AB0375 EXTERNAL VALIDATION OF THE GIANT CELL ARTERITIS PROBABILITY SCORE IN THE NETHERLANDS
Authors: L. M. Neuman, M. Van Nieuwland, M. Vermeer, D. Boumans, E. Colin, C. Alves
Journal: Annals of the Rheumatic Diseases (2021)

Background: Severe complications of giant cell arteritis (GCA), such as blindness and ischemic stroke, can be prevented by timely treatment. This requires early and accurate diagnosis. In 2019, Laskou et al. developed a clinical GCA probability score (GCAPS) that allows physicians to assess the probability of GCA at initial assessment. The GCAPS is suitable for easy implementation in daily practice. It has a high sensitivity (95.7%) and specificity (86.7%) at a cut-off value of 9.5 as proposed by Laskou et al. Objectives: The aim of this study was to externally validate the GCAPS in a general hospital. Methods: A retrospective cohort of patients with suspected GCA between January 1 st 2017 and October 1 st 2019 at Ziekenhuisgroep Twente (the Netherlands) was used. As the parameter extra-cranial artery abnormality was not available in our cohort, a modified version of the GCAPS was used (m-GCAPS). The m-GCAPS was compared to clinical diagnosis of a rheumatologist after six months in accordance with Laskou et al. The validity of the m-GCAPS was assessed by plotting a receiver operating characteristic (ROC) curve and assessing sensitivity and specificity for multiple cut-off values. For practical purposes, risk stratifications as proposed by Sebastian et al. (2020) were applied to our data (low-risk &lt;9; intermediate-risk 9-12; high-risk &gt;12). Results: In our cohort, 40 GCA patients and 95 controls (suspected of GCA) had complete records and were therefore used for analysis. In the patient group, 29 (72.5%) were female and mean (SD) age was 73.3 (1.6). The area under the ROC curve (AUC) was 0.83 (95% CI 0.75 – 0.91). At our optimal cut-off value of 10.5, sensitivity was 80.0% and specificity was 75.8%. In total, 5 patients and 45 controls were stratified as low, 7 patients and 35 controls as intermediate and 28 patients and 15 controls as high-risk. Conclusion: The m-GCAPS showed good discrimination in our cohort. Risk stratification of patients looks promising, although cut-off values by Sebastian et al. might need optimization based on population and modification to the GCAPS. Colour Doppler ultrasound is recommended by EULAR as the first-choice diagnostic test to confirm GCA in case of high pre-test probability and exclude GCA for low pre-test probability. The (m-)GCAPS may aid in quantification of this pre-test probability. References: [1]Laskou F, Coath F, Mackie SL et al. A probability score to aid the diagnosis of suspected giant cell arteritis. Clin Exp Rheumatol. 2019;37 Suppl 1(2):104–8. [2]Sebastian A, Tomelleri A, Kayani A et al. Probability-based algorithm using ultrasound and additional tests for suspected GCA in a fast-track clinic. RMD Open. 2020 Sep 1;6(3):e001297. [3]Dejaco C, Ramiro S, Duftner C et al. EULAR recommendations for the use of imaging in large vessel vasculitis in clinical practice. Ann Rheum Dis. 2018 May 1;77(5):636 LP – 643. Table 1. Cut-off values of the m-GCAPS stratified into risk categories as proposed by Sebastian et al. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and the number of patients scoring above or below the cut-off values are described (optimal cut-off value in blue). The total number of patients with a score within low- (red), intermediate- (orange) and high- (green) risk categories are depicted. GCA+ (N ) N=40 GCA- (N ) N=95 Score m-GCAPS GCA+ (N ) GCA- (N ) Sens. (% ) Spec. (% ) PPV (% ) NPV (% ) Low-risk &lt;9 5 (12.5% ) 45 (47.4% ) 5.5 &lt;5.5 0 14 100 14.7 33.1 100 &gt;5.5 40 81 8.5 &lt;8.5 5 45 87.5 47.4 41.2 90 &gt;8.5 35 50 Intermediate-risk 9-12 7 (17.5% ) 35 (36.8% ) 9.5 &lt;9.5 8 56 80 58.9 45.1 87.5 &gt;9.5 32 39 10.5 &lt;10.5 8 72 80 75.8 58.2 90 &gt;10.5 32 23 11.5 &lt;11.5 10 74 75 77.9 58.8 88.1 &gt;11.5 30 21 High-risk &gt;9 28 (70.0% ) 15 (15.8% ) 12.5 &lt;12.5 12 80 70 84.2 65.1 87 &gt;12.5 28 15 14.5 &lt;14.5 18 89 55 93.7 78.6 83.2 &gt;14.5 22 6 19.5 &lt;19.5 37 95 7.5 100 100 72 &gt;19.5 3 0 Figure 1. ROC curve depicting sensitivity and (1-) specificity. The AUC was 0.83 (95.0% CI 0.75-0.91) with an optimal cut-off value of 10.5 (sensitivity 80.0%, specificity 75.8%). Disclosure of Interests: None declared