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 PARTICIPANTSA 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 Ն40 highly suggestive of depression). MAIN OUTCOMES AND MEASURESMultivariate-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) (
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.
This systematic review aimed to determine currently reported clinical and prodromal ocular symptoms in patients with coronavirus disease 2019 (COVID-19). METHODS. An online article search was performed in PubMed and EMBASE. Altogether 15 studies (retrospective, prospective, or case studies) involving 1533 patients with COVID-19, reporting on ocular symptoms, and with outcome data available were identified. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines were followed. Study-specific estimates (incidence rates of ocular symptoms in patients with COVID-19) of cases were combined using one-group meta-analysis in a random-effects model. RESULTS. Of all included studies, 11.2% (95% confidence interval, 5.5-16.9; 78/1526 cases) reported ocular symptoms. The most common ocular finding was conjunctivitis. Prodromal ocular symptoms occurred in 12.5% (13/104 cases) of patients with COVID-19. Positive real-time polymerase chain reaction results were obtained for 16.7% (10/60 cases) of conjunctival samples and 0% (0/17 cases) of tear samples. Twelve ocular conjunctival swab samples tested positive for SARS-CoV-2. Ten cases were from subjects showing ocular symptoms (16.7%, 10/60 cases), and the remaining two cases were from subjects without ocular manifestation (1.8%, 2/113 cases). Limitations included the short study period, small sample size, findings were limited to the Asian population, only seven articles included ophthalmologic examination details, and there is currently no consensus on COVID-19 management. CONCLUSIONS. Ocular symptoms may occur in the presymptomatic phase as a prodromal symptom (12.5%, 13/104 cases), suggesting the possibility of viral transmission from the conjunctiva.
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.
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