June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
A Decision Analytic Approach for the Management of a Patient with Suspected Giant Cell Arteritis
Author Affiliations & Notes
  • Mikel Mikhail
    Ophthalmology, McGill University, Montreal, Quebec, Canada
  • Leonard A Levin
    Ophthalmology, McGill University, Montreal, Quebec, Canada
    Ophthalmology and Visual Science, University of Wisconsin, Madison, Wisconsin, United States
  • Footnotes
    Commercial Relationships   Mikel Mikhail, None; Leonard Levin, Aerie (C), GSK (C), Inotek (C), Quark (C), Regenera (C), Wisconsin Alumni Research Foundation (P)
  • Footnotes
    Support  NIH R21 R21 EY025074 and P30EY016665; Canada Research Chairs; Canadian Foundation for Innovation
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 3853. doi:
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      Mikel Mikhail, Leonard A Levin; A Decision Analytic Approach for the Management of a Patient with Suspected Giant Cell Arteritis. Invest. Ophthalmol. Vis. Sci. 2017;58(8):3853.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Giant cell arteritis (GCA) is a significant cause of visual and systemic morbidity in the elderly. Despite our improved knowledge of the disease, it is not always obvious when to perform temporal artery biopsy (TAB) and/or treat with corticosteroids. To optimize decision-making in GCA, we created an updated evidence-based formal decision tree analysis to help clinicians manage suspected GCA.

Methods : A computer-based decision analytic model was created using TreeAgePro software (TreeAge, Williamstown, MA). This decision tree reflected common diagnostic and therapeutic options for a patient with suspected GCA. Distributions for pooled probabilities and utilities were derived from a comprehensive literature search. Using utility analysis, the decision model selects the diagnostic and therapeutic pathway resulting in the least possible disutility for a pre-defined set of patient characteristics on presentation. The model assumes that all patients receive laboratory testing prior to the decision whether or not to perform a unilateral or bilateral TAB and/or begin treatment. This is the first decision model incorporating C-reactive protein (CRP) in its algorithm.

Results : The choice of omitting biopsy and treatment vs. performing a unilateral or bilateral biopsy vs. instituting empiric treatment without biopsy depends on several factors, including patient age, symptoms and laboratory testing. These factors were used to calculate the pretest probability of GCA being present. The optimal decision depends on the pretest probability of disease. A biopsy is recommended when there is visual loss and the prior probability of GCA is 1.1-93.8 %, with a unilateral biopsy (and no further biopsy if negative) favored for 1.1-7.6%, a bilateral biopsy for 7.7-76.5%, and a unilateral biopsy (and a contralateral biopsy if negative) for 76.6-93.8%. A sufficiently high pre-test probability (> 93.8% and > 97.2% in patients with and without vision loss at presentation respectively) favors empiric steroid treatment.

Conclusions : In GCA, the disease, biopsy, and steroid treatment can result in significant patient morbidity. The decision whether to forego any further diagnostic modalities or treatment, perform a unilateral vs. bilateral TAB, or empirically treat a patient with suspected GCA, is challenging. This decision model provides an evidence-based method for choosing the optimal pathway for each patient.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

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