Probability-based algorithm using ultrasound and additional tests for suspected GCA in a fast-track clinic.


Journal

RMD open
ISSN: 2056-5933
Titre abrégé: RMD Open
Pays: England
ID NLM: 101662038

Informations de publication

Date de publication:
09 2020
Historique:
received: 30 04 2020
revised: 22 07 2020
accepted: 05 09 2020
entrez: 30 9 2020
pubmed: 1 10 2020
medline: 1 9 2021
Statut: ppublish

Résumé

Clinical presentations of giant cell arteritis (GCA) are protean, and it is vital to make a secure diagnosis and exclude mimics for urgent referrals with suspected GCA. The main objective was to develop a joined-up, end-to-end, fast-track confirmatory/exclusionary, algorithmic process based on a probability score triage to drive subsequent investigations with ultrasound (US) and any appropriate additional tests as required. The algorithm was initiated by stratifying patients to low-risk category (LRC), intermediate-risk category (IRC) and high-risk category (HRC). Retrospective data was extracted from case records. The Southend pretest probability score (PTPS) overall showed a median score of 9 and a 75th percentile score of 12. We, therefore, classified LRC as PTPS <9, IRC 9-12 and HRC >12. GCA diagnosis was made by a combination of clinical, US, and laboratory findings. The algorithm was assessed in all referrals seen in 2018-2019 to test the diagnostic performance of US overall and in individual categories. Of 354 referrals, 89 had GCA with cases categorised as LRC (151), IRC (137) and HRC (66). 250 had US, whereas 104 did not (score <7, and/or high probability of alternative diagnoses). In HRC, US showed sensitivity 94%, specificity 85%, accuracy 92% and GCA prevalence 80%. In LRC, US showed sensitivity undefined (0/0), specificity 98%, accuracy 98% and GCA prevalence 0%. In IRC, US showed sensitivity 100%, specificity 97%, accuracy 98% and GCA prevalence 26%. In the total population, US showed sensitivity 97%, specificity 97% and accuracy 97%. Prevalence of GCA overall was 25%. The Southend PTPS successfully stratifies fast-track clinic referrals and excludes mimics. The algorithm interprets US in context, clarifies a diagnostic approach and identifies uncertainty, need for re-evaluation and alternative tests. Test performance of US is significantly enhanced with PTPS.

Identifiants

pubmed: 32994361
pii: rmdopen-2020-001297
doi: 10.1136/rmdopen-2020-001297
pmc: PMC7547539
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Déclaration de conflit d'intérêts

Competing interests: BD reports grants and personal fees from Roche, personal fees from GSK, BMS, Sanofi and Abbie, outside the submitted work. All other authors have nothing to declare.

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Auteurs

Alwin Sebastian (A)

Rheumatology, Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK.

Alessandro Tomelleri (A)

Rheumatology, Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK.
Unit of Immunology, Rheumatology, Allergy and Rare Diseases, San Raffaele Scientific Institute, Milan, Italy.

Abdul Kayani (A)

Rheumatology, Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK.

Diana Prieto-Pena (D)

Rheumatology, Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK.
Rheumatology, Marques de Valdecilla University Hospital, Santander, Spain.

Chavini Ranasinghe (C)

Rheumatology, Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK.

Bhaskar Dasgupta (B)

Rheumatology, Southend University Hospital NHS Foundation Trust, Westcliff-on-Sea, UK bhaskar.dasgupta@southend.nhs.uk.

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Classifications MeSH