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Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma.
Am J Ophthalmol. 2006 Jan; 141(1):105-115.AJ

Abstract

PURPOSE

To compare the performance of the retinal nerve fiber layer (RNFL) thickness and optic disk algorithms as determined by optical coherence tomography to detect glaucoma.

DESIGN

Observational cross-sectional study.

METHODS

setting: Academic tertiary-care center. study population: One eye from 42 control subjects and 65 patients with open-angle glaucoma with visual acuity of > or =20/40, and no other ocular pathologic condition. observation procedures: Two optical coherence tomography algorithms were used: "fast RNFL thickness" and "fast optic disk." main outcome measures: Area under the receiver operating characteristic curves and sensitivities at fixed specificities were used. Discriminating ability of the average RNFL thickness and RNFL thickness in clock-hour sectors and quadrants was compared with the parameters that were derived from the fast optic disk algorithm. Classification and regression trees were used to determine the best combination of parameters for the detection of glaucoma.

RESULTS

The average visual field mean deviation (+/-SD) was 0.0 +/- 1.3 and -5.3 +/- 5.0 dB in the control and glaucoma groups, respectively. The RNFL thickness at the 7 o'clock sector, inferior quadrant, and the vertical C/D ratio had the highest area under the receiver operating characteristic curves (0.93 +/- 0.02, 0.92 +/- 0.03, and 0.90 +/- 0.03, respectively). At 90% specificity, the best sensitivities (+/-SE) from each algorithm were 86% +/- 3% for RNFL thickness at the 7 o'clock sector and 79% +/- 4% for horizontal integrated rim width (estimated rim area). The combination of inferior quadrant RNFL thickness and vertical C/D ratio achieved the best classification (misclassification rate, 6.2%).

CONCLUSION

The fast optic disk algorithm performs as well as the fast RNFL thickness algorithm for discrimination of glaucoma from normal eyes. A combination of the two algorithms may provide enhanced diagnostic performance.

Authors+Show Affiliations

Glaucoma Division, Jules Stein Eye Institute, University of California-Los Angeles, 100 Stein Plaza, Los Angeles, CA 90095, USA.No affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

16386983

Citation

Manassakorn, Anita, et al. "Comparison of Retinal Nerve Fiber Layer Thickness and Optic Disk Algorithms With Optical Coherence Tomography to Detect Glaucoma." American Journal of Ophthalmology, vol. 141, no. 1, 2006, pp. 105-115.
Manassakorn A, Nouri-Mahdavi K, Caprioli J. Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma. Am J Ophthalmol. 2006;141(1):105-115.
Manassakorn, A., Nouri-Mahdavi, K., & Caprioli, J. (2006). Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma. American Journal of Ophthalmology, 141(1), 105-115.
Manassakorn A, Nouri-Mahdavi K, Caprioli J. Comparison of Retinal Nerve Fiber Layer Thickness and Optic Disk Algorithms With Optical Coherence Tomography to Detect Glaucoma. Am J Ophthalmol. 2006;141(1):105-115. PubMed PMID: 16386983.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Comparison of retinal nerve fiber layer thickness and optic disk algorithms with optical coherence tomography to detect glaucoma. AU - Manassakorn,Anita, AU - Nouri-Mahdavi,Kouros, AU - Caprioli,Joseph, PY - 2005/05/31/received PY - 2005/08/05/revised PY - 2005/08/05/accepted PY - 2006/1/3/pubmed PY - 2006/2/1/medline PY - 2006/1/3/entrez SP - 105 EP - 115 JF - American journal of ophthalmology JO - Am J Ophthalmol VL - 141 IS - 1 N2 - PURPOSE: To compare the performance of the retinal nerve fiber layer (RNFL) thickness and optic disk algorithms as determined by optical coherence tomography to detect glaucoma. DESIGN: Observational cross-sectional study. METHODS: setting: Academic tertiary-care center. study population: One eye from 42 control subjects and 65 patients with open-angle glaucoma with visual acuity of > or =20/40, and no other ocular pathologic condition. observation procedures: Two optical coherence tomography algorithms were used: "fast RNFL thickness" and "fast optic disk." main outcome measures: Area under the receiver operating characteristic curves and sensitivities at fixed specificities were used. Discriminating ability of the average RNFL thickness and RNFL thickness in clock-hour sectors and quadrants was compared with the parameters that were derived from the fast optic disk algorithm. Classification and regression trees were used to determine the best combination of parameters for the detection of glaucoma. RESULTS: The average visual field mean deviation (+/-SD) was 0.0 +/- 1.3 and -5.3 +/- 5.0 dB in the control and glaucoma groups, respectively. The RNFL thickness at the 7 o'clock sector, inferior quadrant, and the vertical C/D ratio had the highest area under the receiver operating characteristic curves (0.93 +/- 0.02, 0.92 +/- 0.03, and 0.90 +/- 0.03, respectively). At 90% specificity, the best sensitivities (+/-SE) from each algorithm were 86% +/- 3% for RNFL thickness at the 7 o'clock sector and 79% +/- 4% for horizontal integrated rim width (estimated rim area). The combination of inferior quadrant RNFL thickness and vertical C/D ratio achieved the best classification (misclassification rate, 6.2%). CONCLUSION: The fast optic disk algorithm performs as well as the fast RNFL thickness algorithm for discrimination of glaucoma from normal eyes. A combination of the two algorithms may provide enhanced diagnostic performance. SN - 0002-9394 UR - https://www.unboundmedicine.com/medline/citation/16386983/Comparison_of_retinal_nerve_fiber_layer_thickness_and_optic_disk_algorithms_with_optical_coherence_tomography_to_detect_glaucoma_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0002-9394(05)00906-2 DB - PRIME DP - Unbound Medicine ER -