Individual multidisciplinary clinical phenotypes of nasal and ocular symptoms in hay fever: Crowdsourced cross-sectional study using AllerSearch.

BACKGROUND: Multidisciplinary efforts to prospectively collect and analyze symptoms of hay fever are limited. We aimed to identify the characteristics of nasal and ocular symptoms of hay fever, using the AllerSearch smartphone application. METHODS: This mobile health-based prospective observational study using the AllerSearch smartphone application was conducted between February 1, 2018, and May 1, 2020. Individuals who downloaded AllerSearch from Japan and provided comprehensive self-assessments (including 17 items related to quality of life [QoL]-related items) were included. The characteristics and risk factors for allergic rhinitis (AR) and allergic conjunctivitis (AC) were identified using hierarchical heat maps and multivariate logistic regression. RESULTS: Of the 9041 participants with hay fever, 58.8% had AR and AC, 22.2% had AR, and 5.7% had AC. The AR-AC comorbid cohort showed worse symptoms of hay fever and QoL scores than the other cohorts. Factors (odds ratio, 95% confidence interval) associated with AR-AC included a lower age (0.98, 0.97-0.98), female sex (1.31, 1.19-1.45), liver disease (1.58, 1.26-2.35), dry eye disease (1.45, 1.30-1.63), unknown dry eye disease status (1.46, 1.31-1.62), contact lens use discontinuation during the hay fever season (1.69, 1.28-2.23), and bedroom flooring material other than hardwood, carpet, tatami, or vinyl (1.91, 1.16-3.14). CONCLUSIONS: Analysis of medical big data for hay fever performed using a mobile health app helped identify risk factors and characteristics of AC, AR, and AR-AC. Phenotyping of highly variable symptoms of hay fever, such as nasal and ocular symptoms, can facilitate better-quality clinical care.

Using Medical Big Data to Develop Personalized Medicine for Dry Eye Disease.

Dry eye disease (DED) is a chronic, multifactorial ocular surface disorder with multiple etiologies that results in tear film instability. Globally, the prevalence of DED is expected to increase with an aging society and daily use of digital devices. Unfortunately, the medical field is currently unprepared to meet the medical needs of patients with DED. Noninvasive, reliable, and readily reproducible biomarkers have not yet been identified, and the current mainstay treatment for DED relies on symptom alleviation using eye drops with no effective preventative therapies available. Medical big data analyses, mining information from multiomics studies and mobile health applications, may offer a solution for managing chronic conditions such as DED. Omics-based data on individual physiologic status may be leveraged to prevent high-risk diseases, accurately diagnose illness, and improve patient prognosis. Mobile health applications enable the portable collection of real-world medical data and biosignals through personal devices. Together, these data lay a robust foundation for personalized treatments for various ocular surface diseases and other pathologies that currently lack the components of precision medicine. To fully implement personalized and precision medicine, traditional aggregate medical data should not be applied directly to individuals without adjustments for personal etiology, phenotype, presentation, and symptoms.

A Review of Dry Eye Questionnaires: Measuring Patient-Reported Outcomes and Health-Related Quality of Life.

Dry eye disease (DED) is among the most common eye diseases and is becoming increasingly prevalent. Its symptoms cause a long-term decline in patients' health-related quality of life (HRQL). Inconsistencies often occur between the clinical findings and the subjective symptoms of DED. Therefore, a holistic, balanced, and quantitative evaluation of the subjective symptoms and HRQL using patient-reported outcome questionnaires, in addition to clinical findings, is crucial for accurate DED assessment in patients. This paper reviewed the characteristics of current dry eye questionnaires, including their objectives, number of questions, inclusion of HRQL-related items, and whether they were properly evaluated for psychometric properties. Twenty-four questionnaires were identified; among them, the following six questionnaires that included items assessing HRQL and were properly evaluated for psychometric properties are recommended: the Ocular Surface Disease Index, Impact of Dry Eye in Everyday Life, Dry Eye-Related Quality-of-life Score, University of North Carolina Dry Eye Management Scale, Chinese version of Dry Eye-Related Quality of Life, and 25-Item National Eye Institute Visual Function Questionnaire. Dry eye questionnaires have different objectives and are available in different languages. Therefore, medical practitioners should confirm the characteristics of applicable questionnaires before selecting the most appropriate ones.

Stratification of Individual Symptoms of Contact Lens-Associated Dry Eye Using the iPhone App DryEyeRhythm: Crowdsourced Cross-Sectional Study.

BACKGROUND: 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. OBJECTIVE: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Comparing the Japanese Version of the Ocular Surface Disease Index and Dry Eye-Related Quality-of-Life Score for Dry Eye Symptom Assessment.

The aim of this study was to compare patient-reported symptoms of dry eye disease (DED) between the Japanese version of the Ocular Surface Disease Index (J-OSDI) and the Dry Eye-Related Quality-of-Life Score (DEQS). A total of 169 participants were enrolled between September 2017 and May 2018. Patients were administered the J-OSDI and DEQS questionnaires at their first (baseline) and follow-up visits to evaluate DED-related symptoms. The correlations between the J-OSDI total score and DEQS (Frequency and Degree) scores were evaluated using Pearson's correlation coefficient, and their clinical differences were assessed using the Bland-Altman analysis. At the baseline visit, the J-OSDI score and DEQS (Frequency and Degree) were significantly correlated (r = 0.855, r = 0.897, respectively). Moreover, a significant correlation was found between the J-OSDI score and DEQS (Frequency and Degree) at the follow-up visit (r = 0.852, r = 0.888, respectively). The Bland-Altman analysis revealed a difference (bias) of 4.18 units at the baseline and 4.08 units at the follow-up between the scores of the two questionnaires. The J-OSDI and DEQS were significantly correlated with negligible score differences, suggesting that the J-OSDI can be reliably used for Japanese patients, allowing for cross-country comparisons.

Association between dry eye and depressive symptoms: Large-scale crowdsourced research using the DryEyeRhythm iPhone application.

PURPOSE: Dry eye (DE) disease and depression are increasing in modern times. We investigated the association between DE and depressive symptoms using the iPhone application, DryEyeRhythm. METHODS: This large-scale crowdsourced observational study was conducted within iPhone users in Japan who downloaded DryEyeRhythm. Participants with a Zung Self-rating Depression Scale (SDS) score ≥ 40 were defined as having depressive symptoms, and those with an Ocular Surface Disease Index (OSDI) score ≥ 13 were defined as having DE symptoms (mild, 13-22; moderate, 23-32; and severe, 33-100). We compared SDS scores between participants with normal eye and mild, moderate, and severe OSDI-based DE symptoms. Logistic regression analyses were used to determine the association between DE severity and depressive symptoms after adjustment for demographic characteristics, medical history, and lifestyle habits. RESULTS: This study included 4454 participants (mean age, 27.9 ± 12.6 years; female, 66.7%). Participants with SDS scores ≥40 accounted for 58.2%, 70.9%, 79.4%, and 85.0% of normal controls and participants with mild, moderate, and severe DE symptoms, respectively (P trend < 0.001). The adjusted odds ratios (95% confidence interval) for depressive symptoms (SDS score of ≥40) were 1.62 (1.35-1.95) for mild, 2.39 (1.92-2.97) for moderate, and 3.29 (2.70-4.00) for severe DE symptoms. CONCLUSION: This large-scale crowdsourced clinical study using DryEyeRhythm suggests that depressive symptoms are more common in individuals with more severe DE symptoms. DryEyeRhythm could play a role in earlier prevention or future prospective interventions for depressive symptoms in individuals with DE symptoms.

Comparison of corneal thickness in patients with dry eye disease using the Pentacam rotating Scheimpflug camera and anterior segment optical coherence tomography.

The purpose of this study was to compare central corneal thickness, thinnest corneal thickness, and the thinnest point of the cornea between Pentacam and anterior segment optical coherence tomography (ASOCT) in patients with dry eye disease (DED). This cross-sectional study included 195 participants between November 2015-June 2017. DED was diagnosed using the Asia Dry Eye Society criteria and further divided into mild and severe DED based on kerato-conjunctival vital staining. Central corneal thickness, thinnest corneal thickness, and the thinnest point of the cornea measured by Pentacam and ASOCT were compared, and Pearson's correlation coefficients were estimated. The differences in central corneal thickness and the thinnest corneal thickness between Pentacam and ASOCT were analysed using Bland-Altman and multivariate regression analyses adjusted for age and sex. This study included 70 non-DED subjects and 52 patients with mild and 73 with severe DED. The Pentacam and ASOCT measurements of central corneal thickness and thinnest corneal thickness were strongly correlated, but the respective values were higher when measured with Pentacam. The Bland-Altman analysis revealed differences in central corneal thickness (non DED, 11.8; mild DED, 13.2; severe DED, 19.6) and in thinnest corneal thickness (non DED, 13.1; mild DED, 13.4; severe DED, 20.7). After adjusting for age and sex, the differences in central corneal thickness (ß = 7.029 µm, 95%CI 2.528-11.530) and thinnest corneal thickness (ß = 6.958 µm, 95%CI 0.037-13.879) were significantly increased in the severe-DED group. The distribution of the thinnest point of the cornea in the cornea's inferior temporal quadrant between Pentacam and ASOCT deviated in severe DED (Pentacam: 90.4% vs. ASOCT: 83.6%). Clinicians should consider that there were significant differences in corneal-morphology assessment between the measurements with Pentacam and ASOCT in severe DED.

Characteristics and Risk Factors Associated With Diagnosed and Undiagnosed Symptomatic Dry Eye Using a Smartphone Application.

Importance: The incidence of dry eye disease has increased; the potential for crowdsource data to help identify undiagnosed dry eye in symptomatic individuals remains unknown. Objective: To assess the characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using the smartphone app DryEyeRhythm. Design, Setting, and Participants: A cross-sectional study using crowdsourced data was conducted including individuals in Japan who downloaded DryEyeRhythm and completed the entire questionnaire; duplicate users were excluded. DryEyeRhythm was released on November 2, 2016; the study was conducted from November 2, 2016, to January 12, 2018. Exposures: DryEyeRhythm data were collected on demographics, medical history, lifestyle, subjective symptoms, and disease-specific symptoms, using the Ocular Surface Disease Index (100-point scale; scores 0-12 indicate normal, healthy eyes; 13-22, mild dry eye; 23-32, moderate dry eye; 33-100, severe dry eye symptoms), and the Zung Self-Rating Depression Scale (total of 20 items, total score ranging from 20-80, with ≥40 highly suggestive of depression). Main Outcomes and Measures: Multivariate-adjusted logistic regression analysis was used to identify risk factors for symptomatic dry eye and to identify risk factors for undiagnosed symptomatic dry eye. Results: A total of 21 394 records were identified in our database; 4454 users, included 899 participants (27.3%) with diagnosed and 2395 participants (72.7%) with undiagnosed symptomatic dry eye, completed all questionnaires and their data were analyzed. A total of 2972 participants (66.7%) were women; mean (SD) age was 27.9 (12.6) years. The identified risk factors for symptomatic vs no symptomatic dry eye included younger age (odds ratio [OR], 0.99; 95% CI, 0.987-0.999, P = .02), female sex (OR, 1.99; 95% CI, 1.61-2.46; P < .001), pollinosis (termed hay fever on the questionnaire) (OR, 1.35; 95% CI, 1.18-1.55; P < .001), depression (OR, 1.78; 95% CI, 1.18-2.69; P = .006), mental illnesses other than depression or schizophrenia (OR, 1.87; 95% CI, 1.24-2.82; P = .003), current contact lens use (OR, 1.27; 95% CI, 1.09-1.48; P = .002), extended screen exposure (OR, 1.55; 95% CI, 1.25-1.91; P < .001), and smoking (OR, 1.65; 95% CI, 1.37-1.98; P < .001). The risk factors for undiagnosed vs diagnosed symptomatic dry eye included younger age (OR, 0.96; 95% CI, 0.95-0.97; P < .001), male sex (OR, 0.55; 95% CI, 0.42-0.72; P < .001), as well as absence of collagen disease (OR, 95% CI, 0.23; 0.09-0.60; P = .003), mental illnesses other than depression or schizophrenia (OR, 0.50; 95% CI, 0.36-0.69; P < .001), ophthalmic surgery other than cataract surgery and laser-assisted in situ keratomileusis (OR, 0.41; 95% CI, 0.27-0.64; P < .001), and current (OR, 0.64; 95% CI, 0.54-0.77; P < .001) or past (OR, 0.45; 95% CI, 0.34-0.58; P < .001) contact lens use. Conclusions and Relevance: This study's findings suggest that crowdsourced research identified individuals with diagnosed and undiagnosed symptomatic dry eye and the associated risk factors. These findings could play a role in earlier prevention or more effective interventions for dry eye disease.

Reliability and validity of the Japanese version of the Ocular Surface Disease Index for dry eye disease.

OBJECTIVES: The Ocular Surface Disease Index (OSDI) questionnaire is widely used to evaluate subjective symptoms of dry eye disease (DED) as a primary diagnostic criterion. This study aimed to develop a Japanese version of the OSDI (J-OSDI) and assess its reliability and validity. DESIGN AND SETTING: Hospital-based cross-sectional observational study. PARTICIPANTS: A total of 209 patients recruited from the Department of Ophthalmology at Juntendo University Hospital. METHODS: We translated and culturally adapted the OSDI into Japanese. The J-OSDI was then assessed for internal consistency, reliability and validity. We also evaluated the optimal cut-off value to suspect DED using an area under the receiver operating characteristic curve (AUC) analysis. PRIMARY OUTCOME MEASURES: Internal consistency, test-retest reliability and discriminant validity of the J-OSDI as well as the optimal cut-off value to suspect DED. RESULTS: Of the participants, 152 had DED and 57 did not. The J-OSDI total score showed good internal consistency (Cronbach's alpha=0.884), test-retest reliability (interclass correlation coefficient=0.910) and discriminant validity by known-group comparisons (non-DED, 19.4±16.0; DED, 37.7±22.2; p<0.001). Factor validity was used to confirm three subscales within the J-OSDI according to the original version of the questionnaire. Concurrent validity was assessed by Pearson correlation analysis, and the J-OSDI total score showed a strong positive correlation with the Dry Eye-Related Quality-of-Life Score (γ=0.829). The optimal cut-off value of the J-OSDI total score was 36.3 (AUC=0.744). CONCLUSIONS: The J-OSDI was developed and validated in terms of reliability and validity as an effective tool for DED assessment and monitoring in the Japanese population.