Inomata Takenori, Nakamura Masahiro, Iwagami Masao, Midorikawa-Inomata Akie, Sung Jaemyoung, Fujimoto Keiichi, Okumura Yuichi, Eguchi Atsuko, Iwata Nanami, Miura Maria, Fujio Kenta, Nagino Ken, Hori Satoshi, Tsubota Kazuo, Dana Reza, Murakami Akira
Department of Ophthalmology, Juntendo University Faculty of Medicine, Tokyo, Japan.
Department of Strategic Department of Operating Room Management and Improvement, Juntendo University Faculty of Medicine, Tokyo, Japan.
J Med Internet Res. 2020 Jun 26;22(6):e18996. doi: 10.2196/18996.
Discontinuation of contact lens use is mainly caused by contact lens-associated dry eye. It is crucial to delineate contact lens-associated dry eye's multifaceted nature to tailor treatment to each patient's individual needs for future personalized medicine.
This paper aims to quantify and stratify individual subjective symptoms of contact lens-associated dry eye and clarify its risk factors for future personalized medicine using the smartphone app DryEyeRhythm (Juntendo University).
This cross-sectional study included iPhone (Apple Inc) users in Japan who downloaded DryEyeRhythm. DryEyeRhythm was used to collect medical big data related to contact lens-associated dry eye between November 2016 and January 2018. The main outcome measure was the incidence of contact lens-associated dry eye. Univariate and multivariate adjusted odds ratios of risk factors for contact lens-associated dry eye were determined by logistic regression analyses. The t-distributed Stochastic Neighbor Embedding algorithm was used to depict the stratification of subjective symptoms of contact lens-associated dry eye.
The records of 4454 individuals (median age 27.9 years, SD 12.6), including 2972 female participants (66.73%), who completed all surveys were included in this study. Among the included participants, 1844 (41.40%) were using contact lenses, and among those who used contact lenses, 1447 (78.47%) had contact lens-associated dry eye. Multivariate adjusted odds ratios of risk factors for contact lens-associated dry eye were as follows: younger age, 0.98 (95% CI 0.96-0.99); female sex, 1.53 (95% CI 1.05-2.24); hay fever, 1.38 (95% CI 1.10-1.74); mental illness other than depression or schizophrenia, 2.51 (95% CI 1.13-5.57); past diagnosis of dry eye, 2.21 (95% CI 1.63-2.99); extended screen exposure time >8 hours, 1.61 (95% CI 1.13-2.28); and smoking, 2.07 (95% CI 1.49-2.88). The t-distributed Stochastic Neighbor Embedding analysis visualized and stratified 14 groups based on the subjective symptoms of contact lens-associated dry eye.
This study identified and stratified individuals with contact lens-associated dry eye and its risk factors. Data on subjective symptoms of contact lens-associated dry eye could be used for prospective prevention of contact lens-associated dry eye progression.
停止使用隐形眼镜主要是由隐形眼镜相关性干眼引起的。明确隐形眼镜相关性干眼的多方面性质对于根据每位患者的个体需求制定治疗方案以实现未来的个性化医疗至关重要。
本文旨在使用智能手机应用程序DryEyeRhythm（顺天堂大学）对隐形眼镜相关性干眼的个体主观症状进行量化和分层，并阐明其未来个性化医疗的风险因素。
这项横断面研究纳入了日本下载了DryEyeRhythm的iPhone（苹果公司）用户。2016年11月至2018年1月期间，使用DryEyeRhythm收集与隐形眼镜相关性干眼相关的医学大数据。主要结局指标是隐形眼镜相关性干眼的发病率。通过逻辑回归分析确定隐形眼镜相关性干眼风险因素的单因素和多因素调整比值比。使用t分布随机邻域嵌入算法描绘隐形眼镜相关性干眼主观症状的分层情况。
本研究纳入了4454名个体（中位年龄27.9岁，标准差12.6）的记录，其中包括2972名女性参与者（66.73%），他们完成了所有调查。在纳入的参与者中，1844人（41.40%）使用隐形眼镜，在使用隐形眼镜的人群中，1447人（78.47%）患有隐形眼镜相关性干眼。隐形眼镜相关性干眼风险因素的多因素调整比值比如下：年龄较小，0.98（95%置信区间0.96 - 0.99）；女性，1.53（95%置信区间1.05 - 2.24）；花粉症，1.38（95%置信区间1.10 - 1.74）；除抑郁症或精神分裂症外的精神疾病，2.51（95%置信区间1.13 - 5.57）；既往干眼诊断，2.21（95%置信区间1.63 - 2.99）；延长屏幕暴露时间>8小时，1.61（95%置信区间1.13 - 2.28）；以及吸烟，2.07（95%置信区间1.49 - 2.88）。t分布随机邻域嵌入分析根据隐形眼镜相关性干眼的主观症状将其可视化并分层为14组。
本研究识别并分层了患有隐形眼镜相关性干眼的个体及其风险因素。隐形眼镜相关性干眼主观症状的数据可用于前瞻性预防隐形眼镜相关性干眼的进展。
