Ocular Surface Workup With Automated Noninvasive Measurements for the Diagnosis of Meibomian Gland Dysfunction

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Abstract

Purpose:

To analyze diagnostic performance of an ocular surface workup based on automated noninvasive measurements in the diagnosis of meibomian gland dysfunction (MGD).

Methods:

Two hundred ninety-eight eyes of 149 patients with MGD and 54 eyes of 27 control patients were analyzed. Ocular Surface Disease Index (OSDI), noninvasive breakup time (BUT), lipid layer thickness, meibomian gland loss, and tear osmolarity were calculated. The correlations among variables in the MGD group were analyzed. The area under the curve (AUC) of receiver operating characteristic curves was calculated.

Results:

OSDI, noninvasive BUT, and meibomian gland loss were significantly different between MGD and control groups (respectively, 37.9 ± 19.6 vs. 7.1 ± 2.8; 8.8 ± 3.6 vs. 11.0 ± 3.0; 28.0 ± 17.6 vs. 21.2 ± 13.0; always P < 0.05). Positive correlations were found between lipid layer thickness and noninvasive BUT and between meibomian gland loss and OSDI (respectively, r = 0.169, P = 0.004; r = 0.187, P = 0.004). Noninvasive BUT had the highest diagnostic power as a single parameter, followed by meibomian gland loss (respectively AUC = 0.686, AUC = 0.598). When the diagnosis of MGD was made based on either noninvasive BUT or meibomian gland loss being abnormal, sensitivity was 86.2% and specificity 38.5%. When the diagnosis was made on both noninvasive BUT and meibomian gland loss being abnormal, sensitivity was 39.3% and specificity 85.6%.

Conclusions:

This automated noninvasive ocular surface workup may represent a useful screening tool for the diagnosis of MGD. In case of positivity of either noninvasive BUT or meibomian gland loss, subsequent qualitative clinical tests should be performed to achieve a reliable diagnosis and more precise characterization of MGD.

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