Background/Aims To assess the diagnostic performance of retinal nerve fibre layer (RNFL) data of optical coherence tomography (OCT) for detecting glaucoma.
Methods Secondary analyses of a prospective, multicentre diagnostic study (Glaucoma Automated Tests Evaluation (GATE)) referred to hospital eye services in the UK were conducted. We included data from 899 of 966 participants referred to hospital eye services with suspected glaucoma or ocular hypertension. We used both eyes’ data and logistic regression-based receiver operator characteristics analysis to build a set of models to measure the sensitivity and specificity of the average and inferior quadrant RNFL thickness data of OCT. The reference standard was expert clinician examination including automated perimetry. The main outcome measures were sensitivity at 0.95 specificity and specificity at 0.95 sensitivity and the corresponding RNFL thickness thresholds. We explored the possibility of accuracy improvement by adding measures of within-eye and between-eye variation, scan quality, intraocular pressure (IOP) and age.
Results Glaucoma was diagnosed in at least one eye in 17% of participants. Areas under the curve were between 0.83 and 0.88. When specificity was fixed at 0.95, the sensitivity was between 0.38 and 0.55, and the highest values were reached with models including the inferior quadrant rather than the average RNFL thickness. Fixing sensitivity at 0.95, the specificity was between 0.36 and 0.58. The addition of age, refractive error, IOP or within-subject variation did not improve the accuracy.
Conclusion RNFL thickness data of OCT can be used as a diagnostic test, but accuracy estimates remain moderate even in exploratory multivariable modelling of aiming to improve accuracy.
- diagnostic tests/investigation
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.