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Multicenter Study
. 2007 Feb;66(2):222-7.
doi: 10.1136/ard.2006.054593. Epub 2006 Aug 10.

Development and validation of a consensus methodology for the classification of the ANCA-associated vasculitides and polyarteritis nodosa for epidemiological studies

Richard Watts  1 , Suzanne Lane, Thomas Hanslik, Thomas Hauser, Bernhard Hellmich, Wenche Koldingsnes, Alfred Mahr, Mårten Segelmark, Jan W Cohen-Tervaert, David Scott
Affiliations
Multicenter Study

Development and validation of a consensus methodology for the classification of the ANCA-associated vasculitides and polyarteritis nodosa for epidemiological studies

Richard Watts et al. Ann Rheum Dis. 2007 Feb.

Abstract

Background: The classification of antineutrophil cytoplasmic antibody-associated vasculitis (AAV) and polyarteritis nodosa (PAN) for epidemiology studies is confusing. The existing schemes such as American College of Rheumatology (ACR) criteria, Chapel Hill Consensus Conference (CHCC) definitions and Lanham criteria produce overlapping and conflicting classifications, making it difficult to compare incidence figures.

Aim: To develop a consensus method of using these criteria and definitions for epidemiological studies to permit comparison without confounding by classification.

Methods: A stepwise algorithm was developed by consensus between a group of doctors interested in the epidemiology of vasculitis. The aim was to categorise patients with Wegener's granulomatosis, microscopic polyangiitis (MPA), Churg-Strauss syndrome (CSS) and PAN into single clinically relevant categories. The ACR and Lanham criteria for CSS, and ACR criteria for Wegener's granulomatosis were applied first, as these were considered to be the most specific. Surrogate markers for Wegener's granulomatosis were included to distinguish Wegener's granulomatosis from MPA. MPA was classified using the CHCC definition and surrogate markers for renal vasculitis. Finally, PAN was classified using the CHCC definition. The algorithm was validated by application to 20 cases from each centre and 99 from a single centre, followed by a paper case exercise.

Results: A four-step algorithm was devised. It successfully categorises patients into a single classification. There was good correlation between observers in the paper case exercise (91.5%; unweighted kappa = 0.886).

Conclusion: The algorithm achieves its aim of reliably classifying patients into a single category. The use of the algorithm in epidemiology studies should permit comparison between geographical areas.

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Conflict of interest statement

Competing interests: None.

References

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