Please review British Society for Rheumatology guideline on
diagnosis and treatment of giant cell arteritis: executive summary [PubMed Abstract] [Full-Text HTML] [Full-Text PDF]. Rheumatology, Volume 59, Issue 3, March 2020, Pages 487–494, https://doi.org/10.1093/rheumatology/kez664
To calculate the Southend Giant Cell Arteritis Probablility Score, please see Southend GCA Probability Score (GCAPS) from RheumCalc. Accessed 4/28/2024.
Today, I review, link to, and excerpt from Validation of the Southend giant cell arteritis probability score in a Scottish single-centre fast-track pathway. [PubMed Abstract] [Full-Text HTML] [Full-Text PDF]. . Rheumatol Adv Pract. 2021 Dec 15;6(1):rkab102. doi: 10.1093/rap/rkab102. eCollection 2022.
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Treat-to-target recommendations in giant cell arteritis and polymyalgia rheumatica.
Ann Rheum Dis. 2024 Jan 2;83(1):48-57. doi: 10.1136/ard-2022-223429.PMID: 36828585 Free PMC article.
All that follows is from the above outstanding resource.
Abstract
Objective: The aim was to provide external validation of the Southend GCA probability score (GCAPS) in patients attending a GCA fast-track pathway (GCA FTP) in NHS Lanarkshire.
Methods: Consecutive GCA FTP patients between November 2018 and December 2020 underwent GCAPS assessment as part of routine care. GCA diagnoses were supported by US of the cranial and axillary arteries (USS), with or without temporal artery biopsy (TAB), and confirmed at 6 months. Percentages of patients with GCA according to GCAPS risk group, performance of total GCAPS in distinguishing GCA/non-GCA final diagnoses, and test characteristics using different GCAPS binary cut-offs were assessed. Associations between individual GCAPS components and GCA and the value of USS and TAB in the diagnostic process were also explored.
Results: Forty-four of 129 patients were diagnosed with GCA, including 0 of 41 GCAPS low-risk patients (GCAPS <9), 3 of 40 medium-risk patients (GCAPS 9-12) and 41 of 48 high-risk patients (GCAPS >12). Overall performance of GCAPS in distinguishing GCA/non-GCA was excellent [area under the receiver operating characteristic curve, 0.976 (95% CI 0.954, 0.999)]. GCAPS cut-off ≥10 had 100.0% sensitivity and 67.1% specificity for GCA. GCAPS cut-off ≥13 had the highest accuracy (91.5%), with 93.2% sensitivity and 90.6% specificity. Several individual GCAPS components were associated with GCA. Sensitivity of USS increased by ascending GCAPS risk group (nil, 33.3% and 90.2%, respectively). TAB was diagnostically useful in cases where USS was inconclusive.
Conclusion: This is the first published study to describe application of GCAPS outside the specialist centre where it was developed. Performance of GCAPS as a risk stratification tool was excellent. GCAPS might have additional value for screening GCA FTP referrals and guiding empirical glucocorticoid treatment.
Keywords: GCA; diagnosis; fast-track pathway; probability score; temporal arteritis; ultrasound.
© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.
Key messages
Introduction
International guidelines recommend temporal and axillary artery US as a first-line modality for confirmation of diagnoses of GCA [1, 2]. Fast-track GCA pathways (GCA FTP) with application of US are growing in popularity; however, the number and availability of trained sonographers is a potential limiting factor. Delayed clinical assessment carries the risk of complications, including loss of vision [3], in addition to jeopardizing the value of clinical findings and test results in patients started on glucocorticoids by their referring clinician.
Risk stratification scores could help prioritize referrals to GCA FTPs, facilitating timely assessment and treatment of patients with GCA, in addition to avoidance of over-treatment with glucocorticoids in those with other diagnoses. The Southend GCA probability score (GCAPS), also known as the Southend GCA clinical pre-test probability score [4], developed in a specialist centre at Southend University Hospital, has shown promise in discriminating patients with low, medium and high pre-test probability for GCA [5] and appears to augment the diagnostic performance of US. There is a suggestion that GCAPS alone might be sufficient to exclude GCA in low-risk patients, without additional tests, which, if confirmed, could have major significance for resource allocation. These findings require validation, particularly given the potentially serious consequences of missed diagnoses. Evidence to support the use of GCAPS in external cohorts is currently limited, consisting of conference reports but no published studies [6, 7].
fig1
The next 3 figures are enlargements of the above fig1 graphic.
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Fig. 2 shows the final number of patients with GCA according to risk group. There were no patients with GCA in the low-risk group, 3 in the medium-risk group (7.5%) and 41 in the high-risk group (85.4%).
GCAPS components
Overview Table 1 shows GCAPS components and total scores for the whole group, by GCAPS risk group and by final GCA diagnosis. Most patients presented with cranial pain [120 of 129 (93.0%)], with similar percentages across risk groups and between GCA and non-GCA groups. Only 3 of 129 patients (2.3%) were <50 years of age, none of whom had GCA. No cases of GCA had CRP <10 mg/L. Ophthalmological abnormalities (i.e. anterior ischaemic optic neuropathy, central retinal artery occlusion, visual field defect or relative afferent pupillary defect) were rare, occurring in 7 of 129 patients (5.4%; 2 of 7 medium risk, 5 of 7 high risk; 5 of 7 GCA, 2 of 7 non-GCA).
Supplementary Table S2, available at Rheumatology Advances in Practice online, shows additional GCAPS data for the whole group and subgroups as in Table 1. Extracranial vascular signs (i.e. bruits or loss of pulse) and cranial nerve palsies were rare, affecting 5 of 129 (3.9%) and 2 of 129 (1.6%), respectively. Alternative diagnosis more likely than GCA occurred in 57 of 129 patients (44.2%).
Reciver operating characteristic curve analyses Fig. 3 shows the ROC curve for overall performance of GCAPS in discriminating final diagnoses of GCA from non-GCA, with an area under the curve of 0.976 (95% CI 0.954, 0.999), indicating excellent diagnostic ability [14]. The maximum Youden index (a measure of optimal compromise between sensitivity and specificity) [15] was 0.873, corresponding to a GCAPS binary cut-off value of ≥13.
Binary cut-offs
Using a GCAPS binary cut-off of ≥9, 44 of 88 patients were correctly identified as GCA and 41 of 41 as non-GCA, giving a sensitivity of 100.0% and specificity of 48.2%. Using a cut-off of ≥10, sensitivity remained 100.0%, but specificity was higher, at 67.1%. Maximum accuracy (91.5%) was seen with a cut-off of ≥13 (also the maximum Youden index in ROC analyses). These results, along with additional cut-off values, are detailed in Table 2.
