Utility of online GCA risk models in predicting the result of temporal artery biopsy within a clinical setting: study of diagnostic and screening tests

Abstract

BackgroundTemporal artery biopsy (TAB) is the gold standard for the diagnosis of giant cell arteritis (GCA) but has many limitations. The Ing model, González-López model, and Weis model are tools to estimate a patient's likelihood of GCA. This study investigates the utility of these prediction models in triaging patients referred for TAB. MethodsThis study is a retrospective examination of patients who underwent TAB by a neuro-ophthalmologist over a 5-year period. Data collected through chart review were inputted into prediction models to evaluate GCA risk and compared with TAB results and clinical diagnosis. Cut-off values for 100% sensitivity and specificity for TAB result were used to determine whether TAB could be avoided where there was preoperative certainty of the result. ResultsAmong 155 eligible patients, mean age was 73 years, and 78.1% were female. TAB was negative in 103 patients (66.5%) and positive in 42 patients (27.1%). Twenty-three patients (22.3%) were diagnosed clinically and treated for biopsy-negative GCA. The Ing model had no positive biopsies below 10.59% and no negative biopsies above 68.44%. The González-López model had no positive biopsies below 0.27% and no negative biopsies above 98.08%. The Weis model had no positive biopsies with a score less than zero. ConclusionForty-one biopsies (28.9%) could have been avoided using the Ing model, 9 (6.34%) using the González-López model, and 28 (19.7%) using the Weis model. The findings suggest that the Ing and Weis models are useful screening tools for GCA with the potential to improve the effective use of health care resources.