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Symptom-based stratification algorithm for heterogeneous symptoms of dry eye disease: a feasibility study

Eye volume 37, pages 3484–3491 (2023)Cite this article

Subjects

Abstract

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.

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Fig. 1: Hierarchical heat map of dry eye disease subjective symptoms determined by the Japanese version of the Ocular Surface Disease Index score (J-OSDI).
Fig. 2: Heatmap of severity ranks of dry eye disease examination by clusters.
Fig. 3: Comparison of the Japanese version of the Ocular Surface Disease Index score (J-OSDI) between patients in this study and smartphone application users in a previous study by each stratified cluster.

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Data availability

All data are available in the main text. The code supporting the findings of this study is available from the corresponding author upon reasonable request. Data processing and analysis were performed using STATA version 16.1.

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Acknowledgements

We thank OHAKO, Inc. (Tokyo, Japan) and Medical Logue, Inc. (Tokyo, Japan) for developing the DryEyeRhythm application, and Shiang T, Yoshimura Y, Hirastuka Y, Hori S, Uchino M, and Tsubota K for the initial development of the application.

Funding

This research was supported by JST COI Grant Number JPMJCER02WD02 (TI), JSPS KAKENHI Grant Numbers 20KK0207 (TI), 20K23168 (AMI), 21K17311 (AMI), 21K20998 (AE), and 22K16983 (AE), Kondou Kinen Medical Foundation, Medical Research Encouragement Prize 2020 (TI), Charitable Trust Fund for Ophthalmic Research in Commemoration of Santen Pharmaceutical’s Founder 2020 (TI), Nishikawa Medical Foundation, Medical Research Encouragement Prize 2020 (TI), and the OTC Self- Medication Promotion Foundation (YO). The sponsors had no role in the design or performance of the study, in data collection and management, in the analysis and interpretation of the data, in the preparation, review, approval of the manuscript, or in the decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Ken Nagino, Takenori Inomata, Akie Midorikawa-Inomata, Atsuko Eguchi & Hiroyuki Kobayashi

  2. Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Ken Nagino, Takenori Inomata, Masahiro Nakamura, Jaemyoung Sung, Kenta Fujio, Yasutsugu Akasaki, Yuichi Okumura, Tianxiang Huang, Keiichi Fujimoto, Maria Miura, Shokirova Hurramhon, Jun Zhu, Mizu Ohno, Kunihiko Hirosawa, Yuki Morooka & Akira Murakami

  3. Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan

    Ken Nagino, Takenori Inomata, Masahiro Nakamura, Jaemyoung Sung, Kenta Fujio, Yasutsugu Akasaki, Yuichi Okumura, Tianxiang Huang, Keiichi Fujimoto, Atsuko Eguchi, Maria Miura, Mizu Ohno, Kunihiko Hirosawa, Yuki Morooka & Akira Murakami

  4. Juntendo University Graduate School of Medicine, AI Incubation Farm, Tokyo, Japan

    Takenori Inomata

  5. Precision Health, Department of Engineering, Graduate School of Bioengineering, The University of Tokyo, Tokyo, Japan

    Masahiro Nakamura

  6. University of South Florida, Morsani College of Medicine, Tampa, FL, USA

    Jaemyoung Sung

  7. Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan

    Masao Iwagami

  8. Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA

    Reza Dana

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  1. Ken Nagino
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Contributions

KN was responsible for methodology, data curation, software, visualization, formal analysis, investigation, writing-original draft preparation, and writing-reviewing and editing. TI was responsible for conceptualization, methodology, validation, investigation, writing-original draft preparation, and writing-reviewing and editing. MN was responsible for software, visualization, formal analysis, investigation, and writing-reviewing and editing. JS conducted the investigation and wrote the original draft, and reviewed and edited the manuscript. AMI performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. MI performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. KF performed data curation, validation, investigation, writing-original draft preparation, and writing reviewing and editing. YA performed data curation, validation, investigation, writing-original draft preparation, and writing-reviewing and editing. YO performed data curation, validation, investigation, writing-original draft preparation, and writing-reviewing and editing. TH performed data curation, validation, investigation, writing-original draft preparation, and writing-reviewing and editing. KF performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. AE performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. MM performed data curation, investigation, writing-original draft preparation, and writing-reviewing and editing. SH performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. JZ performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. MK performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. KH performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. YM performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. RD performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. AM performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing. HK performed validation, investigation, writing-original draft preparation, and writing-reviewing and editing.

Corresponding author

Correspondence to Takenori Inomata.

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Competing interests

The DryEyeRhythm app was created using Apple’s ResearchKit (Cupertino, CA, USA) along with OHAKO, Inc. (Tokyo, Japan) and Medical Logue, Inc. (Tokyo, Japan). TI and YO are the owners of InnoJin, Inc, Tokyo, Japan, for developing DryEyeRhythm. TI reported receiving grants from Johnson & Johnson Vision Care, SEED Co., Ltd, Novartis Pharma K.K., and Kowa Company, Ltd. outside the submitted work, as well as personal fees from Santen Pharmaceutical Co., Ltd., and InnoJin, Inc. The remaining authors declare no conflict of interest.

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Nagino, K., Inomata, T., Nakamura, M. et al. Symptom-based stratification algorithm for heterogeneous symptoms of dry eye disease: a feasibility study. Eye 37, 3484–3491 (2023). https://doi.org/10.1038/s41433-023-02538-4

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