Symptom-based Stratification Algorithm for Heterogeneous Symptoms of Dry Eye Disease: a Feasibility Study.

Background/objective To test the feasibility of a dry eye disease (DED) symptom stratification algorithm previously established for the general population among patients visiting ophthalmologists. Subject/methods This retrospective cross-sectional study was conducted between December 2015 and October 2021 at a university hospital in Japan; participants who underwent a comprehensive DED examination and completed the Japanese version of the Ocular Surface Disease Index (J-OSDI) were included. Patients diagnosed with DED were stratified into seven clusters using a previously established symptom-based stratification algorithm for DED. Characteristics of the patients in stratified clusters were compared. Results In total, 426 participants were included (median age [interquartile range]; 63 [48–72] years; 357 (83.8%) women). Among them, 291 (68.3%) participants were diagnosed with DED and successfully stratified into seven clusters. The J-OSDI total score was highest in cluster 1 (61.4 [52.2–75.0]), followed by cluster 5 (44.1 [38.8–47.9]). The tear film breakup time was the shortest in cluster 1 (1.5 [1.1–2.1]), followed by cluster 3 (1.6 [1.0–2.5]). The J-OSDI total scores from the stratified clusters in this study and those from the clusters identified in the previous study showed a significant correlation ( r = 0.991, P < 0.001). Conclusions The patients with DED who visited ophthalmologists were successfully stratified by the previously established algorithm for the general population, uncovering patterns for their seemingly heterogeneous and variable clinical characteristics of DED. The results have important implications for promoting treatment interventions tailored to individual patients and implementing smartphone-based clinical data collection in the future.