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A probability score to aid the diagnosis of suspected giant cell arteritis


1, 2, 3, 4, 5, 6

 

  1. Southend University Hospital NHS Trust, Leeds Teaching Hospitals NHS Trust and Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, UK.
  2. Southend University Hospital NHS Trust, Leeds Teaching Hospitals NHS Trust and Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, UK.
  3. NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust and Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, UK.
  4. Southend University Hospital NHS Trust, Leeds Teaching Hospitals NHS Trust and Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, UK.
  5. Southend University Hospital NHS Trust, Leeds Teaching Hospitals NHS Trust and Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, UK.
  6. Southend University Hospital NHS Trust, Leeds Teaching Hospitals NHS Trust and Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, UK. bhaskar.dasgupta@southend.nhs.uk

CER11494
2019 Vol.37, N°2 ,Suppl.117
PI 0104, PF 0108
Diagnosis

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PMID: 30767870 [PubMed]

Received: 04/07/2018
Accepted : 10/12/2018
In Press: 15/02/2019
Published: 21/05/2019

Abstract

OBJECTIVES:
We propose a GCA probability score intended to help to risk-stratify patients referred by general practitioners with suspected GCA into those with high probability of GCA versus low probability of GCA. In this pilot study we evaluated the diagnostic accuracy of this proposed scoring system.
METHODS:
A scoring system was proposed based on clinical experience. Retrospective analysis was conducted from clinical notes of consecutive patients presenting to a Fast Track Pathway clinic between August 2016 and August 2017. The GCA Probability Score was calculated for each patient and receiver operating characteristic (ROC) curve plotted.
RESULTS:
Of 122 consecutive patients, full data were available for calculation of GCA probability score in all patients except one (excluded from this analysis). The area under the ROC curve was 0.953 (95% confidence interval: 0.911, 0.994). The ROC curve showed an optimal cut point of 9.5 out of a possible score of 32. At this cut-point there was a sensitivity of 95.7% and specificity 86.7%, and 88.4% of cases were correctly classified.
CONCLUSIONS:
The GCA Probability Score is a promising and feasible tool for risk stratification of patients referred by general practitioners with suspected GCA. In a fast track clinic setting this aids exclusion of GCA in low probability cases and confirmation of disease in high probability disease. Refinement and subsequent external validation of this score is required.

Rheumatology Article