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Cornea. 2013 Dec;32(12):1549-53. doi: 10.1097/ICO.0b013e3182a7f3e1.

Evaluation of lipid layer thickness measurement of the tear film as a diagnostic tool for Meibomian gland dysfunction.

Author information

1
Department of Ophthalmology, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.

Abstract

PURPOSE:

The LipiView interferometer (TearScience Inc, Morrisville, NC) is capable of delivering quantitative values of the tear-film lipid layer thickness (LLT). The purpose of this study was to investigate whether there is a correlation between the LLT and other diagnostic criteria for Meibomian gland dysfunction (MGD).

METHODS:

We performed a retrospective analysis of 110 patients (199 eyes) from our dry eye clinic. Subjective symptoms, break-up time (BUT), expressible Meibomian glands, and LLT were measured.

RESULTS:

There was a significant correlation between expressible Meibomian glands and LLT (r = 0.36, P < 0.0001). Also, a possible trend of inverse correlation between subjective symptoms (standard patient evaluation of eye dryness) and the LLT was observed; however, this was not significant (r = -0.13, P < 0.08). Analysis of the whole study collective revealed no correlation between the BUT and the LLT (r = 0.003, P = 0.97). A subgroup analysis of the patients with an LLT ≤ 60 showed a better correlation between the LLT and the BUT, which was marginally not significant (r = 0.21; P = 0.059). For a cut-off value of ≤ 75-nm LLT, we found a sensitivity of 65.8% and a specificity of 63.4% for the detection of an MGD. For a cut-off value of ≤ 60, the sensitivity was 47.9%, and the specificity was 90.2%.

CONCLUSIONS:

The positive correlation between the LLT and expressible meibomian glands found in this study suggests a higher probability of MGD in patients with a low LLT. This automated assessment of the LLT might be a suitable screening test for detecting MGD. However, further prospective studies are needed to confirm these results and to identify potential confounders.

PMID:
24097185
DOI:
10.1097/ICO.0b013e3182a7f3e1
[Indexed for MEDLINE]

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