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Vatinee Y Bunya, Mina Massaro-Giordano, Frederick B Vivino, esen akpek, Alan Baer, John Alexander Gonzales, Tom Lietman, Gui-Shuang Ying; A Brief Questionnaire to Screen Dry Eye Patients for Sjogren's Syndrome. Invest. Ophthalmol. Vis. Sci. 2019;60(9):6777.
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© ARVO (1962-2015); The Authors (2016-present)
To develop a sensitive and specific screening tool for ophthalmologists to identify dry eye patients with a high likelihood of having underlying Sjögren’s Syndrome (SS), utilizing data from the Sjögren's International Collaborative Clinical Alliance (SICCA) cohort.
Participants previously enrolled in the SICCA study for possible SS underwent extensive testing including specimen and data collection in order to classify them as SS, controls, or indeterminate. Symptoms that were candidate predictors of SS were selected based on which organ systems are potentially affected by SS. All participants also underwent ocular surface exams. Among 1053 participants who were self-referred or were referred by an ophthalmologist, univariate and multivariate logistic regression models were created to identify which questions and ocular signs were useful in distinguishing SS cases from controls. Odds ratios (OR) and 95% confidence intervals (95% CI) were used to assess the association of specific symptoms and ocular signs with SS. Area under the ROC curve (AUC) analyses were used to determine the best model to identify SS.
In the univariate analysis, a set of 11 questions was identified that could distinguish dry eye patients with or without SS (p<0.05). From this set, four questions were statistically significant in the final multivariate logistic regression model including: 1) Is your mouth dry when eating a meal? [yes = OR 1.53 (1.14-2.05)]; 2) Can you eat a cracker without drinking a fluid or liquid? [no = OR 1.37 (1.02-1.84)]; 3) How often do you have excessive tearing? [none of the time = OR 3.63 (1.86-7.07); and 4) Are you able to produce tears? [no = OR 2.23 (1.65-3.01)]. The area under the ROC curve (AUC) for the multivariate logistic regression model that also included gender was 0.69 (95% CI 0.66-0.72). The inclusion of tear break-up time and lissamine green staining of the conjunctiva further improved the prediction model with an AUC of 0.78 (95% CI: 0.76-0.81).
Here we describe a new evidence-based screening questionnaire for ophthalmologists to identify dry eye patients with a high likelihood of having SS. With future refinement and validation, this questionnaire could be used alone, or in combination with other ocular surface exam findings to identify SS patients earlier, thereby facilitating treatment and better clinical outcomes.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.
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