High-Tech Parameters for the Evaluation of Signs and Symptoms of Dry Eye Disease: Identification of Clinical Cut-Offs and Agreement with Low-Tech Tests.

INTRODUCTION

High-tech devices for the assessment of dry eye disease (DED) are increasingly available. However, the agreement between high- and low-tech parameters has been poorly explored to date. Trying to fill these gaps, we conducted a post hoc analysis on a recently published retrospective study on patients with DED receiving both low- and high-tech (Keratograph) assessments, and treatment with different lubricating eyedrops.

METHODS

Six clinical questions were defined by the authors, considering literature gaps and their clinical experience, namely: (1) are NIKBUT-i and T-BUT interchangeable parameters? (2) What was the correlation between low- and high-tech parameters in untreated and treated patients with DED? (3) What was the correlation between signs and symptoms at baseline and during/after treatment? (4) Which parameters were better associated with symptoms? And with symptoms change over time? (5) What was the performance of NIKBUT-i and T-BUT in detecting clinically relevant changes? (6) What was the clinical advantage of adding other high- and low-tech parameters, respectively, to NIKBUT-i and T-BUT?

RESULTS

Low-tech measures were the best descriptors of the Ocular Surface Disease Index (OSDI) at baseline. In contrast, high-tech assessments demonstrate better performance in detecting changes over time. The distribution of NIKBUT-i data was more dispersed than TBUT both at baseline and follow-up. At a fixed specificity of 80%, the sensitivity in detecting clinically relevant ameliorations of symptoms was 42% for NIKBUT-i and 25% for T-BUT. A battery of high-tech tests could detect 90% of clinical amelioration, compared with 45% with low-tech tests (p < 0.001). Correlation between low- and high-tech parameters in both treated and untreated patients is lacking.

CONCLUSIONS

Low-tech measures are adequate for diagnostic purposes in DED, whereas high-tech showed better performances at follow-up, particularly when different tests are combined. Overall, poor interchangeability among parameters and agreement with symptoms was reported both with high- and low-tech assessments.