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G. Wollstein, Y. Pan, R.A. Bilonick, H. Ishikawa, L. Kagemann, J.S. Schuman; Glaucoma Probability Based on OCT Classification Data . Invest. Ophthalmol. Vis. Sci. 2006;47(13):3349.
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© ARVO (1962-2015); The Authors (2016-present)
Optical coherence tomography (OCT) measurements are automatically compared to a normative dataset and classified as within normal limits (WNL), borderline (BL) or outside normal limits (ONL). These classifications are given for numerous OCT parameters; however, the optimal use of the complete set of OCT classifications to detect glaucoma remains unclear. The purpose of this study was to determine the glaucoma probability using OCT classification data.
65 healthy subjects (65 eyes) and 57 glaucoma patients (57 eyes) with moderate glaucoma damage were recruited. All subjects had a visual field examination and OCT assessment of the macula and circumpapillary region. Glaucoma probability was computed by logistic regression models using demographic (age, gender, race) and all macular (9 sectors) and circumpapillary (global mean nerve fiber layer (NFL) thickness, quadrants and clock hours) OCT classification data. Area under receiver operating characteristics (AROC) was used to assess the performance of the models in distinguishing between healthy and glaucomatous eyes. Odds ratio (OR) were computed for each parameter in the models.
The mean visual field mean deviation (MD) of the healthy group was 0.18±1.34 dB and in the glaucoma group –7.99±6.23 dB (p<0.001, Student t–test). The model with the highest AROC included the number of sectors with BL and/or ONL NFL classification and age (AROC=0.994). An increase of one sector in the model increased the odds of a diagnosis of glaucoma by five fold (OR=5.01). An increase in age by 1 year increased the odds of glaucoma by 1.15. Gender and race were not statistically significant contributors in the models. The addition of macular data in the models diminished the discriminatory performance as assessed by AROC. A model that included the nominal values of mean NFL and age had an AROC=0.992. The difference between the AROCs was not significant.
OCT NFL classification data had high discrimination performance between healthy and moderately damaged glaucomatous eyes. Similar discrimination capability was noted using the mean NFL thickness measurements.
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