     EISSN: 15525783     Source Type: Journal    
DOI: 10.1167/iovs.14-14598     Document Type: Article

References (39)

1. Quigley HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmol. 2006; 90: 262-267.
2. Tan O, Chopra V, Lu AT, et al. Detection of macular ganglion cell loss in glaucoma by Fourier-domain optical coherence tomography. Ophthalmology. 2009; 116: 2305-2314.
3. Rao HL, Babu GJ, Sekhar GC. Comparison of the diagnostic capability of the Heidelberg retina tomographs 2 and 3 for glaucoma in the Indian population. Ophthalmology. 2010; 117: 275-281.
4. Rao HL, Babu JG, Addepalli UK, Senthil S, Garudadri CS. Retinal nerve fiber layer and macular inner retina measurements by spectral domain optical coherence tomograph in Indian eyes with early glaucoma. Eye (Lond). 2012; 26: 133-139.
5. Arintawati P, Sone T, Akita T, Tanaka J, Kiuchi Y. The applicability of ganglion cell complex parameters determined from SD-OCT images to detect glaucomatous eyes. J Glaucoma. 2013; 22: 713-718.
6. Rao HL, Addepalli UK, Chaudhary S, et al. Ability of different scanning protocols of spectral domain optical coherence tomography to diagnose preperimetric glaucoma. Invest Ophthalmol Vis Sci. 2013; 54: 7252-7257.
7. Na JH, Lee K, Lee JR, Baek S, Yoo SJ, Kook MS. Detection of macular ganglion cell loss in preperimetric glaucoma patients with localized retinal nerve fibre defects by spectral-domain optical coherence tomography. Clin Exp Ophthalmol. 2013; 41: 870-880.
8. Mwanza JC, Durbin MK, Budenz DL, et al. Glaucoma diagnostic accuracy of ganglion cell-inner plexiform layer thickness: comparison with nerve fiber layer and optic nerve head. Ophthalmology. 2012; 119: 1151-1158.
9. Takayama K, Hangai M, Durbin M, et al. A novel method to detect local ganglion cell loss in early glaucoma using spectraldomain optical coherence tomography. Invest Ophthalmol Vis Sci. 2012; 53: 6904-6913.
10. Kotowski J, Folio LS, Wollstein G, et al. Glaucoma discrimination of segmented cirrus spectral domain optical coherence tomography (SD-OCT) macular scans. Br J Ophthalmol. 2012; 96: 1420-1425.
11. Jeoung JW, Choi YJ, Park KH, Kim DM. Macular ganglion cell imaging study: glaucoma diagnostic accuracy of spectraldomain optical coherence tomography. Invest Ophthalmol Vis Sci. 2013; 54: 4422-4429.
12. Akashi A, Kanamori A, Nakamura M, Fujihara M, Yamada Y, Negi A. Comparative assessment for the ability of Cirrus, RTVue, and 3D-OCT to diagnose glaucoma. Invest Ophthalmol Vis Sci. 2013; 54: 4478-4484.
13. Nouri-Mahdavi K, Nowroozizadeh S, Nassiri N, et al. Macular ganglion cell/inner plexiform layer measurements by spectral domain optical coherence tomography for detection of early glaucoma and comparison to retinal nerve fiber layer measurements. Am J Ophthalmol. 2013; 156: 1297-1307.
14. Mwanza JC, Budenz DL, Godfrey DG, et al. Diagnostic performance of optical coherence tomography ganglion cellinner plexiform layer thickness measurements in early glaucoma. Ophthalmology. 2014; 121: 849-854.
15. Sato S, Hirooka K, Baba T, Tenkumo K, Nitta E, Shiraga F. Correlation between the ganglion cell-inner plexiform layer thickness measured with cirrus HD-OCT and macular visual field sensitivity measured with microperimetry. Invest Ophthalmol Vis Sci. 2013; 54: 3046-3051.
16. Sung MS, Yoon JH, Park SW. Diagnostic validity of macular ganglion cell-inner plexiform layer thickness deviation map algorithm using Cirrus HD-OCT in preperimetric and early glaucoma [published online ahead of print November 14, 2013]. J Glaucoma.
17. Kim MJ, Jeoung JW, Park KH, Choi YJ, Kim DM. Topographic profiles of retinal nerve fiber layer defects affect the diagnostic performance of macular scans in preperimetric glaucoma. Invest Ophthalmol Vis Sci. 2014; 55: 2079-2087.
18. Anderson DR, Patella VM. Automated Static Perimetry. 2nd ed. St. Louis, MO: Mosby; 1999: 152.
19. Mwanza JC, Oakley JD, Budenz DL, Anderson DR. Ability of cirrus HD-OCT optic nerve head parameters to discriminate normal from glaucomatous eyes. Ophthalmology. 2011; 118: 241-248.
20. Mwanza JC, Oakley JD, Budenz DL, Chang RT, Knight OJ, Feuer WJ. Macular ganglion cell-inner plexiform layer: automated detection and thickness reproducibility with spectral domain-optical coherence tomography in glaucoma. Invest Ophthalmol Vis Sci. 2011; 52: 8323-8329.
21. Glynn RJ, Rosner B. Accounting for the correlation between fellow eyes in regression analysis. Arch Ophthalmol. 1992; 110: 381-387.
22. Zhou XH, Obuchowski NA, McClish DK. Analysis of correlated ROC data. In: Zhou XH, Obuchowski NA, McClish DK, eds. Statistical Methods in Diagnostic Medicine. New York, NY: John Wiley & Sons, Inc.; 2002: 274-306.
23. Alonzo TA, Pepe MS. Distribution-free ROC analysis using binary regression techniques. Biostatistics. 2002; 3: 421-432.
24. Zhou XH, Obuchowski NA, McClish DK. Comparing the accuracy of two diagnostic tests. In: Zhou XH, Obuchowski NA, McClish DK, eds. Statistical Methods in Diagnostic Medicine. New York, NY: John Wiley & Sons; 2002: 165-194.
25. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988; 44: 837-845.
26. Jaeschke R, Guyatt GH, Sackett DL. Users' guides to the medical literature. III. How to use an article about a diagnostic test. B. What are the results and will they help me in caring for my patients? The Evidence-Based Medicine Working Group. JAMA. 1994; 271: 703-707.
27. Simel DL, Samsa GP, Matchar DB. Likelihood ratios with confidence: sample size estimation for diagnostic test studies. J Clin Epidemiol. 1991; 44: 763-770.
28. Pepe MS. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford, UK: Oxford University Press; 2003: 130-167.
29. Garway-Heath DF, Hitchings RA. Sources of bias in studies of optic disc and retinal nerve fibre layer morphology. Br J Ophthalmol. 1998; 82: 986.
30. Rao HL, Kumbar T, Addepalli UK, et al. Effect of spectrum bias on the diagnostic accuracy of spectral-domain optical coherence tomography in glaucoma. Invest Ophthalmol Vis Sci. 2012; 53: 1058-1065.
31. Kim CS, Seong GJ, Lee NH, Song KC, Namil Study Group KGS. Prevalence of primary open-angle glaucoma in central South Korea the Namil study. Ophthalmology. 2011; 118: 1024-1030.
32. Kim DM, Seo JH, Kim SH, Hwang SS. Comparison of localized retinal nerve fiber layer defects between a low-teen intraocular pressure group and a high-teen intraocular pressure group in normal-tension glaucoma patients. J Glaucoma. 2007; 16: 293-296.
33. Langlotz CP. Fundamental measures of diagnostic examination performance: usefulness for clinical decision making and research. Radiology. 2003; 228: 3-9.
34. Medeiros FA, Zangwill LM, Bowd C, Weinreb RN. Comparison of the GDx VCC scanning laser polarimeter, HRT II confocal scanning laser ophthalmoscope, and stratus OCT optical coherence tomograph for the detection of glaucoma. Arch Ophthalmol. 2004; 122: 827-837.
35. Medeiros FA, Ng D, Zangwill LM, Sample PA, Bowd C, Weinreb RN. The effects of study design and spectrum bias on the evaluation of diagnostic accuracy of confocal scanning laser ophthalmoscopy in glaucoma. Invest Ophthalmol Vis Sci. 2007; 48: 214-222.
36. Medeiros FA, Zangwill LM, Bowd C, Sample PA, Weinreb RN. Use of progressive glaucomatous optic disk change as the reference standard for evaluation of diagnostic tests in glaucoma. Am J Ophthalmol. 2005; 139: 1010-1018.
37. Medeiros FA, Lisboa R, Zangwill LM, et al. Evaluation of progressive neuroretinal rim loss as a surrogate end point for development of visual field loss in glaucoma. Ophthalmology. 2014; 121: 100-109.
38. Savitz DA, Olshan AF. Multiple comparisons and related issues in the interpretation of epidemiologic data. Am J Epidemiol. 1995; 142: 904-908.
39. Sankoh AJ, Huque MF, Dubey SD. Some comments on frequently used multiple endpoint adjustment methods in clinical trials. Stat Med. 1997; 16: 2529-2542.