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Likelihood ratios for glaucoma diagnosis using spectral-domain optical coherence tomography.
- Author(s): Lisboa, Renato;
- Mansouri, Kaweh;
- Zangwill, Linda M;
- Weinreb, Robert N;
- Medeiros, Felipe A
- et al.
Published Web Locationhttps://pubmed.ncbi.nlm.nih.gov/23972303/
No data is associated with this publication.
PurposeTo present a methodology for calculating likelihood ratios for glaucoma diagnosis for continuous retinal nerve fiber layer (RNFL) thickness measurements from spectral-domain optical coherence tomography (spectral-domain OCT).
DesignObservational cohort study.
MethodsA total of 262 eyes of 187 patients with glaucoma and 190 eyes of 100 control subjects were included in the study. Subjects were recruited from the Diagnostic Innovations Glaucoma Study. Eyes with preperimetric and perimetric glaucomatous damage were included in the glaucoma group. The control group was composed of healthy eyes with normal visual fields from subjects recruited from the general population. All eyes underwent RNFL imaging with Spectralis spectral-domain OCT. Likelihood ratios for glaucoma diagnosis were estimated for specific global RNFL thickness measurements using a methodology based on estimating the tangents to the receiver operating characteristic (ROC) curve.
ResultsLikelihood ratios could be determined for continuous values of average RNFL thickness. Average RNFL thickness values lower than 86 μm were associated with positive likelihood ratios (ie, likelihood ratios greater than 1), whereas RNFL thickness values higher than 86 μm were associated with negative likelihood ratios (ie, likelihood ratios smaller than 1). A modified Fagan nomogram was provided to assist calculation of posttest probability of disease from the calculated likelihood ratios and pretest probability of disease.
ConclusionThe methodology allowed calculation of likelihood ratios for specific RNFL thickness values. By avoiding arbitrary categorization of test results, it potentially allows for an improved integration of test results into diagnostic clinical decision making.
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