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Clinical Sciences
Oct 2011

Clinical and Soft-Tissue Computed Tomographic Predictors of Dysthyroid Optic Neuropathy: Refinement of the Constellation of Findings at Presentation

Author Affiliations

Author Affiliations: Department of Ophthalmology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada (Dr Weis); Departments of Radiology (Drs Heran, Chan, Chiu, and Hurley) and Ophthalmology and Visual Science (Dr Rootman), Vancouver General Hospital and The University of British Columbia, Vancouver, British, Columbia, Canada; and Department of Radiology, Royal Brisbane Hospital, Brisbane, Queensland, Australia (Dr Jhamb).

Arch Ophthalmol. 2011;129(10):1332-1336. doi:10.1001/archophthalmol.2011.276
Abstract

Objective To evaluate the ability to predict the presence of dysthyroid optic neuropathy (DON) using computed tomography assessment of soft-tissue and clinical features.

Study Design A retrospective consecutive case series of patients with thyroid-related orbitopathy.

Results One hundred eighty-nine orbits from 99 patients were evaluated. Statistically significant clinical predictors of DON on univariate analysis included a difference in intraocular pressure from primary gaze to upgaze (P = .02), the presence of lagophthalmos (P = .04), and inflammation as measured by the VISA (vision, inflammation, strabismus, appearance/exposure) inflammatory scale (P = .004). Dysthyroid optic neuropathy was inversely related to the marginal reflex distance (P = .01), levator function (P = .02), total ductions (P = .003), and interpalpebral fissure (P = .04). Statistically significant radiologic predictors determined on univariate analysis included apical crowding (P < .001), presence of enlarged tendons (P = .004), increasing total rectus diameter (P = .02), and presence of small, low densities within the recti muscles (P = .04). Multivariate analysis found only total ductions (P = .02) and marginal reflex distance (P = .04) determined on clinical examination and apical crowding shown on computed tomography (P = .003) to be significantly associated with DON. Receiver operating characteristic curves were used to evaluate the ability of the clinical and radiologic assessment, as well as the combination of these assessments, to predict DON. All 3 models were strong predictors of DON, with no statistically significant differences in the area under the receiver operating characteristic curve among them (P = .14).

Conclusions Total ductions, marginal reflex distance, and apical crowding observed on computed tomography scans are able to predict the presence of DON with high sensitivity, specificity, positive predictive value, and negative predictive value. Eyelid ptosis is a novel predictor of DON.

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