The predictive ability of clinical tests for contact lens induced dry eye

Abstract

Approximately half of current contact lens wearers suffer from dryness and discomfort, particularly towards the end of the day. Contact lens practitioners have a number of dry eye tests available to help them to predict which of their patients may be at risk of contact lens drop out and advise them accordingly. This thesis set out to rationalize them to see if any are of more diagnostic significance than others. This doctorate has found: (1) The Keratograph, a device which permits an automated, examiner independent
technique for measuring non invasive tear break up time (NITBUT) measured NITBUT consistently shorter than measurements recorded with the Tearscope. When measuring central corneal curvature the spherical equivalent power of the
cornea was measured as being significantly flatter than with a validated automated keratometer.
(2) Non-invasive and invasive tear break-up times significantly correlated to each other, but not the other tear metrics. Symptomology, assessed using the OSDI questionnaire, correlated more with those tests indicating possible damage to the ocular surface (including LWE, LIPCOF and conjunctival staining) than with tests of either tear volume or stability. Cluster analysis showed some statistically significant groups of patients with different sign and symptom profiles. The largest cluster demonstrated poor tear quality with both non-invasive and invasive tests, low tear volume and more symptoms. (3) Care should be taken in fitting patients new to contact lenses if they have a
NITBUT less than 10s or an OSDI comfort rating greater than 4.2 as they are more likely to drop-out within the first 6 months. Cluster analysis was not found to be
beneficial in predicting which patients will succeed with lenses and which will not. A combination of the OSDI questionnaire and a NITBUT measurement was most useful both in diagnosing dry eye and in predicting contact lens drop out.

Date of Award	2013
Original language	English
Supervisor	James Wolffsohn (Supervisor)