Strouthidis, NG; (2007) Measuring progression in glaucoma. Doctoral thesis , UCL (University College London).

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Abstract

Background: Primary open angle glaucoma is characterised by progressive optic neuropathy associated with characteristic visual field loss. The ability to measure disease progression is of vital importance in the management of patients with glaucoma. Conventionally, disease progression has been monitored using static automated perimetry. Recently, devices which image the optic nerve head quantifiably have been introduced. This thesis sets out to compare structural and functional progression in ocular hypertensive subjects followed longitudinally using novel progression algorithms. Plan of research: The investigations may be considered in three parts. Firstly, the factors affecting the test-retest variability of the Heidelberg Retina Tomograph (HRT) are identified and methods to improve repeatability are investigated. Secondly, novel HRT trend and event analyses, based on the findings of the test-retest studies, are compared with established field progression techniques in ocular hypertensive and control subjects. Thirdly, a previously described novel spatial filter is assessed in terms of its impact on the monitoring of visual field progression and in terms of its agreement with a previously described 'structural' map. Results: Rim area was identified as the most repeatable HRT parameter its variability can be improved by using the 320pm reference plane and by using only good quality images. Agreement as regards structural and functional progression was poor, regardless of the estimated specificities of the algorithms used or technique adopted. The novel spatial filter appeared to confer some advantage in terms of specificity, comparable to the effect of confirmatory testing. The functional relationship between test-points, characterised by the filter, correlated well with the expected structural pattern. Clinical significance: The poor agreement suggests that the monitoring of both structure and function is essential to provide the best chance of detecting progression at all stages of the disease. Spatial filtering techniques may provide some additional benefit in the monitoring of progression, particularly once structural data are incorporated.

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