Combining Nerve Fiber Layer Parameters to Optimize Glaucoma Diagnosis with Optical Coherence Tomography

Ake Tzu Hui Lu, Mingwu Wang, Rohit Varma, Joel S. Schuman, David S. Greenfield, Scott D. Smith, David Huang

Research output: Contribution to journalArticlepeer-review


Purpose: To identify the best combination of Stratus optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness parameters for the detection of glaucoma. Design: Observational cross-sectional study. Participants: Eighty-nine age-matched normal and perimetric glaucoma participants enrolled in the Advanced Imaging for Glaucoma Study. Methods: The Zeiss Stratus OCT system was used to obtain the circumpapillary RNFL thickness in both eyes of each participant. Right and left eye clock-hour data are analyzed together, assuming mirror-image symmetry. The RNFL diagnostic parameters were combined using either or-logic or and-logic approaches. Main Outcome Measures: Area under the receiver operating characteristic curve (AROC), sensitivity, and specificity are used to evaluate diagnostic performance. Results: Overall average RNFL thickness has the highest AROC value (0.89) of all single parameters evaluated, followed by the inferior and superior quadrants (0.88 and 0.86, respectively). The clock hours with the best AROC values are in the inferior and superior quadrants. The highest AROC (0.92) was achieved by the or-logic combination of overall, inferior, and superior quadrant RNFL thicknesses. The 3-parameter combination was significantly better than the overall average alone (P = 0.01). The addition of more quadrants or clock hours to the combination reduced diagnostic performance. Conclusions: The best stand-alone diagnostic strategy for Stratus OCT RNFL data is to classify an eye as glaucomatous if the overall, inferior quadrant, or superior quadrant RNFL thickness average is below normal.

Original languageEnglish (US)
Pages (from-to)1352-1357.e2
Issue number8
StatePublished - Aug 2008

ASJC Scopus subject areas

  • Ophthalmology


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