Objective To develop and validate updated classification criteria for giant cell arteritis (GCA).
Methods Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in six phases: (1) identification of candidate items, (2) prospective collection of candidate items present at the time of diagnosis, (3) expert panel review of cases, (4) data‐driven reduction of candidate items, (5) derivation of a points‐based risk classification score in a development data set and (6) validation in an independent data set.
Results The development data set consisted of 518 cases of GCA and 536 comparators. The validation data set consisted of 238 cases of GCA and 213 comparators. Age ≥50 years at diagnosis was an absolute requirement for classification. The final criteria items and weights were as follows: positive temporal artery biopsy or temporal artery halo sign on ultrasound (+5); erythrocyte sedimentation rate ≥50 mm/hour or C reactive protein ≥10 mg/L (+3); sudden visual loss (+3); morning stiffness in shoulders or neck, jaw or tongue claudication, new temporal headache, scalp tenderness, temporal artery abnormality on vascular examination, bilateral axillary involvement on imaging and fluorodeoxyglucose–positron emission tomography activity throughout the aorta (+2 each). A patient could be classified as having GCA with a cumulative score of ≥6 points. When these criteria were tested in the validation data set, the model area under the curve was 0.91 (95% CI 0.88 to 0.94) with a sensitivity of 87.0% (95% CI 82.0% to 91.0%) and specificity of 94.8% (95% CI 91.0% to 97.4%).
Conclusion The 2022 American College of Rheumatology/EULAR GCA classification criteria are now validated for use in clinical research.
- giant cell arteritis
- systemic vasculitis
- magnetic resonance imaging
Statistics from Altmetric.com
If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.
Giant cell arteritis (GCA), formerly known as temporal arteritis, is the most common form of systemic vasculitis in patients aged ≥50 years.1 GCA is defined by granulomatous arteritis that affects large‐sized and medium‐sized blood vessels with a predisposition to affect the cranial arteries.2 Common presenting features of the disease include headache, constitutional symptoms, jaw claudication, scalp tenderness, visual disturbances and elevated inflammatory markers.3
In 1990, the American College of Rheumatology (ACR) endorsed classification criteria for GCA.4 These criteria were established before the widespread use of non-invasive and advanced vascular imaging modalities, which have become increasingly incorporated in the clinical assessment of GCA. Vascular ultrasound can be used to diagnose GCA, and depending on the clinical setting, a non-compressible ‘halo’ sign of a temporal±axillary artery may replace the need for temporal artery biopsy (TAB).5–8 Moreover, vascular imaging has demonstrated that arterial involvement in GCA is not exclusively confined to the cranial arteries9 10 and can commonly affect the aorta and primary branches in a pattern similar to Takayasu arteritis (TAK).11 12
The limitations of the ACR 1990 criteria for GCA have become more apparent in the conduct of recent clinical trials and other research studies, in which investigators typically modify the 1990 ACR criteria to reflect modern practice.6 13 14 Notably, the 1990 ACR criteria focus mostly on cranial features of GCA and do not perform well in classifying patients with disease predominantly affecting the larger arteries. The 1990 ACR criteria were derived by using comparator populations, which included many patients with small‐vessel vasculitis, a form of vasculitis that is not typically difficult to differentiate from GCA. In addition, the 1990 ACR criteria for GCA followed the ‘number of criteria’ rule, which considered each criterion to have equal weight as a classifier for the disease. Since then, methodologic advances in classification criteria have allowed movement towards weighted criteria with threshold scores that improve performance characteristics.15 16
This article outlines the development and validation of the revised ACR/EULAR-endorsed classification criteria for GCA.
An international Steering Committee comprising clinician investigators with expertise in vasculitis, statisticians and data managers was assembled to oversee the overall development of classification criteria for primary vasculitis.17 A detailed and complete description of the methods involved in the development and validation of the classification criteria for GCA is located in online supplemental appendix 1. Briefly, the Steering Committee implemented a six‐stage plan using data‐driven and consensus methodology to develop the following criteria.
Stage 1: generation of candidate classification items for the systemic vasculitides
Candidate classification items were generated by expert opinion and reviewed by a group of vasculitis experts across a range of specialties using nominal group technique.
Stage 2: Diagnostic and Classification Criteria for Vasculitis prospective observational study
A prospective, international, multisite observational study was conducted (see online supplemental file 3 for study investigators and sites). Consecutive patients representing the full spectrum of vasculitides were recruited from academic and community practices. Patients were included if they were 18 years or older and had a diagnosis of vasculitis or a condition that mimics vasculitis (eg, infection, malignancy, atherosclerosis). Patients with GCA could only be enrolled within 2 years of diagnosis. Only data present at diagnosis were used to develop the classification criteria.
Stage 3: expert review to derive a gold standard-defined set of cases of large-vessel vasculitis
Experts in vasculitis from a wide range of geographic locations and specialties (see online supplemental file 3) reviewed all submitted cases of vasculitis and a random selection of vasculitis mimics. Each reviewer was asked to review ~50 submitted cases to confirm the diagnosis and to specify the degree of certainty of their diagnosis as follows: very certain, moderately certain, uncertain or very uncertain. Only cases agreed on with at least moderate certainty by two reviewers were retained for further analysis.
Stage 4: refinement of candidate items specifically for large-vessel vasculitis
The Steering Committee conducted a data‐driven process to reduce the number of candidate items of relevance to cases and comparators for large-vessel vasculitis (LVV). Density plots were assessed to study age distribution at diagnosis and symptom onset for GCA and TAK. Absolute age requirements vs incorporation of age as a candidate criteria item was considered. Items related to the vascular physical examination, vascular imaging, arterial biopsy and laboratory values were combined or eliminated based on consensus review. Items were selected for exclusion if they had a prevalence of <5% within the data set, and/or they were not clinically relevant for classification criteria (eg, related to infection, malignancy or demography). Low‐frequency items of clinical importance could be combined, when appropriate. Patterns of vascular imaging findings detected by vascular ultrasound, angiography or positron emission tomography (PET) were defined by K‐means clustering.18
Stage 5: derivation of the final classification criteria for GCA
The Diagnostic and Classification Criteria for Vasculitis (DCVAS) data set was split into development (70%) and validation (30%) sets. Comparisons were performed between cases of GCA and a randomly selected comparator group in the following proportions: TAK, 33.5%; other vasculitides that mimic GCA and TAK (isolated aortitis, primary central nervous system vasculitis, polyarteritis nodosa, Behçet’s disease and other LVV), 33.4%; and other diagnoses that mimic LVV (eg, atherosclerosis, unspecific headache), 33.1%. Least absolute shrinkage and selection operator (lasso) logistic regression was used to identify predictors from the data set and create a parsimonious model including only the most important predictors.19 The final items in the model were formulated into a clinical risk‐scoring tool, with each factor assigned a weight based on its respective regression coefficient. A threshold that best balanced sensitivity and specificity was identified for classification.
Stage 6: validation of the final classification criteria for GCA
Performance of the new criteria was validated in an independent set of cases and comparators. Performance of the final classification criteria was examined in specific subsets of patients with GCA using data from the combined development and validation sets to maximise sample sizes for the subgroups. Patients were studied according to different disease subtypes (biopsy‐proven GCA and large‐vessel GCA) and regions of the world (North America, Europe) where clinical strategies to assess GCA are known to differ.20 Biopsy‐proven GCA was defined as definite vasculitis on TAB reported by the submitting physician, and large‐vessel GCA was defined as vasculitic involvement of the aorta or its branch arteries on either angiography (computed tomography, magnetic resonance imaging, or catheter‐based angiography), ultrasound or PET, without vasculitis on TAB. Comparison was made between the measurement properties of the new classification criteria for GCA and the 1990 ACR classification criteria in the validation data set. Performance characteristics of the criteria were also tested in patients with TAK and compared with those with GCA diagnosed between the ages of 50 and 60 years.
Generation of candidate classification items for the systemic vasculitides
The Steering Committee identified >1000 candidate items for the DCVAS Case Report Form (see online supplemental appendix 2).
DCVAS prospective observational study
Between January 2011 and December 2017, the DCVAS study recruited 6991 participants from 136 sites in 32 countries. Information on the DCVAS sites, investigators and study participants is listed in online supplemental appendices 3, 4 and 5.
Expert review methodology to derive a gold standard-defined final set of cases of LVV
The LVV expert panel review process included 56 experts who reviewed vignettes derived from the Case Report Forms for 2131 cases submitted with a diagnosis of LVV (1608 (75.5% of Case Report Forms)), another type of vasculitis (118 (5.5% of Case Report Forms)) or a mimic of vasculitis (405 (19.0% of Case Report Forms)). Characteristics and the list of expert reviewers are shown in online supplemental appendices 6 and 7. A sample vignette and the LVV expert panel review flow chart are shown in online supplemental appendices 8 and 9. A total of 1695 cases (80%) passed the main LVV process. An additional 373 cases of LVV and comparators, confirmed during a previous review process to derive the classification criteria for antineutrophil cytoplasmic antibody-associated vasculitis, were also included. In total, after both review processes, 2068 cases were available for the stages 4 and 5 analyses.
The submitting physician diagnosis of GCA was confirmed in 913 of 1137 cases (80.3%) after both expert panel reviews. The reasons for exclusion were diagnosis of GCA categorised as ‘uncertain’ or ‘very uncertain’ during panel review (n=187) or change in diagnosis during panel review to another type of vasculitis (isolated aortitis, TAK, other vasculitides) (n=11) or to a comparator disease (n=26). An additional 29 patients who were not initially diagnosed as having GCA by the submitting physician were diagnosed as having GCA after panel review and DCVAS Steering Committee member adjudication. In total, 942 cases of confirmed GCA were available for analysis. To balance the number of cases of GCA with the number of available comparators, 756 cases of GCA were randomly selected for subsequent analysis.
Refinement of candidate items specifically for GCA
Only 7 of 942 patients with GCA (<1%) were diagnosed at age <50 years (see online supplemental appendix 10 for the distribution of ‘age at diagnosis’ in patients with LVV, and the similar distribution of ‘age at symptom onset,’). Therefore, an age of ≥50 years at diagnosis was considered an absolute requirement to classify GCA. Cluster analyses of vascular imaging data identified bilateral axillary involvement and diffuse fluorodeoxyglucose uptake throughout the aorta on PET as specific imaging patterns for GCA (see online supplemental appendices 11 and 12). These imaging patterns were tested as potential classifiers.
Following a data‐driven and expert consensus process, 72 items of the DCVAS Case Report Form were retained for regression analysis, including 32 demographic and clinical items, 14 laboratory items (including values of C reactive protein (CRP) level and erythrocyte sedimentation rate (ESR), each divided into 5 categories), 14 imaging items (13 composite), 11 vascular examination items (5 composite and upper extremity blood pressure divided into 6 categories) and 1 biopsy item (online supplemental appendix 13).
Derivation of the final classification criteria for GCA
A total of 1505 patients were selected for analysis (756 GCA and 749 comparators), of which 1054 (70%) were in the development data set (518 GCA and 536 comparators) and 451 (30%) in the validation data set (238 GCA and 213 comparators). Table 1 describes the demographic and clinical features of the patients with GCA and the comparators. The patients with GCA were recruited from Europe (n=796), North America (n=112), Oceania (n=18) and Asia (n=16). Clinical diagnoses assigned to patients in the comparator group are detailed in online supplemental appendix 14.
Lasso regression of the previously selected 72 items yielded 27 independent predictor variables for GCA (online supplemental appendix 15A). Each predictor variable was then reviewed for inclusion by the DCVAS Steering Committee, based on their ORs and specificity to GCA, to ensure face validity. The variables ‘definitive vasculitis on TAB’ and ‘halo sign on temporal artery ultrasound’ were found to dominate the model as quite strong predictors of GCA (see online supplemental appendix 16A for cluster plots showing almost a perfect overlap between the diagnosis of GCA and positive TAB or halo sign on temporal artery ultrasound). Therefore, for the remaining variables to have discriminatory value, both of these items were removed from the model, combined into one composite item ‘vasculitis on TAB or halo sign on temporal artery ultrasound’ and given a risk score of one point below the final threshold set to classify GCA to maintain face validity. The variables ‘jaw claudication’ and ‘tongue claudication’ were combined into one item, as were the variables ‘maximum ESR (>50 mm/hour)’ and ‘maximum CRP (>10 mg/L).’ Although the variable ‘new persistent headache, occipital or cervical’ showed important statistical significance, it decreased the overall specificity of the model when testing their final performance characteristics (patients vs comparators) and was, therefore, also removed. Weighting of the individual criterion included in the model was based on logistic regression fitted to the remaining nine selected predictors (online supplemental appendix 17A).
Validation of the final classification criteria for GCA
Using a cut-off of ≥6 in total risk score in the validation data set (see online supplemental appendix 18A for different cut-off points), the sensitivity was 87.0% (95% CI 82.0% to 91.0%) and specificity was 94.8% (95% CI 91.0% to 97.4%). The area under the curve for the model was 0.91 (95% CI 0.88 to 0.94) (online supplemental appendix 19A). The final 2022 ACR/EULAR classification criteria for GCA are presented in figure 1 (for the slide presentation versions, see online supplemental figure 1).
The performance characteristics of the criteria in different subsets of patients with GCA are shown in table 2 and online supplemental appendix 20A. Biopsy‐proven GCA showed a sensitivity of 100% (95% CI 99.0% to 100.0%) and a specificity of 94.9% (95% CI 93.1% to 96.4%) and large‐vessel GCA showed a sensitivity of 55.7% (95% CI 46.5% to 64.6%) and a specificity of 94.9% (95% CI 93.1% to 96.4%). Sensitivity of the criteria in North America was 77.8% (95% CI 67.8% to 85.9%) and in Europe was 87.2% (95% CI 84.4% to 89.7%). Specificity in North America was 95.6% (95% CI 90.6% to 98.4%) and in Europe was 88.8% (95% CI 84.9% to 92.0%).
When the 1990 ACR classification criteria for GCA were applied to the DCVAS validation data set, the criteria performed poorly due to low sensitivity (80.3% (95% CI 74.6% to 85.1%)) but retained good specificity (92.5% (95% CI 88.1% to 95.7%)). In particular, the 1990 ACR criteria had poor sensitivity for patients with large‐vessel GCA (37.1% (95% CI 28.6% to 46.2%)).
Age restrictions are absolute requirements for the 2022 ACR/EULAR classification criteria for GCA (≥50 years at diagnosis) and TAK (≤60 years at diagnosis). However, of the 70 patients with GCA diagnosed between the ages of 50 and 60 years, 44 (62.9%) met the new GCA classification criteria, 9 (12.9%) met the new TAK classification criteria, and only 2 (2.9%) met both the new GCA and TAK classification criteria (online supplemental appendix 21).
Presented here are the final 2022 ACR/EULAR GCA classification criteria. A six‐stage approach was used, underpinned by data from the multinational, prospective DCVAS study and informed by expert review and consensus at each stage. The comparator group for developing and validating the criteria were other vasculitides and conditions that mimic GCA, where discrimination from GCA is difficult but important. In the validation set, the new criteria had a sensitivity of 87.0% (95% CI 82.0% to 91.0%) and a specificity of 94.8% (95% CI 91.0% to 97.4%). These are the official final values that should be quoted when referring to the criteria. The sensitivity and specificity values calculated in the development set were very similar, providing reassurance that the statistical methods avoided overfitting of models. The new criteria incorporate modern imaging techniques and have excellent specificity and sensitivity within a large, international cohort of patients with GCA. The criteria were designed to have face and content validity for use in clinical trials and other research studies.
These criteria are validated and intended for the purpose of classification of vasculitis and are not appropriate for use to establish a diagnosis of vasculitis. The aim of the classification criteria is to differentiate cases of GCA from similar types of vasculitis in research settings.21 Therefore, the criteria should only be applied when a diagnosis of LVV or medium-vessel vasculitis has been made and all potential “vasculitis mimics” have been excluded. The exclusion of mimics is a key aspect of many classification criteria including those for Sjögren’s syndrome22 and rheumatoid arthritis.16 The 1990 ACR classification criteria for vasculitis perform poorly when used for diagnosis (ie, when used to differentiate between cases of vasculitis vs mimics without vasculitis),23 and it is expected that the 2022 criteria would also perform poorly if used inappropriately as diagnostic criteria in people for whom alternative diagnoses, such as infection or other non‐vasculitis inflammatory diseases, are still being considered.
The 2022 ACR/EULAR GCA classification criteria are the result of an incredibly large worldwide effort, in which an extensive set of data was collected from >1000 patients with the submitted diagnosis of GCA. These criteria reflect current clinical practice, integrating different investigative methods (eg, TAB, ultrasound, angiography, PET) from various countries and medical specialties. Real cases of GCA and comparators were reviewed by a wide range of experts in vasculitis to establish an unbiased diagnostic reference to derive the criteria. Advanced statistical methods including lasso logistic regression and cluster analyses were applied, which facilitated testing for different covariates of interest, namely specific patterns of vasculitic involvement in imaging. Modern classification techniques with weighted criterion with threshold scores were used, allowing for more discriminatory items to factor more heavily in disease classification.
When compared with the original 1990 ACR classification criteria for GCA, the 2022 ACR/EULAR GCA classification criteria demonstrated greater sensitivity while maintaining similar specificity to the 1990 criteria. In particular, the new criteria were able to correctly classify more patients with the large‐vessel GCA subtype, in whom the sensitivity of the 1990 ACR criteria was only 37.1%. Unlike the 1990 ACR criteria, an age of ≥50 years at diagnosis is a mandatory requirement to classify GCA in the 2022 ACR/EULAR criteria. This age threshold included>99% of patients with the reference diagnosis of GCA. The new criteria maintain good discriminative ability for patients diagnosed between the ages of 50 and 60 years, the interval where the absolute age requirements for the 2022 ACR/EULAR criteria for GCA and for TAK can overlap.
A potential limitation of these criteria was the non-standardised acquisition of clinical and imaging data among patients with LVV and comparators (eg, not all patients underwent vascular examination of the temporal arteries, PET was not available in many centres treating patients with LVV, and TAB and/or ultrasound was not performed in all patients with suspected GCA, etc). However, this reflects existing differences in clinical practice, and the 11 items included in the criteria allow for a feasible evaluation of patients in any clinical setting. These criteria also provide flexibility for classifying a patient, regardless of the diagnostic assessment strategy employed by physicians. Definite vasculitis on TAB was defined by the submitting physician and did not undergo central review; ~20% of cases did not have specific histopathologic findings but were reported as ‘definitive vasculitis on TAB’ alone. Most patients were recruited from Europe and North America, with fewer patients from Asia and Oceania. The performance characteristics of the criteria should be further tested in other populations that were underrepresented in the DCVAS cohort and may have different clinical presentations of GCA.
The 2022 ACR/EULAR classification criteria for GCA are the product of a rigorous methodologic process that utilised an extensive data set generated by the work of a remarkable international group of collaborators. These criteria have been endorsed by the ACR and EULAR and are now ready for use in clinical research.
Patient consent for publication
Ethical approval was obtained from local ethics committees.
We acknowledge the patients and clinicians who provided data to the DCVAS project.
Presented at This article is published simultaneously in Arthritis & Rheumatology.
Correction notice This article has been corrected since it published Online First. An amendment has been made to figure one in the line: LABORATORY, IMAGING, AND BIOPSY CRITERIA, in the subrow labeled Maximum ESR ≥ 50 mm/hour or maximum CRP ≥ 10 mg/liter2. This correction has not been made in print.
Collaborators The DCVAS study investigators are as follows: Paul Gatenby (ANU Medical Centre, Canberra, Australia); Catherine Hill (Central Adelaide Local Health Network: The Queen Elizabeth Hospital, Australia); Dwarakanathan Ranganathan (Royal Brisbane and Women’s Hospital, Australia); Andreas Kronbichler (Medical University Innsbruck, Austria); Daniel Blockmans (University Hospitals Leuven, Belgium); Lillian Barra (Lawson Health Research Institute, London, Ontario, Canada); Simon Carette, Christian Pagnoux (Mount Sinai Hospital, Toronto, Canada); Navjot Dhindsa (University of Manitoba, Winnipeg, Canada); Aurore Fifi-Mah (University of Calgary, Alberta, Canada); Nader Khalidi (St Joseph’s Healthcare, Hamilton, Ontario, Canada); Patrick Liang (Sherbrooke University Hospital Centre, Canada); Nataliya Milman (University of Ottawa, Canada); Christian Pineau (McGill University, Canada); Xinping Tian (Peking Union Medical College Hospital, Beijing, China); Guochun Wang (China-Japan Friendship Hospital, Beijing, China); Tian Wang (Anzhen Hospital, Capital Medical University, China); Ming-hui Zhao (Peking University First Hospital, China); Vladimir Tesar (General University Hospital, Prague, Czech Republic); Bo Baslund (University Hospital, Copenhagen [Rigshospitalet], Denmark); Nevin Hammam (Assiut University, Egypt); Amira Shahin (Cairo University, Egypt); Laura Pirila (Turku University Hospital, Finland); Jukka Putaala (Helsinki University Central Hospital, Finland); Bernhard Hellmich (Kreiskliniken Esslingen, Germany); Jörg Henes (Universitätsklinikum Tübingen, Germany); Julia Holle, Frank Moosig (Klinikum Bad Bramstedt, Germany); Peter Lamprecht (University of Lübeck, Germany); Thomas Neumann (Universitätsklinikum Jena, Germany); Wolfgang Schmidt (Immanuel Krankenhaus Berlin, Germany); Cord Sunderkoetter (Universitätsklinikum Müenster, Germany); Zoltan Szekanecz (University of Debrecen Medical and Health Science Center, Hungary); Debashish Danda (Christian Medical College & Hospital, Vellore, India); Siddharth Das (Chatrapathi Shahuji Maharaj Medical Center, Lucknow [IP], India); Rajiva Gupta (Medanta, Delhi, India); Liza Rajasekhar (NIMS, Hyderabad, India); Aman Sharma (Postgraduate Institute of Medical Education and Research, Chandigarh, India); Shrikant Wagh (Jehangir Clinical Development Centre, Pune [IP], India); Michael Clarkson (Cork University Hospital, Ireland); Eamonn Molloy (St. Vincent’s University Hospital, Dublin, Ireland); Carlo Salvarani (Santa Maria Nuova Hospital, Reggio Emilia, Italy); Franco Schiavon (L’Azienda Ospedaliera of University of Padua, Italy); Enrico Tombetti (Università Vita- Salute San Raffaele Milano, Italy); Augusto Vaglio (University of Parma, Italy); Koichi Amano (Saitama Medical University, Japan); Yoshihiro Arimura (Kyorin University Hospital, Japan); Hiroaki Dobashi (Kagawa University Hospital, Japan); Shouichi Fujimoto (Miyazaki University Hospital [HUB], Japan); Masayoshi Harigai, Fumio Hirano (Tokyo Medical and Dental University Hospital, Japan); Junichi Hirahashi (University Tokyo Hospital, Japan); Sakae Honma (Toho University Hospital, Japan); Tamihiro Kawakami (St. Marianna University Hospital Dermatology, Japan); Shigeto Kobayashi (Juntendo University Koshigaya Hospital, Japan); Hajime Kono (Teikyo University, Japan); Hirofumi Makino (Okayama University Hospital, Japan); Kazuo Matsui (Kameda Medical Centre, Kamogawa, Japan); Eri Muso (Kitano Hospital, Japan); Kazuo Suzuki, Kei Ikeda (Chiba University Hospital, Japan); Tsutomu Takeuchi (Keio University Hospital, Japan); Tatsuo Tsukamoto (Kyoto University Hospital, Japan); Shunya Uchida (Teikyo University Hospital, Japan); Takashi Wada (Kanazawa University Hospital, Japan); Hidehiro Yamada (St. Marianna University Hospital Internal Medicine, Japan); Kunihiro Yamagata (Tsukuba University Hospital, Japan); Wako Yumura (IUHW Hospital [Jichi Medical University Hospital], Japan); Kan Sow Lai (Penang General Hospital, Malaysia); Luis Felipe Flores-Suarez (Instituto Nacional de Enfermedades Respiratorias, Mexico City, Mexico); Andrea Hinojosa-Azaola (Instituto Nacional de Ciencias Médicas y Nutricion Salvador Zubiran, Mexico City, Mexico); Bram Rutgers (University Hospital Groningen, Netherlands); Paul-Peter Tak (Academic Medical Centre, University of Amsterdam, Netherlands); Rebecca Grainger (Wellington, Otago, New Zealand); Vicki Quincey (Waikato District Health Board, New Zealand); Lisa Stamp (University of Otago, Christchurch, New Zealand); Ravi Suppiah (Auckland District Health Board, New Zealand); Emilio Besada (Tromsø, Northern Norway, Norway); Andreas Diamantopoulos (Hospital of Southern Norway, Kristiansand, Norway); Jan Sznajd (University of Jagiellonian, Poland); Elsa Azevedo (Centro Hospitalar de Sao Joao, Porto, Portugal); Ruth Geraldes (Hospital de Santa Maria, Lisbon, Portugal); Miguel Rodrigues (Hospital Garcia de Orta, Almada, Portugal); Ernestina Santos (Hospital Santo Antonio, Porto, Portugal); Yeong-Wook Song (Seoul National University Hospital, Republic of Korea); Sergey Moiseev (First Moscow State Medical University, Russia); Alojzija Hocevar (University Medical Centre Ljubljana, Slovenia); Maria Cinta Cid (Hospital Clinic de Barcelona, Spain); Xavier Solanich Moreno (Hospital de Bellvitge-Idibell, Spain); Inoshi Atukorala (University of Colombo, Sri Lanka); Ewa Berglin (Umeå University Hospital, Sweden); Aladdin Mohammed (Lund-Malmo University, Sweden); Mårten Segelmark (Linköping University, Sweden); Thomas Daikeler (University Hospital Basel, Switzerland); Haner Direskeneli (Marmara University Medical School, Turkey); Gulen Hatemi (Istanbul University, Cerrahpasa Medical School, Turkey); Sevil Kamali (Istanbul University, Istanbul Medical School, Turkey); Ömer Karadag (Hacettepe University, Turkey); Seval Pehlevan (Fatih University Medical Faculty, Turkey); Matthew Adler (Frimley Health NHS Foundation Trust, Wexham Park Hospital, UK); Neil Basu (NHS Grampian, Aberdeen Royal Infirmary, UK); Iain Bruce (Manchester University Hospitals NHS Foundation Trust, UK); Kuntal Chakravarty (Barking, Havering and Redbridge University Hospitals NHS Trust, UK); Bhaskar Dasgupta (Southend University Hospital NHS Foundation Trust, UK); Oliver Flossmann (Royal Berkshire NHS Foundation Trust, UK); Nagui Gendi (Basildon and Thurrock University Hospitals NHS Foundation Trust, UK); Alaa Hassan (North Cumbria University Hospitals, UK); Rachel Hoyles (Oxford University Hospitals NHS Foundation Trust, UK); David Jayne (Cambridge University Hospitals NHS Foundation Trust, UK); Colin Jones (York Teaching Hospitals NHS Foundation Trust, UK); Rainer Klocke (The Dudley Group NHS Foundation Trust, UK); Peter Lanyon (Nottingham University Hospitals NHS Trust, UK); Cathy Laversuch (Taunton & Somerset NHS Foundation Trust, Musgrove Park Hospital, UK); Raashid Luqmani, Joanna Robson (Nuffield Orthopaedic Centre, Oxford, UK); Malgorzata Magliano (Buckinghamshire Healthcare NHS Trust, UK); Justin Mason (Imperial College Healthcare NHS Trust, UK); Win Win Maw (Mid Essex Hospital Services NHS Trust, UK); Iain McInnes (NHS Greater Glasgow & Clyde, Gartnavel Hospital & GRI, UK); John Mclaren (NHS Fife, Whyteman’s Brae Hospital, UK); Matthew Morgan (University Hospitals Birmingham NHS Foundation Trust, Queen Elizabeth Hospital, UK); Ann Morgan (Leeds Teaching Hospitals NHS Trust, UK); Chetan Mukhtyar (Norfolk and Norwich University Hospitals NHS Foundation Trust, UK); Edmond O’Riordan (Salford Royal NHS Foundation Trust, UK); Sanjeev Patel (Epsom and St Helier University Hospitals NHS Trust, UK); Adrian Peall (Wye Valley NHS Trust, Hereford County Hospital, UK); Joanna Robson (University Hospitals Bristol NHS Foundation Trust, UK); Srinivasan Venkatachalam (The Royal Wolverhampton NHS Trust, UK); Erin Vermaak, Ajit Menon (Staffordshire & Stoke on Trent Partnership NHS Trust, Haywood Hospital, UK); Richard Watts (East Suffolk and North Essex NHS Foundation Trust, UK); Chee-Seng Yee (Doncaster and Bassetlaw Hospitals NHS Foundation Trust, UK); Daniel Albert (DartmouthHitchcock Medical Center, US); Leonard Calabrese (Cleveland Clinic Foundation, US); Sharon Chung (University of California, San Francisco, US); Lindsy Forbess (Cedars-Sinai Medical Center, US); Angelo Gaffo (University of Alabama at Birmingham, US); Ora Gewurz-Singer (University of Michigan, US); Peter Grayson (Boston University School of Medicine, US); Kimberly Liang (University of Pittsburgh, US); Eric Matteson (Mayo Clinic, US); Peter A. Merkel, Rennie Rhee (University of Pennsylvania, US); Jason Springer (University of Kansas Medical Center Research Institute, US); and Antoine Sreih (Rush University Medical Center, US).
Contributors All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. PAM had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study conception and design: CP, PCG, JCR, RS, AJ, AC, AH, RAW, PAM and RAL. Acquisition of data: CP, PCG, JCR, RS, AC, RAW, PAM and RAL. Analysis and interpretation of data: CP, PCG, JCR, RS, KBG, AJ, AC, SK, AH, RAW, PAM and RAL.
Funding The Diagnostic and Classification Criteria in Vasculitis (DCVAS) study, which included the development of this classification criteria, was funded by grants from the American College of Rheumatology (ACR), EULAR, the Vasculitis Foundation and the University of Pennsylvania Vasculitis Center. This study was also supported by the Intramural Research Program of the National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH.
Competing interests None declared.
Provenance and peer review Commissioned; internally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.