Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Strategic Operating Room Management and Improvement, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Strategic Operating Room Management and Improvement, Juntendo University Graduate School of Medicine, Tokyo, Japan; Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Ocul Surf. 2022 Jul;25:19-25. doi: 10.1016/j.jtos.2022.04.005. Epub 2022 Apr 25.
Undiagnosed or inadequately treated dry eye disease (DED) decreases the quality of life. We aimed to investigate the reliability, validity, and feasibility of the DryEyeRhythm smartphone application (app) for the diagnosis assistance of DED.
This prospective, cross-sectional, observational, single-center study recruited 82 participants (42 with DED) aged ≥20 years (July 2020-May 2021). Patients with a history of eyelid disorder, ptosis, mental disease, Parkinson's disease, or any other disease affecting blinking were excluded. Participants underwent DED examinations, including the Japanese version of the Ocular Surface Disease Index (J-OSDI) and maximum blink interval (MBI). We analyzed their app-based J-OSDI and MBI results. Internal consistency reliability and concurrent validity were evaluated using Cronbach's alpha coefficients and Pearson's test, respectively. The discriminant validity of the app-based DED diagnosis was assessed by comparing the results of the clinical-based J-OSDI and MBI. The app feasibility and screening performance were evaluated using the precision rate and receiver operating characteristic curve analysis.
The app-based J-OSDI showed good internal consistency (Cronbach's α = 0.874). The app-based J-OSDI and MBI were positively correlated with their clinical-based counterparts (r = 0.891 and r = 0.329, respectively). Discriminant validity of the app-based J-OSDI and MBI yielded significantly higher total scores for the DED cohort (8.6 ± 9.3 vs. 28.4 ± 14.9, P < 0.001; 19.0 ± 11.1 vs. 13.2 ± 9.3, P < 0.001). The app's positive and negative predictive values were 91.3% and 69.1%, respectively. The area under the curve (95% confidence interval) was 0.910 (0.846-0.973) with concurrent use of the app-based J-OSDI and MBI.
DryEyeRhythm app is a novel, non-invasive, reliable, and valid instrument for assessing DED.
未确诊或治疗不充分的干眼症（DED）会降低生活质量。本研究旨在评估干眼症节律智能手机应用程序（app）在 DED 诊断辅助中的可靠性、有效性和可行性。
本前瞻性、横断面、观察性、单中心研究招募了 82 名年龄≥20 岁（2020 年 7 月至 2021 年 5 月）的参与者（42 名患有 DED）。患有眼睑疾病、上睑下垂、精神疾病、帕金森病或任何其他影响眨眼的疾病的患者被排除在外。参与者接受了 DED 检查，包括日本版眼表疾病指数（J-OSDI）和最大眨眼间隔（MBI）。我们分析了他们基于应用程序的 J-OSDI 和 MBI 结果。使用 Cronbach's alpha 系数和 Pearson 检验分别评估内部一致性可靠性和同时效性。通过比较基于临床的 J-OSDI 和 MBI 结果，评估基于应用程序的 DED 诊断的判别有效性。使用精度率和受试者工作特征曲线分析评估应用程序的可行性和筛选性能。
基于应用程序的 J-OSDI 显示出良好的内部一致性（Cronbach's α=0.874）。基于应用程序的 J-OSDI 和 MBI 与基于临床的对应值呈正相关（r=0.891 和 r=0.329）。基于应用程序的 J-OSDI 和 MBI 的判别有效性表明 DED 组的总分显著更高（8.6±9.3 与 28.4±14.9，P<0.001；19.0±11.1 与 13.2±9.3，P<0.001）。应用程序的阳性和阴性预测值分别为 91.3%和 69.1%。同时使用基于应用程序的 J-OSDI 和 MBI 时，曲线下面积（95%置信区间）为 0.910（0.846-0.973）。
干眼症节律应用程序是一种新型的、非侵入性的、可靠的、有效的评估 DED 的仪器。
