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Meta-Analysis
. 2015 Nov 30;2015(11):CD008803.
doi: 10.1002/14651858.CD008803.pub2.

Optic nerve head and fibre layer imaging for diagnosing glaucoma

Manuele Michelessi  1 , Ersilia Lucenteforte, Francesco Oddone, Miriam Brazzelli, Mariacristina Parravano, Sara Franchi, Sueko M Ng, Gianni Virgili
Affiliations
Meta-Analysis

Optic nerve head and fibre layer imaging for diagnosing glaucoma

Manuele Michelessi et al. Cochrane Database Syst Rev. .

Abstract

Background: The diagnosis of glaucoma is traditionally based on the finding of optic nerve head (ONH) damage assessed subjectively by ophthalmoscopy or photography or by corresponding damage to the visual field assessed by automated perimetry, or both. Diagnostic assessments are usually required when ophthalmologists or primary eye care professionals find elevated intraocular pressure (IOP) or a suspect appearance of the ONH. Imaging tests such as confocal scanning laser ophthalmoscopy (HRT), optical coherence tomography (OCT) and scanning laser polarimetry (SLP, as used by the GDx instrument), provide an objective measure of the structural changes of retinal nerve fibre layer (RNFL) thickness and ONH parameters occurring in glaucoma.

Objectives: To determine the diagnostic accuracy of HRT, OCT and GDx for diagnosing manifest glaucoma by detecting ONH and RNFL damage.

Search methods: We searched several databases for this review. The most recent searches were on 19 February 2015.

Selection criteria: We included prospective and retrospective cohort studies and case-control studies that evaluated the accuracy of OCT, HRT or the GDx for diagnosing glaucoma. We excluded population-based screening studies, since we planned to consider studies on self-referred people or participants in whom a risk factor for glaucoma had already been identified in primary care, such as elevated IOP or a family history of glaucoma. We only considered recent commercial versions of the tests: spectral domain OCT, HRT III and GDx VCC or ECC.

Data collection and analysis: We adopted standard Cochrane methods. We fitted a hierarchical summary ROC (HSROC) model using the METADAS macro in SAS software. After studies were selected, we decided to use 2 x 2 data at 0.95 specificity or closer in meta-analyses, since this was the most commonly-reported level.

Main results: We included 106 studies in this review, which analysed 16,260 eyes (8353 cases, 7907 controls) in total. Forty studies (5574 participants) assessed GDx, 18 studies (3550 participants) HRT, and 63 (9390 participants) OCT, with 12 of these studies comparing two or three tests. Regarding study quality, a case-control design in 103 studies raised concerns as it can overestimate accuracy and reduce the applicability of the results to daily practice. Twenty-four studies were sponsored by the manufacturer, and in 15 the potential conflict of interest was unclear.Comparisons made within each test were more reliable than those between tests, as they were mostly based on direct comparisons within each study.The Nerve Fibre Indicator yielded the highest accuracy (estimate, 95% confidence interval (CI)) among GDx parameters (sensitivity: 0.67, 0.55 to 0.77; specificity: 0.94, 0.92 to 0.95). For HRT measures, the Vertical Cup/Disc (C/D) ratio (sensitivity: 0.72, 0.60 to 0.68; specificity: 0.94, 0.92 to 0.95) was no different from other parameters. With OCT, the accuracy of average RNFL retinal thickness was similar to the inferior sector (0.72, 0.65 to 0.77; specificity: 0.93, 0.92 to 0.95) and, in different studies, to the vertical C/D ratio.Comparing the parameters with the highest diagnostic odds ratio (DOR) for each device in a single HSROC model, the performance of GDx, HRT and OCT was remarkably similar. At a sensitivity of 0.70 and a high specificity close to 0.95 as in most of these studies, in 1000 people referred by primary eye care, of whom 200 have manifest glaucoma, such as in those who have already undergone some functional or anatomic testing by optometrists, the best measures of GDx, HRT and OCT would miss about 60 cases out of the 200 patients with glaucoma, and would incorrectly refer 50 out of 800 patients without glaucoma. If prevalence were 5%, e.g. such as in people referred only because of family history of glaucoma, the corresponding figures would be 15 patients missed out of 50 with manifest glaucoma, avoiding referral of about 890 out of 950 non-glaucomatous people.Heterogeneity investigations found that sensitivity estimate was higher for studies with more severe glaucoma, expressed as worse average mean deviation (MD): 0.79 (0.74 to 0.83) for MD < -6 db versus 0.64 (0.60 to 0.69) for MD ≥ -6 db, at a similar summary specificity (0.93, 95% CI 0.92 to 0.94 and, respectively, 0.94; 95% CI 0.93 to 0.95; P < 0.0001 for the difference in relative DOR).

Authors' conclusions: The accuracy of imaging tests for detecting manifest glaucoma was variable across studies, but overall similar for different devices. Accuracy may have been overestimated due to the case-control design, which is a serious limitation of the current evidence base.We recommend that further diagnostic accuracy studies are carried out on patients selected consecutively at a defined step of the clinical pathway, providing a description of risk factors leading to referral and bearing in mind the consequences of false positives and false negatives in the setting in which the diagnostic question is made. Future research should report accuracy for each threshold of these continuous measures, or publish raw data.

PubMed Disclaimer

Conflict of interest statement

Manuele Michelessi: none known Ersilia Lucenteforte: none known Francesco Oddone. none known Miriam Brazzelli: none known Mariacristina Parravano: none known Sara Franchi: none known Sueko M Ng: none known Gianni Virgili: none known

Figures

1
1
Flow diagram.
2
2
Risk of bias and applicability concerns graph: review authors' judgements about each domain presented as percentages across included studies
3
3
Summary ROC Plot of tests with data extracted at the highest specificity in case of multiple study measures for the same parameter: 2 GDx: NFI, 4 GDx: TSNIT average, 5 OCT: mean RNFL thickness, 6 OCT: RNFL at inferior quadrant, 13 HRT: vertical C‐D ratio, 17 HRT: MRA, 39 OCT: ONH C/D area ratio, 41 OCT: ONH C/D vertical ratio.
4
4
Summary ROC Plot of tests: 47 Direct comparison: GDx NFI, 48 Direct comparison: OCT RNFL average.
1
1. Test
GDx: Inferior average
2
2. Test
GDx: NFI
3
3. Test
GDx: Superior average
4
4. Test
GDx: TSNIT average
5
5. Test
OCT: RNFL average
6
6. Test
OCT: RNFL inferior quadrant
7
7. Test
OCT: RNFL nasal quadrant
8
8. Test
OCT: RNFL superior quadrant
9
9. Test
OCT: RNFL temporal quadrant
10
10. Test
HRT: Bathija function
11
11. Test
HRT: Cup area
12
12. Test
HRT: C/D area ratio
13
13. Test
HRT: vertical C/D ratio
14
14. Test
HRT: Cup shape measure
15
15. Test
HRT: Cup volume
16
16. Test
HRT: FSM discriminant function o Mikelberg function
17
17. Test
HRT: MRA
18
18. Test
HRT: Rim area
19
19. Test
HRT: RB discriminant function
20
20. Test
HRT: Rim Volume
21
21. Test
OCT: GCC RTVue average thickness
22
22. Test
OCT: GCC RTVue superior thickness
23
23. Test
OCT: GCC RTVue inferior thickness
24
24. Test
OCT: GCC RTVue FLV
25
25. Test
OCT: GCC RTVue GLV
26
26. Test
OCT: GCC 3DTopcon average thickness
27
27. Test
OCT: GCC 3DTopcon superior thickness
28
28. Test
OCT: GCC 3DTopcon inferior thickness
29
29. Test
OCT: GCIPL Cirrus average thickness
30
30. Test
OCT: GCIPL Cirrus minimum thickness
31
31. Test
OCT: GCIPL Cirrus superior thickness
32
32. Test
OCT: GCIPL Cirrus inferior thickness
33
33. Test
OCT: ONH Disc area
34
34. Test
OCT: ONH Cup area
35
35. Test
OCT: ONH Rim area
36
36. Test
OCT: ONH Rim volume
37
37. Test
OCT: ONH Nerve head volume
38
38. Test
OCT: ONH Cup volume
39
39. Test
OCT: ONH C/D area ratio
40
40. Test
OCT: ONH horizontal C/D ratio
41
41. Test
OCT: ONH vertical C/D ratio
42
42. Test
OCT: GCIPL Cirrus Inferonasal quadrant
43
43. Test
OCT: GCIPL Cirrus Inferotemporal quadrant
44
44. Test
OCT: GCIPL Cirrus Superonasal quadrant
45
45. Test
OCT: GCIPL Cirrus Superotemporal quadrant
46
46. Test
OCT: GCC Spectralis average thickness
47
47. Test
Direct comparison: GDx NFI
48
48. Test
Direct comparison: OCT RNFL average
See this image and copyright information in PMC

Update of

  • doi: 10.1002/14651858.CD008803

References

References to studies included in this review

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Badala 2007 {published data only}
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Barella 2013 {published data only}
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Bertuzzi 2014 {published data only}
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Chen 2007 {published data only}
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Chen 2013 {published data only}
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Cho 2011 {published data only}
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Choi 2013 {published data only}
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Da Pozzo 2005 {published data only}
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Da Pozzo 2006 {published data only}
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De Leon‐Ortega 2006 {published data only}
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De Leon‐Ortega 2007 {published data only}
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Essock 2005 {published data only}
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Fang 2010 {published data only}
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Ferreras 2007 {published data only}
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Ferreras 2008a {published data only}
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Ferreras 2008b {published data only}
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Garas 2011 {published data only}
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Garas 2012 {published data only}
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Garudadri 2012 {published data only}
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Gonzales de la Rosa 2013 {published data only}
    1. Gonzalez de la Rosa M, Gonzalez-Hernandez M, Sanchez-Garcia M, Rodriguez de la Vega R, Diaz-Aleman T, Pareja Rios A. Oculus-Spark perimetry compared with 3 procedures of glaucoma morphologic analysis (GDx, HRT, and OCT). European Journal of Ophthalmology 2013;23(3):316-23. - PubMed
Harizman 2006 {published data only}
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Hoesl 2013 {published data only}
    1. Hoesl LM, Tornow RP, Schrems WA, Horn FK, Mardin CY, Kruse FE, et al. Glaucoma diagnostic performance of GDxVCC and spectralis OCT on eyes with atypical retardation pattern. Journal of Glaucoma 2013;22(4):317-24. - PubMed
Hong 2007 {published data only}
    1. Hong S, Ahn H, Ha SJ, Yeom HY, Seong GJ, Hong YJ. Early glaucoma detection using the Humphrey Matrix Perimeter, GDx VCC, Stratus OCT, and retinal nerve fiber layer photography. Ophthalmology 2007;114(2):210-5. - PubMed
Huang 2010 {published data only}
    1. Huang ML, Chen HY, Huang WC, Tsai YY. Linear discriminant analysis and artificial neural network for glaucoma diagnosis using scanning laser polarimetry-variable cornea compensation measurements in Taiwan Chinese population. Graefe's Archive for Clinical and Experimental Ophthalmology 2010;248(3):435-41. - PubMed
Huang 2011 {published data only}
    1. Huang JY, Pekmezci M, Mesiwala N, Kao A, Lin S. Diagnostic power of optic disc morphology, peripapillary retinal nerve fiber layer thickness, and macular inner retinal layer thickness in glaucoma diagnosis with fourier-domain optical coherence tomography. Journal of Glaucoma 2011;20(2):87-94. - PubMed
Hwang 2012 {published data only}
    1. Hwang YH, Kim YY. Glaucoma diagnostic ability of quadrant and clock-hour neuroretinal rim assessment using cirrus HD optical coherence tomography. Investigative Ophthalmology and Visual Science 2012;53(4):2226-34. - PubMed
Iester 2008 {published data only}
    1. Iester M, Perdicchi A, Capris E, Siniscalco A, Calabria G, Recupero SM. Comparison between discriminant analysis models and "glaucoma probability score" for the detection of glaucomatous optic nerve head changes. Journal of Glaucoma 2008;17(7):535-40. - PubMed
Jeoung 2010 {published data only}
    1. Jeoung JW, Park KH. Comparison of Cirrus OCT and Stratus OCT on the ability to detect localized retinal nerve fiber layer defects in preperimetric glaucoma. Investigative Ophthalmology and Visual Science 2010;51(2):938-45. - PubMed
Jeoung 2013 {published data only}
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Jindal 2010 {published data only}
    1. Jindal S, Dada T, Sreenivas V, Gupta V, Sihota R, Panda A. Comparison of the diagnostic ability of Moorfield's regression analysis and glaucoma probability score using Heidelberg retinal tomograph III in eyes with primary open angle glaucoma. Indian Journal Ophthalmology 2010;58(6):487-92. - PMC - PubMed
Kanamori 2006 {published data only}
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Kang 2012 {published data only}
    1. Kang SY, Sung KR, Na JH, Choi EH, Cho JW, Cheon MH, et al. Comparison between deviation map algorithm and peripapillary retinal nerve fiber layer measurements using Cirrus HD-OCT in the detection of localized glaucomatous visual field defects. Journal of Glaucoma 2012;21(6):372-8. - PubMed
Kim 2011 {published data only}
    1. Kim NR, Lee ES, Seong GJ, Kang SY, Kim JH, Hong S, et al. Comparing the ganglion cell complex and retinal nerve fibre layer measurements by Fourier domain OCT to detect glaucoma in high myopia. British Journal of Ophthalmology 2011;95(8):1115-21. - PubMed
Kim 2013a {published data only}
    1. Kim NR, Hong S, Kim JH, Rho SS, Seong GJ, Kim CJ. Comparison of macular ganglion cell complex thickness by Fourier-domain OCT in normal tension glaucoma and primary open-angle glaucoma. Journal of Glaucoma 2013;22(2):133-9. - PubMed
Kim 2013b {published data only}
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Kim 2014a {published data only}
    1. 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. Investigative Ophthalmology & Visual Science 2014;55(4):2079-87. - PubMed
Kim 2014b {published data only}
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Kita 2013 {published data only}
    1. Kita Y, Kita R, Takeyama A, Takagi S, Nishimura C, Tomita G. Ability of optical coherence tomography-determined ganglion cell complex thickness to total retinal thickness ratio to diagnose glaucoma. Journal of Glaucoma 2013;22(9):757-62. - PubMed
Koh 2014 {published data only}
    1. Koh KM, Jin S, Hwang YH. Cirrus high-definition optical coherence tomography versus spectral optical coherence tomography/scanning laser ophthalmoscopy in the diagnosis of glaucoma. Current Eye Research 2014;39(1):62-8. - PubMed
Kook 2005 {published data only}
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Kotowski 2012 {published data only}
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Kratz 2014 {published data only}
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Lee 2010 {published data only}
    1. Lee S, Sung KR, Cho JW, Cheon MH, Kang SY, Kook MS. Spectral-domain optical coherence tomography and scanning laser polarimetry in glaucoma diagnosis. Japanese Journal of Ophthalmology 2010;54(6):544-9. - PubMed
Leite 2011 {published data only}
    1. Leite MT, Rao HL, Zangwill LM, Weinreb RN, Medeiros FA. Comparison of the diagnostic accuracies of the Spectralis, Cirrus, and RTVue optical coherence tomography devices in glaucoma. Ophthalmology 2011;118(7):1334-9. - PMC - PubMed
Leung 2010 {published data only}
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Lisboa 2013 {published data only}
    1. Lisboa R, Paranhos A Jr, Weinreb RN, Zangwill LM, Leite MT, Medeiros FA. Comparison of different spectral domain OCT scanning protocols for diagnosing preperimetric glaucoma. Investigative Ophthalmology and Visual Science 2013;54(5):3417-25. - PMC - PubMed
Mai 2007 {published data only}
    1. Mai TA, Reus NJ, Lemij HG. Diagnostic accuracy of scanning laser polarimetry with enhanced versus variable corneal compensation. Ophthalmology 2007;114(11):1988-93. - PubMed
Mansoori 2011 {published data only}
    1. Mansoori T, Viswanath K, Balakrishna N. Ability of spectral domain optical coherence tomography peripapillary retinal nerve fiber layer thickness measurements to identify early glaucoma. Indian Journal Ophthalmology 2011;59(6):455-9. - PMC - PubMed
Medeiros 2004a {published data only}
    1. Medeiros FA, Zangwill LM, Bowd C, Mohammadi K, Weinreb RN. Comparison of scanning laser polarimetry using variable corneal compensation and retinal nerve fiber layer photography for detection of glaucoma. Archives of Ophthalmology 2004;122(5):698-704. - PubMed
Medeiros 2004b {published data only}
    1. 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. Archives of Ophthalmology 2004;122(6):827-37. - PubMed
Medeiros 2005 {published data only}
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Moreno 2011 {published data only}
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Moreno‐Montañés 2008 {published data only}
    1. Moreno-Montañés J, Antón A, García N, Mendiluce L, Ayala E, Sebastián A. Glaucoma probability score vs Moorfields classification in normal, ocular hypertensive, and glaucomatous eyes. American Journal of Ophthalmology 2008;145(2):360-8. - PubMed
Moreno‐Montañés 2010 {published data only}
    1. Moreno-Montañés J, Olmo N, Alvarez A, García N, Zarranz-Ventura J. Cirrus high-definition optical coherence tomography compared with Stratus optical coherence tomography in glaucoma diagnosis. Investigative Ophthalmology and Visual Science 2010;51(1):335-43. - PubMed
Mwanza 2012 {published data only}
    1. Mwanza JC, Durbin MK, Budenz DL, Sayyad FE, Chang RT, Neelakantan A, et al. Glaucoma diagnostic accuracy of ganglion cell-inner plexiform layer thickness: comparison with nerve fiber layer and optic nerve head. Ophthalmology 2012;119(6):1151-8. - PubMed
Mwanza 2013 {published data only}
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Mwanza 2014 {published data only}
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Sung 2013 {published data only}
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Weinreb 2003 {published data only}
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Wu 2012 {published data only}
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Yamada 2014 {published data only}
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Yoshida 2014 {published data only}
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Zelefsky 2006 {published data only}
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Zeppieri 2010 {published data only}
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References to other published versions of this review

Oddone 2010
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Publication types