May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
Likelihood Ratios for Glaucoma Diagnosis Using Scanning Laser Polarimetry
Author Affiliations & Notes
  • F. A. Medeiros
    Ophthalmology, Univ of California-San Diego, La Jolla, California
  • L. M. Zangwill
    Ophthalmology, Univ of California-San Diego, La Jolla, California
  • D. Ng
    Ophthalmology, Univ of California-San Diego, La Jolla, California
  • G. Vizzeri
    Ophthalmology, Univ of California-San Diego, La Jolla, California
  • R. N. Weinreb
    Ophthalmology, Univ of California-San Diego, La Jolla, California
  • Footnotes
    Commercial Relationships  F.A. Medeiros, Carl-Zeiss Meditec, Heidelberg Engineering, R; Carl-Zeiss Meditec, F; L.M. Zangwill, Carl-Zeiss Meditec, Heidelberg Engineering, F; D. Ng, None; G. Vizzeri, None; R.N. Weinreb, Heidelberg Engineering; Carl-Zeiss Meditec, F; Carl-Zeiss Meditec, C.
  • Footnotes
    Support  NIH EY01008 (LMZ). Participant incentive grants in the form of glaucoma medications at no cost from Alcon Laboratories, Inc., Allergan, Pfizer, Inc., and SANTEN Inc.
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 5432. doi:https://doi.org/
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    • Get Citation

      F. A. Medeiros, L. M. Zangwill, D. Ng, G. Vizzeri, R. N. Weinreb; Likelihood Ratios for Glaucoma Diagnosis Using Scanning Laser Polarimetry. Invest. Ophthalmol. Vis. Sci. 2008;49(13):5432. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract
 
Purpose:
 

Evidence-Based medicine guidelines have suggested that likelihood ratios (LR) are the best way to judge by how much a test result can help in making the diagnosis of a disease. The purpose of this study was to provide likelihood ratio values for retinal nerve fiber layer analysis using scanning laser polarimetry for diagnosing glaucoma in patients suspected of having the disease.

 
Methods:
 

The study included a cohort of 82 patients suspected of having glaucoma based on the appearance of the optic nerve. All patients were imaged using the GDx VCC scanning laser polarimeter and had normal standard automated perimetry visual fields at the time of imaging. Patients were classified based on history of documented stereophotographic evidence of progressive glaucomatous change in the appearance of the optic nerve occurring before the imaging sessions. Likelihood ratios were obtained for single values of GDx VCC parameters according to a logistic regression model.

 
Results:
 

Forty eyes with progressive glaucomatous optic nerve change were included in the glaucoma group and 42 eyes without any evidence of progressive damage to the optic nerve followed untreated for an average time of 8.97 ± 3.08 years were included in the normal group. From comparison of areas under the receiver operating characteristic curves, the parameter Nerve Fiber Indicator had the best diagnostic accuracy. LRs for single values of the NFI were calculated by the formula LR(NFI) = exp (-4.11 + 0.154*NFI). The Table shows results of LRs for arbitrary values of NFI.  

 
Conclusions:
 

The proposed method is an efficient way of calculating likelihood ratios for single values of a test parameter and may be more clinically useful than applying categorization strategies. LRs for the NFI parameter indicate that the finding of certain values of this parameter when evaluating a patient suspected of having glaucoma with the GDx VCC can provide significant changes in the post-test probability of disease.

 
Keywords: imaging/image analysis: clinical • nerve fiber layer • optic nerve 
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