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Grading Severity and Activity in Thyroid Eye Disease
source: Ophthalmic Plastic and Reconstructive Surgery
year: 2018
authors: Dolman PJ
summary/abstract:Purpose:
Thyroid eye disease (TED) is an autoimmune disorder causing inflammation, expansion, and fibrosis of orbital fat, muscle, and lacrimal gland. This article reviews the different methods of grading severity and activity of TED and focuses on the VISA Classification for disease evaluation and planning management.
Methods:
Accurate evaluation of the clinical features of TED is essential for early diagnosis, identification of high-risk disease, planning medical and surgical intervention, and assessing response to therapy. Evaluation of the activity and severity of TED is based on a number of clinical features: appearance and exposure, periorbital tissue inflammation and congestion, restricted ocular motility and strabismus, and dysthyroid optic neuropathy. The authors review these clinical features in relation to disease activity and severity.
Results:
Several classification systems have been devised to grade severity of these clinical manifestations. These include the NO SPECS Classification, the European Group on Graves Orbitopathy severity scale, the Clinical Activity Score of Mourits, and the VISA Classification as outlined here. The authors compare and contrast these evaluation schemes.
Conclusions:
An accurate clinical assessment of TED, including grading of disease severity and activity, is necessary for early diagnosis, recognition of those cases likely to develop more serious complications, and appropriate management planning. The VISA Classification grades both disease severity and activity using subjective and objective inputs. It organizes the clinical features of TED into 4 discrete groupings: V (vision, dysthyroid optic neuropathy); I (inflammation, congestion); S (strabismus, motility restriction); A (appearance, exposure). The layout follows the usual sequence of the eye examination and facilitates comparison of measurements between visits and data collation for research.
DOI: 10.1097/IOP.0000000000001150
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