Challenges in Using Signs and Symptoms to Evaluate New Biomarkers of Dry Eye Disease

Abstract

INTRODUCTION T he lack of association between signs and symptoms in dry eye disease (DED) presents a challenge in designing clinical trials to objectively evaluate the performance of new biomarkers. When making a diagnosis based on multiple signs, one must balance the sensitivity of therapeutic response, temporal variability, the a priori probability distribution of each sign, and the patient’s history of therapy against the practical considerations of patient recruitment. Proper classification will almost entirely determine the success or failure of a clinical trial, yet there is little agreement between the constituent signs in DED. Tear osmolarity, a test that has been proposed as the “gold standard” of DED and has recently become widely available, represents an example of such a conundrum that clinicians and researchers must learn how to resolve. Many decades of basic research have confirmed the role of tear hyperosmolarity as a causative mechanism and clinical endpoint in the pathogenesis of DED. Current literature reveals that tear osmolarity demonstrated the highest correlation with disease severity and was found to be the single best metric to diagnose and classify DED. Osmolarity has been found to be superior in overall accuracy to any other single test for dry eye diagnosis, even when the other test measures were applied to a diagnosis within the sample groups from which they were derived, and recent data suggest that patients with severe keratoconjunctivitis sicca have a significantly higher tear film osmolarity than healthy controls. Testing tear film osmolarity can be a very effective objective diagnostic tool in the diagnosis of DED. Further, with tear osmolarity >305 mOsm/L selected as the cut-off value for dry eye, the likelihood ratio was 10.99, higher than that