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1647
Ponte C, etal. Ann Rheum Dis 2022;81:1647–1653. doi:10.1136/ard-2022-223480
Criteria
2022 American College of Rheumatology/EULAR
classification criteria for giant cellarteritis
Cristina Ponte ,1,2 Peter C Grayson ,3 Joanna C Robson,4,5 Ravi Suppiah,6
Katherine Bates Gribbons,3 Andrew Judge ,7,8,9 Anthea Craven ,7 Sara Khalid,7
Andrew Hutchings ,10 Richard A Watts ,7,11 Peter A Merkel ,12
Raashid A Luqmani ,7 For the DCVAS Study Group
To cite: PonteC,
GraysonPC, RobsonJC,
etal. Ann Rheum Dis
2022;81:1647–1653.
►Additional supplemental
material is published online
only. To view, please visit
the journal online (http:// dx.
doi. org/ 10. 1136/ ard- 2022-
223480).
For numbered affiliations see
end of article.
Correspondence to
Professor Peter A Merkel,
Rheumatology, University of
Pennsylvania, Philadelphia, PA
19104, USA;
pmerkel@ upenn. edu
This article is published
simultaneously in Arthritis &
Rheumatology.
Received 13 October 2022
Accepted 13 October 2022
Published Online First
9November2022
© Author(s) (or their
employer(s)) 2022. No
commercial re- use. See rights
and permissions. Published
by BMJ.
ABSTRACT
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.
INTRODUCTION
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 claudi-
cation, scalp tenderness, visual disturbances and
elevated inflammatory markers.3
In 1990, the American College of Rheuma-
tology (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 diag-
nose GCA, and depending on the clinical setting,
a non- compressible ‘halo’ sign of a temporal±axil-
lary 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 classi-
fying 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 valida-
tion of the revised ACR/EULAR- endorsed classifi-
cation criteria for GCA.
METHODS
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 classifica-
tion criteria for GCA is located in online supple-
mental appendix 1. Briefly, the Steering Committee
implemented a six‐stage plan using data‐driven and
consensus methodology to develop the following
criteria.
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1648 Ponte C, etal. Ann Rheum Dis 2022;81:1647–1653. doi:10.1136/ard-2022-223480
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 prac-
tices. Patients were included if they were 18 years or older and
had a diagnosis of vasculitis or a condition that mimics vascu-
litis (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, arte-
rial 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 ultra-
sound, 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 propor-
tions: TAK, 33.5%; other vasculitides that mimic GCA and TAK
(isolated aortitis, primary central nervous system vasculitis, poly-
arteritis 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 oper-
ator (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 speci-
ficity 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 classifi-
cation 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 clas-
sification criteria in the validation data set. Performance charac-
teristics 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.
RESULTS
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. Infor-
mation 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
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1649
Ponte C, etal. Ann Rheum Dis 2022;81:1647–1653. doi:10.1136/ard-2022-223480
Criteria
Table 1 Demographic and disease features of the patients with
giant cell arteritis and the comparators*
GCA (n=756)
Comparators
(n=749)† P value
Age, mean±SD years 72.2 ± 8.5 44.6±18.0 <0.001
Female sex 511 (67.6) 447 (59.7) 0.001
Clinical features
Morning stiffness, neck/torso 88 (11.6) 15 (2.0) <0.001
Morning stiffness, shoulders/
arms
174 (23.0) 23 (3.1) <0.001
Sudden visual loss 102 (13.5) 29 (3.9) <0.001
Jaw claudication 356 (47.1) 19 (2.5) <0.001
Tongue claudication 21 (2.8) 1 (0.1) <0.001
New persistent temporal
headache
475 (62.8) 90 (12.0) <0.001
Scalp tenderness 260 (34.4) 25 (3.3) <0.001
Temporal artery abnormality
on vascular examination‡
354 (46.8) 35 (4.7) <0.001
Investigations
Maximum ESR ≥50 mm/hour 558 (73.8) 291 (38.9) <0.001
Maximum CRP ≥10 mg/L 683 (90.3) 445 (59.4) <0.001
Definitive vasculitis on
temporal artery biopsy
335 (44.3) 1 (0.1) <0.001
Halo sign on temporal artery
ultrasound
211 (27.9) 1 (0.1) <0.001
Bilateral axillary involvement
on imaging§
57 (7.5) 12 (1.6) <0.001
FDG‐PET activity throughout
aorta¶
52 (6.9) 9 (1.2) <0.001
*Except where indicated otherwise, values are the number (%).
†Diagnoses of comparators for the classification criteria for giant cell arteritis (GCA)
included Takayasu arteritis (n=251), Behçet’s disease (n=133), polyarteritis nodosa
(n=74), isolated aortitis (n=16), primary central nervous system vasculitis (n=16),
large‐vessel vasculitis (LVV) that could not be subtyped (n=9), other diseases that
mimic LVV (n=250).
‡Absent or diminished pulse, tenderness or hard ‘cord‐ like’ appearance.
§Defined as damage (ie, stenosis, occlusion or aneurysm) on angiography (CT, MR
or catheter based) or ultrasound, halo sign on ultrasound or abnormal FDG uptake
on PET.
¶Descending thoracic and abdominal aorta.
CRP, C reactive protein; ESR, erythrocyte sedimentation rate; FDG‐PET, 18F‐
fluorodeoxyglucose–positron emission tomography; GCA, giant cell arteritis.
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 avail-
able 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 require-
ment to classify GCA. Cluster analyses of vascular imaging data
identified bilateral axillary involvement and diffuse fluorodeox-
yglucose 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 regres-
sion 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 develop-
ment 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 supple-
mental 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 vari-
ables ‘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 crite-
rion 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 clas-
sification 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 speci-
ficity 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
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Criteria
Figure 1 The final 2022 American College of Rheumatology/EULAR Classification Criteria for Giant Cell Arteritis.
Table 2 Performance characteristics of the 2022 ACR/EULAR classification criteria for giant cell arteritis*
Patient subset Total no patients (no GCA patients) Sensitivity (95% CI) Specificity (95% CI) AUC (95% CI)
Development data set 1054 (518) 84.8 (81.4 to 87.7) 95.0 (92.8 to 96.7) 0.90 (0.88 to 0.92)
Validation data set 451 (238) 87.0 (82.0 to 91.0) 94.8 (91.0 to 97.4) 0.91 (0.88 to 0.94)
Biopsy‐proven GCA† 1104 (355) 100.0 (99.0 to 100.0) 94.9 (93.1 to 96.4) 0.97 (0.97 to 0.98)
Large‐vessel GCA‡ 873 (124) 55.7 (46.5 to 64.6) 94.9 (93.1 to 96.4) 0.75 (0.71 to 0.80)
*Performance characteristics were tested in the subsets using the combined development and validation data sets to maximise sample size.
†Definite vasculitis on temporal artery biopsy (TAB).
‡Involvement of the aorta or its branch arteries on imaging, without vasculitis on TAB.
ACR, American College of Rheumatology; AUC, area under the curve; GCA, giant cell arteritis.
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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 sensi-
tivity 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 supple-
mental appendix 21).
DISCUSSION
Presented here are the final 2022 ACR/EULAR GCA classifi-
cation 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 vali-
dation 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 speci-
ficity values calculated in the development set were very similar,
providing reassurance that the statistical methods avoided over-
fitting 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 vascu-
litis 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 infec-
tion 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 clas-
sify 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 ultra-
sound was not performed in all patients with suspected GCA,
etc). However, this reflects existing differences in clinical prac-
tice, 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 ‘defini-
tive 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 underrepre-
sented 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.
Author affiliations
1Department of Rheumatology, Centro Hospitalar Universitário Lisboa Norte, Centro
Académico de Medicina de Lisboa, Lisbon, Portugal
2Rheumatology Research Unit, Instituto de Medicina Molecular, Faculdade de
Medicina, Universidade de Lisboa, Centro Académico de Medicina de Lisboa, Lisbon,
Portugal
3Systemic Autoimmunity Branch, National Institute of Arthritis and Musculoskeletal
and Skin Diseases, NIH, Bethesda, Maryland, USA
4Centre for Health and Clinical Research, University of the West of England, Bristol,
UK
5Rheumatology Department, University Hospitals Bristol and Weston NHS Foundation
Trust, Bristol, UK
6Te Whatu Ora - Health New Zealand, Auckland, New Zealand
7Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences,
Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
8Musculoskeletal Research Unit, Translational Health Sciences, Bristol Medical School,
University of Bristol, Bristol, UK
on December 29, 2022 by Cristina Ponte. Protected by copyright.http://ard.bmj.com/Ann Rheum Dis: first published as 10.1136/ard-2022-223480 on 9 November 2022. Downloaded from
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Criteria
9National Institute for Health Research Bristol Biomedical Research Centre, University
Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol,
UK
10Department of Health Services Research and Policy, London School of Hygiene and
Tropical Medicine, London, UK
11Norwich Medical School, University of East Anglia, Norwich, UK
12Division of Rheumatology, Department of Medicine, and Division of Epidemiology,
Department of Biostatistics, Epidemiology, and Informatics, University of
Pennsylvania, Philadelphia, Pennsylvania, USA
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.
Acknowledgements We acknowledge the patients and clinicians who provided
data to the DCVAS project.
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.
Patient consent for publication Not applicable.
Ethics approval Ethical approval was obtained from local ethics committees.
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.
on December 29, 2022 by Cristina Ponte. Protected by copyright.http://ard.bmj.com/Ann Rheum Dis: first published as 10.1136/ard-2022-223480 on 9 November 2022. Downloaded from
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Ponte C, etal. Ann Rheum Dis 2022;81:1647–1653. doi:10.1136/ard-2022-223480
Criteria
ORCID iDs
CristinaPonte http://orcid.org/0000-0002-3989-1192
Peter CGrayson http://orcid.org/0000-0002-8269-9438
AndrewJudge http://orcid.org/0000-0003-3015-0432
AntheaCraven http://orcid.org/0000-0001-9477-7889
AndrewHutchings http://orcid.org/0000-0003-0215-9923
Richard AWatts http://orcid.org/0000-0002-2846-4769
Peter AMerkel http://orcid.org/0000-0001-9284-7345
Raashid ALuqmani http://orcid.org/0000-0002-4446-5841
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SUPPLEMENTARY MATERIAL
2022 AMERICAN COLLEGE OF RHEUMATOLOGY AND EULAR CLASSIFICATION CRITERIA FOR
LARGE-VESSEL VASCULITIS [GIANT CELL ARTERITIS AND TAKAYASU ARTERITIS]
1. Detailed description of the research methods for the development of classification
criteria for giant cell arteritis and Takayasu arteritis
2. Diagnosis and Classification of Vasculitis Study case report form
3. Diagnosis and Classification of Vasculitis Study sites and investigators
4. Diagnosis and Classification of Vasculitis Study Sites/Investigators characteristics
5. Study participant details
6. Expert reviewer characteristics
7. Names of expert panel reviewers
8. Example of a clinical vignette extracted from the case report form
9. Flow chart of expert review process to create the Diagnosis and Classification of
Vasculitis Study dataset for large-vessel vasculitis
10. Age distribution in giant cell arteritis and Takayasu arteritis
11. Frequency of arterial territory involvement in giant cell arteritis and Takayasu
arteritis
12. Patterns of vascular involvement in giant cell arteritis and Takayasu arteritis
13. Final candidate items used within each regression analysis to derive classification
criteria for giant cell arteritis and Takayasu arteritis
14. Mimics used to develop the giant cell arteritis and Takayasu arteritis criteria
15. Results of regression analysis for giant cell arteritis and Takayasu arteritis
16. Cluster distribution plots of giant cell arteritis and Takayasu arteritis, temporal
artery biopsy results, presence of halo sign on temporal artery ultrasound, large-
vessel involvement in imaging
17. Data-driven and clinically-selected models for giant cell arteritis and Takayasu
arteritis with associated risk scored based off beta coefficient weighting
18. Performance characteristics of a points-based risk score for giant cell arteritis and
Takayasu arteritis with different thresholds
19. Discrimination curves for the classification criteria of giant cell arteritis and
Takayasu arteritis
20. Performance characteristics of the 2022 ACR-EULAR and the 1990 ACR
classification criteria for giant cell arteritis and Takayasu arteritis in the complete
DCVAS database (development and validation datasets)
21. Age in the new classification criteria – the 50-60 years interval
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Supplementary Materials 1. Detailed description of the research methods for the
development of classification criteria for giant cell arteritis
and Takayasu arteritis
An international Steering Committee comprised of clinician investigators with expertise in
vasculitis, statisticians, and data managers was established to oversee the overall Diagnostic
and Classification Criteria in Vasculitis (DCVAS) project. The Steering Committee established
a six-stage plan using data-driven and consensus methodology to develop the criteria for six
systemic vasculitides: three small-vessel vasculitides (granulomatosis with polyangiitis
[GPA], microscopic polyangiitis [MPA], and eosinophilic granulomatosis with polyangiitis
[EGPA]), a medium-vessel vasculitis (PAN), and two large-vessel vasculitides (giant cell
arteritis [GCA] and Takayasu arteritis [TAK]). A flow chart depicting an overview of each
stage of the methodology used to develop classification criteria for GCA and TAK is listed
below.
STAGE FIVE.
Derivation of the classification criteria for GCA and TAK
STAGE ONE
Generation of an extensive list of candidate items and subsequent creation of the Case
Report Form: >1000 clinical, laboratory, pathology, and imaging data elements
STAGE TWO
DCVAS International prospective multisite observational study of patients with recently
diagnosed vasculitis or mimics of vasculitis
STAGE FOUR
Data reduction of candidate items
Data from DCVAS study on frequency of
items across LVV subtypes used as basis for
data-driven consensus
Final 72 candidate items
STAGE THREE
Cases from DCVAS turned into clinical vignettes
External expert panel review as diagnostic gold
standard
Final 2068 cases
METHODOLOGY FOR THE DEVELOPMENT OF CLASSIFICATION CRITERIA
FOR THE LARGE-VESSEL VASCULITIDES: GCA and TAK
STAGE SIX.
Validation of the classification criteria for GCA and TAK in independent datasets
DCVAS: Diagnostic and Classification Criteria in Vasculitis; GCA: giant cell arteritis; LVV: large-vessel
vasculitis; TAK: Takayasu arteritis
Stage One: Generation of candidate classification items for the systemic vasculitides
Candidate items were generated by expert opinion including items from the 1990 ACR
Classification Criteria, the 2012 Chapel Hill Nomenclature, and the major disease activity and
damage indices for vasculitis [1–7]. Items were categorized as demographic, symptoms,
physician-observed findings, laboratory tests, diagnostic radiology, and biopsy results.
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Candidate items were reviewed and discussed at a major international vasculitis conference,
and nominal group technique was used to modify the potential list of items with input from
vasculitis experts across a range of specialties. The full list of items was then reviewed by
the Steering Committee to address potential omissions or redundancy in the list with
appropriate revisions made. A list of data elements was finalized by the Steering Committee
for use in prospective data collection in Stage Two. The resulting DCVAS case report form
(CRF) is shown in Supplementary Materials 2.
Stage Two: DCVAS prospective observational study
The DCVAS study is an international prospective multisite observational study of patients
recently diagnosed with vasculitis or mimics of vasculitis [8].
The University of Oxford sponsored the study and overall ethical approval was given by the
UK Berkshire Research Ethics Committee (reference 10/H0505/19) on 7 May 2010.The study
was performed in accordance with the 1964 Declaration of Helsinki. Additional ethical
approval was obtained by national and local ethics committees in accordance with national
legislation.
Site Selection
A wide range of sites were targeted for inclusion to ensure representation from different
geographical regions, clinical specialties, and types of sites (including both academic and
non-academic clinical practices). To increase the number and types of study sites, the
DCVAS study was promoted through national and international presentations, and the
DCVAS website (Supplementary Materials 3 & 4).
Patient Recruitment
Inclusion criteria:
1) Patients aged ≥18 years; 2) Ability to give informed consent or consent via an appropriate
surrogate; 3) i) Diagnosis as made by the submitting clinician within the previous two years
of GPA, MPA, EGPA, other ANCA-associated vasculitis, GCA, anti-glomerular basement
membrane disease, cryoglobulinemic vasculitis, Behçet’s disease, primary central nervous
system vasculitis, IgA vasculitis, isolated aortitis, other large-vessel vasculitis (LVV), or a
diagnosis within the previous five years of PAN or TAK; OR ii) Diagnosis as made by the
submitting clinician within the previous two years of a condition which mimics systemic
vasculitis, e.g., infection, tumor, other inflammatory conditions (see Supplementary
Materials 5 for the complete details of physician-submitted diagnoses).
Exclusion criteria:
1) Patients < 18 years of age; 2) Inability to provide informed consent.
Data Collection
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Paper and web-based versions of the CRF were used (Supplementary Materials 2). Data
from patients with a working diagnosis of systemic vasculitis or mimics of systemic vasculitis
were entered. The diagnosis and level of certainty for diagnosis was requested from the
submitting physician at time of diagnosis. For patients with vasculitis who were enrolled in
the DCVAS study within six months of the initial diagnosis, the submitting physician was
asked to confirm the accuracy of the diagnosis at the six-month time point in a separate
study form. Data from all study participants was reviewed at a central location for
completeness. Local investigators were contacted to resolve and data discrepancies.
Stage Three: Expert panel methodology to derive a gold standard-defined set of cases of
large-vessel vasculitis
An online independent Expert Review Process was used to minimize investigator bias and to
avoid the circularity of applying a previously derived gold standard such as the 1990 ACR
Criteria [2]. Experts in vasculitis from a wide range of geographical locations and specialties
were invited to review cases submitted to DCVAS (see Supplementary Materials 6 & 7 for
the expert reviewer characteristics). External experts reviewed approximately 50 cases
each, blinded to the submitting physician’s diagnosis. The review process took place over
two time periods. In 2016, primarily cases of AAV, with a smaller fraction of LVV cases (233
cases, 8.1% of total number of cases), were reviewed. In 2018, 1596 cases of LVV (74.9% of
total number of cases) were reviewed.
Clinical vignettes of each case, including clinical, laboratory, imaging, and biopsy results
were produced using data from the CRFs and presented in a standard clinical vignette
format (Supplementary Materials 8). All cases labeled GCA, TAK, or a different form of LVV
by the submitting physician were reviewed. To ensure a rigorous process, in the 2018 review
25.1% of cases with a submitting physician diagnosis of other vasculitides (6.1%), or a
condition mimicking vasculitis (19.0%) were also randomly included for expert review.
For each case vignette, the expert reviewer indicated:
(i) whether or not the diagnosis was vasculitis
(ii) which category of vasculitis was present, based on vessel size (small, medium, large,
or no predominant size)
(iii) if a category was chosen in (ii) then which subtype of vasculitis was present (for
example, if LVV was selected, then a choice of GCA, TAK, isolated aortitis, or
uncertain sub-type was provided)
Reviewers were asked about their certainty for each of (i)-(iii) as follows: very certain,
moderately certain, uncertain, or very uncertain.
A case was considered to be agreed in full if the Expert Reviewer’s assessment matched the
submitting physician’s assessment at each level, with at least moderate certainty. Cases that
were not agreed on expert review were submitted for a blinded second review by a member
of the Steering Committee. If the Steering Committee member agreed with either the
submitting physician’s assessment or the initial expert reviewer with moderate certainty,
then the case was agreed upon in full. Cases that were not agreed upon in full were rejected
from further analysis. A flow diagram depicting the results from the expert review process is
provided in Supplementary Materials 9.
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Stage Four: Refinement of candidate items specifically for large-vessel vasculitis
The DCVAS CRF included > 1000 data elements. The final statistical analysis to create
classification criteria for LVV required approximately 100 predictors to avoid over-fitting of
the final models during regression analysis [9]. Using a series of data-driven and consensus
approaches, the number of candidate items was reduced, and specific items were further
defined as necessary.
Age as a Classifier
Since age is a key differentiator between forms of LVV, distribution of age at symptom and
diagnosis was plotted for GCA and TAK to determine whether specific age thresholds should
be regarded as absolute requirements for disease classification (Supplementary Materials
10).
Vascular Physical Examination Findings
The elements of the vascular physical examination considered as candidate items were
diminished or absent pulse, bruits, blood pressure asymmetry and arterial tenderness.
Given the clinical challenge of accurately localizing a bruit to a specific arterial territory,
presence of “any bruit” was considered as a candidate item [10]. Given the increased
prevalence of vascular pathology in arterial territories above the diaphragm in LVV, vascular
pulse abnormalities were studied separately in the upper and lower extremities. Carotidynia
is a specific feature for TAK [11], and temporal artery abnormalities are specific for GCA.
Consequently, vascular examination findings related to the temporal arteries (diminished or
absent pulse, tenderness, or hard ‘cord-like’) and to the carotid arteries (diminished or
absent pulse, or tenderness / carotidynia) were considered independently. Blood pressure
readings were only recorded for the upper extremities in the database. Difference in systolic
blood pressure in the upper extremities was categorized at 10 mmHg intervals. A difference
in systolic blood pressure of 20 mmHg maximized sensitivity and specificity between TAK
and GCA and was selected for further analysis.
Vascular Imaging Findings
Investigators recorded vascular imaging findings (luminal and wall abnormalities) detected
by vascular ultrasound, angiography (computed tomography [CT], magnetic resonance
[MR], or catheter-based), or positron emission tomography (PET). Temporal artery
abnormalities documented by vascular ultrasound were stenosis, occlusion, wall thickening,
and halo sign. However, halo sign was the only temporal artery ultrasound finding included
for subsequent analyses, given its high specificity to diagnose GCA in comparison to other
ultrasonographic abnormalities [12,13]. Increased fluorodeoxyglucose (FDG) uptake in
specific arterial territories, as determined by the submitting physician, was recorded for the
PET studies. Eleven territories related to the large arteries were evaluated: bilateral carotid,
subclavian, axillary, renal, and mesenteric arteries; and thoracic and abdominal aorta. Given
the lack of clear definitions to define vascular wall abnormalities (e.g. wall thickness) and
the lack of specificity of these findings in comparison to other conditions such as
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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atherosclerosis [14], only findings of luminal damage (i.e. stenosis, occlusion, aneurysm)
detected by angiography (i.e. computed tomography, magnetic resonance, or catheter-
based angiography) or ultrasound were considered in the large arteries. Frequency of
luminal damage was compared in each vascular territory between TAK and GCA
(Supplementary Materials 11).
Prevalence of symmetric involvement of paired branch arteries (e.g., right and left
subclavian artery) was evaluated. K-means cluster analysis of vascular imaging was
performed to identify distinctive patterns of large vessel involvement in GCA and TAK, as
previously reported [15] . (Supplementary Materials 12).
Vascular Biopsy Findings
Biopsy findings of the temporal artery and other arterial sites were recorded. Data from 784
temporal artery biopsies was collected. Other than the temporal arteries, there were too
few biopsies of other arterial territories to consider in subsequent analysis (aorta = 6; other
artery = 19).
Temporal artery biopsy findings were not subject to central review. Instead, the submitting
physician provided information about histopathologic interpretation of biopsy findings
which were reported as normal, non-diagnostic, consistent with vasculitis but not definite,
or definite vasculitis. Specific histopathologic findings (e.g., giant cells, granuloma, etc.) were
recorded at the discretion of the submitting physician. Histopathologic interpretation
without details of accompanying histopathologic features were reported for 151 patients
(19%). Consequently, a positive temporal artery biopsy for all subsequent analyses was
defined as histopathologic interpretation of definite vasculitis by the local submitting
physician. Specific histopathologic criteria do not exist to define “definite vasculitis” by a
temporal artery biopsy. Presence of giant cells, mononuclear leukocyte infiltration, and
fragmentation of the internal elastic lamina were independently associated with
histopathologic interpretation of definite vasculitis in the DCVAS cohort [16]. These features
can be used as a guide to inform the definition of a positive temporal artery biopsy when
the criteria are applied in clinical practice.
Laboratory Values
The maximum value of erythrocyte sedimentation rate (ESR) and c-reactive protein (CRP)
were recorded in the DCVAS CRF as continuous variables. Fractional polynomial regression
was used to check the assumption of a linear relationship of ESR and CRP with the panel-
reviewed diagnosis as the outcome variable. Since there was evidence of non-linearity for
both ESR and CRP, these variables were categorized into five groups with cut-points based
on plots from fractional polynomial regression models [17]. Threshold values of ESR ≥
50mm/hr and CRP ≥ 10mg/L were chosen by Steering Committee based on optimization of
model fit and ease of clinical application. Other laboratory variables of interest were
recorded in the DCVAS CRF only as categorical variables (e.g., anemia (hemoglobin <
10g/dL); thrombocythemia (platelets > 500 x109/L); and leukocytosis (white blood cell count
> 15.0 x109/L).
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Reduction of Candidate Items
A data-driven process was used to retain candidate items of relevance to cases and
comparators for LVV. Seven members of the DCVAS steering committee (JR, RW, RS, RL, PM,
PG, CP) were split into groups of two, and each group reviewed all variables within an
assigned domain: clinical symptoms, vascular examination, laboratory, biopsy, and vascular
imaging. Data on frequency of items was prepared for review from cases of GCA and TAK
from the DCVAS dataset. Items were selected for exclusion if they had i) prevalence of <5%
within the data set and/or ii) they were non-clinically relevant for classification criteria (e.g.,
related to infection, malignancy, or demography). Low-frequency items of clinical
importance could be combined, when appropriate. Consensus on final items to include for
the next phase of analysis was reached between the two independent steering committee
members, and then within the wider steering committee. The final list of candidate
predictors used in the next stage of data analysis is listed in Supplementary Materials 13.
Stage Five: Derivation of classification criteria for giant cell arteritis and Takayasu
arteritis
The DCVAS dataset was split into development (70%) and validation (30%) datasets. A
larger development dataset was chosen to maximize the potential to identify the best
model. Comparisons were performed between cases defined in Stage Three as either having
the diagnosis of GCA, or diagnosis of TAK, other vasculitis that mimic GCA and TAK (isolated
aortitis, primary central nervous system vasculitis, PAN, Behçet’s disease, and other LVV), or
other diagnosis that mimic LVV (e.g., headache, atherosclerosis - Supplementary Materials
14). This process resulted in generation of a binary outcome variable (LVV sub-type or
comparators). To ensure balance in the sample (50% cases vs. 50% controls) for outcome
definition, the following splits were made, giving equal weighting to the three types of
controls: GCA (50%) vs. TAK (16.6%), other vasculitis (16.6%), and other diagnosis that mimic
LVV (16.6%); or TAK (50%) vs. GCA (16.6%), other vasculitis (16.6%), and other diagnosis that
mimic LVV (16.6%).
The candidate predictors from Stage Four were included in a logistic regression model.
Fractional polynomial regression modeling was used to assess evidence of linearity with
outcome for continuous predictor variables [17]. Multiple imputation was used to overcome
potential bias from missing data [18]. LASSO (least absolute shrinkage and selection
operator) logistic regression was used to identify predictors from the dataset and create a
parsimonious model including only the most important predictors [9,19,20]. To extract the
non-zero coefficients and, therefore, the significant predictors, a single model was fitted
and adjusted for all potential variables with a 10-fold cross-validation and the minimum
average mean-squared error (Supplementary Materials 15 & 16).
The reduced item model was tested for discrimination, area under the curve (AUC)
sensitivity and specificity. This was an iterative process within the Steering Committee, with
the clinician researchers and expert biostatisticians working collaboratively, to ensure face
and content validity and acceptability of the resultant criteria.
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 [21] (Supplementary
Materials 17). A threshold was identified for classification, which best balanced sensitivity
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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and specificity (Supplementary Materials 18 & 19). Absolute age requirements were
imposed to cases and comparators as the final step of classification.
Stage Six: Validation of the final classification criteria for giant cell arteritis and
Takayasu arteritis in an independent dataset
The performance characteristics of the final criteria were tested in an independent dataset
of cases and comparators. These are the official final values that should be quoted when
referring to the criteria.
Comparisons were made between the measurement properties of the new classification
criteria for GCA and TAK and the respective 1990 ACR Classification Criteria for GCA and TAK
using data from the validation datasets (Supplementary Material 20). Because an aim of
the project was to develop criteria that were derived from an international dataset, the
performance characteristics of the new criteria were tested in different regions of the world
using pooled data from the development and validation datasets to maximize sample size
for the subgroups. Since patients diagnosed with LVV between the ages of 50-60 years can
be particularly difficult to classify, the performance characteristics of the GCA and TAK
classification criteria were tested for all patients diagnosed with GCA or TAK in this age
range (Supplementary Material 21).
Additional Acknowledgements:
The DCVAS study recognizes the contributions of Joe Barrett, David T. Gray, Marian
Montgomery, Ann-Marie Morgan, and Joe Rosa from the Oxford study team for efforts to
design and implement the DCVAS database and clinical vignettes/expert panel review,
perform quality control of submitted data elements, and communicate with participating
sites.
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Supplementary Materials 2. DCVAS case report form
See separate PDF file titled “DCVAS case report form”
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Supplementary Materials 3: Diagnosis and Classification of Vasculitis Study (DCVAS)
sites and investigators
Country
Investigator
Participating Center
Australia
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
Austria
Andreas Kronbichler
Medical University Innsbruck
Belgium
Daniel Blockmans
University Hospitals Leuven
Canada
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
China
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
Czech Republic
Vladimir Tesar
General University Hospital, Prague
Denmark
Bo Baslund
University Hospital, Copenhagen (Rigshospitalet)
Egypt
Nevin Hammam
Assiut University
Egypt
Amira Shahin
Cairo University
Finland
Laura Pirila
Turku University Hospital
Finland
Jukka Putaala
Helsinki University Central Hospital
Germany
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
Hungary
Zoltan Szekanecz
University of Debrecen Medical and Health
Science Center
India
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
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
14
Country
Investigator
Participating Center
India
Aman Sharma
Postgraduate Institute of Medical Education and
Research, Chandigarh
India
Shrikant Wagh
Jehangir Clinical Development Centre, Pune (IP)
Ireland
Michael Clarkson
Cork University Hospital
Ireland
Eamonn Molloy
St. Vincent's University Hospital, Dublin
Italy
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
Japan
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
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)
Malaysia
Kan Sow Lai
Penang General Hospital
Mexico
Luis Felipe Flores-
Suarez
Instituto Nacional de Enfermedades
Respiratorias, Mexico City
Mexico
Andrea Hinojosa-
Azaola
Instituto Nacional de Ciencias Médicas y
Nutrición Salvador Zubirán, Mexico City
Netherlands
Bram Rutgers
University Hospital Groningen
Netherlands
Paul-Peter Tak
Academic Medical Centre, University of
Amsterdam
New Zealand
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
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Country
Investigator
Participating Center
Norway
Emilio Besada
Tromsø, Northern Norway
Norway
Andreas
Diamantopoulos
Hospital of Southern Norway, Kristiansand
Poland
Jan Sznajd
University of Jagiellonian
Portugal
Elsa Azevedo
Centro Hospitalar de São João, Porto
Portugal
Ruth Geraldes
Hospital de Santa Maria, Lisbon
Portugal
Miguel Rodrigues
Hospital Garcia de Orta, Almada
Portugal
Ernestina Santos
Hospital Santo Antonio, Porto
Republic of Korea
Yeong-Wook Song
Seoul National University Hospital
Russia
Sergey Moiseev
First Moscow State Medical University
Slovenia
Alojzija Hočevar
University Medical Centre Ljubljana
Spain
Maria Cinta Cid
Hospital Clinic de Barcelona
Spain
Xavier Solanich
Moreno
Hospital de Bellvitge-Idibell
Sri Lanka
Inoshi Atukorala
University of Colombo
Sweden
Ewa Berglin
Umeå University Hospital
Sweden
Aladdin Mohammed
Lund-Malmo University
Sweden
Mårten Segelmark
Linköping University
Switzerland
Thomas Daikeler
University Hospital Basel
Turkey
Haner Direskeneli
Marmara University Medical School
Turkey
Gulen Hatemi
Istanbul University, Cerrahpasa Medical School
Turkey
Sevil Kamali
Istanbul University, Istanbul Medical School
Turkey
Ömer Karadağ
Hacettepe University
Turkey
Seval Pehlevan
Fatih University Medical Faculty
United Kingdom
Matthew Adler
Frimley Health NHS Foundation Trust, Wexham
Park Hospital,
United Kingdom
Neil Basu
NHS Grampian, Aberdeen Royal Infirmary
United Kingdom
Iain Bruce
Manchester University Hospitals NHS Foundation
Trust
United Kingdom
Kuntal Chakravarty
Barking, Havering and Redbridge University
Hospitals NHS Trust
United Kingdom
Bhaskar Dasgupta
Southend University Hospital NHS Foundation
Trust
United Kingdom
Oliver Flossmann
Royal Berkshire NHS Foundation Trust
United Kingdom
Nagui Gendi
Basildon and Thurrock University Hospitals NHS
Foundation Trust
United Kingdom
Alaa Hassan
North Cumbria University Hospitals
United Kingdom
Rachel Hoyles
Oxford University Hospitals NHS Foundation Trust
United Kingdom
David Jayne
Cambridge University Hospitals NHS Foundation
Trust
United Kingdom
Colin Jones
York Teaching Hospitals NHS Foundation Trust
United Kingdom
Rainer Klocke
The Dudley Group NHS Foundation Trust
United Kingdom
Peter Lanyon
Nottingham University Hospitals NHS Trust
United Kingdom
Cathy Laversuch
Taunton & Somerset NHS Foundation Trust,
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Country
Investigator
Participating Center
Musgrove Park Hospital
United Kingdom
Raashid Luqmani/
Joanna Robson
Nuffield Orthopaedic Centre, Oxford
United Kingdom
Malgorzata Magliano
Buckinghamshire Healthcare NHS Trust
United Kingdom
Justin Mason
Imperial College Healthcare NHS Trust
United Kingdom
Win Win Maw
Mid Essex Hospital Services NHS Trust
United Kingdom
Iain McInnes
NHS Greater Glasgow & Clyde, Gartnavel Hospital
& GRI
United Kingdom
John Mclaren
NHS Fife, Whyteman's Brae Hospital
United Kingdom
Matthew Morgan
University Hospitals Birmingham NHS Foundation
Trust, Queen Elizabeth Hospital
United Kingdom
Ann Morgan
Leeds Teaching Hospitals NHS Trust
United Kingdom
Chetan Mukhtyar
Norfolk and Norwich University Hospitals NHS
Foundation Trust
United Kingdom
Edmond O'Riordan
Salford Royal NHS Foundation Trust
United Kingdom
Sanjeev Patel
Epsom and St Helier University Hospitals NHS
Trust
United Kingdom
Adrian Peall
Wye Valley NHS Trust, Hereford County Hospital
United Kingdom
Joanna Robson
University Hospitals Bristol NHS Foundation Trust
United Kingdom
Srinivasan
Venkatachalam
The Royal Wolverhampton NHS Trust
United Kingdom
Erin Vermaak / Ajit
Menon
Staffordshire & Stoke on Trent Partnership NHS
Trust, Haywood Hospital
United Kingdom
Richard Watts
East Suffolk and North Essex NHS Foundation
Trust
United Kingdom
Chee-Seng Yee
Doncaster and Bassetlaw Hospitals NHS
Foundation Trust
United States
Daniel Albert
Dartmouth-Hichcock Medical Center
United States
Leonard Calabrese
Cleveland Clinic Foundation
United States
Sharon Chung
University of California, San Francisco
United States
Lindsy Forbess
Cedars-Sinai Medical Center
United States
Angelo Gaffo
University of Alabama at Birmingham
United States
Ora Gewurz-Singer
University of Michigan
United States
Peter Grayson
Boston University School of Medicine
United States
Kimberly Liang
University of Pittsburgh
United States
Eric Matteson
Mayo Clinic
United States
Peter A. Merkel
Rennie Rhee
Antoine Sreih
University of Pennsylvania
United States
Jason Springer
University of Kansas Medical Center Research
Institute
United States
Antoine Sreih
Rush University Medical Center
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
18
Supplementary Materials 4. Diagnosis and Classification of Vasculitis Study Sites/Investigators characteristics
Characteristics
N=136 (%)
Characteristics
N=136 (%)
Characteristics
N=136 (%)
Country
Country
Specialty
Australia
3 (2.2)
Norway
2 (1.5)
Rheumatology
99 (72.8)
Austria
1 (0.7)
Poland
1 (0.7)
Nephrology
21 (15.4)
Belgium
1 (0.7)
Portugal
4 (2.9)
Neurology
5 (3.7)
Canada
8 (5.9)
Republic of Korea
1 (0.7)
Internal Medicine
4 (2.9)
China
4 (2.9)
Russia
1 (0.7)
Immunology
4 (2.9)
Czech Republic
1 (0.7)
Slovenia
1 (0.7)
Dermatology
2 (1.5)
Denmark
1 (0.7)
Spain
2 (1.5)
Respiratory
1 (0.7)
Egypt
2 (1.5)
Sri Lanka
1 (0.7)
Finland
2 (1.5)
Sweden
3 (2.2)
Years within specialty
Germany
6 (4.4)
Switzerland
1 (0.7)
0-5
0 (0.0)
Hungary
1 (0.7)
Turkey
5 (3.7)
6-10
15 (11.0)
India
6 (4.4)
United Kingdom
31 (22.8)
11-15
22 (16.2)
Ireland
2 (1.5)
United States of America
12 (8.8)
16-20
21 (15.4)
Italy
4 (2.9)
>20
48 (35.3)
Japan
20 (14.7)
Background
Unknown
30 (22.1)
Malaysia
1 (0.7)
Academic hospital/
89 (65.4)
Mexico
2 (1.5)
Medical school
Sex of primary investigator
Netherlands
2 (1.5)
Non-academic hospital
17 (12.5)
Male
99 (72.8)
New Zealand
4 (2.9)
Unknown
30 (22.1)
Female
37 (27.2)
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Supplementary Materials 6. Expert reviewer characteristics
6A. Expert reviewer characteristics - 2016 review
6B. Expert reviewer characteristics - 2018 review
Characteristics
N=55 (%)
Characteristics
N=55 (%)
Country
Specialty
Australia
1 (1.8)
Rheumatology
33 (60.0)
Canada
3 (5.5)
Nephrology
11 (20.0)
Czech Republic
2 (3.6)
Internal Medicine
4 (7.3)
Denmark
1 (1.8)
Immunology
3 (5.5)
Egypt
1 (1.8)
Dermatology
2 (3.6)
France
1 (1.8)
Neurology
1 (1.8)
Germany
7 (12.7)
Pathology
1(1.8)
India
2 (3.6)
Ireland
2 (3.6)
Years in specialty
Italy
3 (5.5)
0-5
2 (3.6)
Japan
2 (3.6)
6-10
11 (20.0)
Mexico
2 (3.6)
11-15
13 (23.6)
Netherlands
2 (3.6)
16-20
9 (16.4)
New Zealand
1 (1.8)
>20
19 (34.5)
Portugal
2 (3.6)
Unknown
1 (1.8)
Russia
2 (3.6)
Slovenia
1 (1.8)
Sex
Spain
1 (1.8)
Male
38 (69.1)
Switzerland
2 (3.6)
Female
17 (30.9)
Turkey
2 (3.6)
United Kingdom
6 (10.9)
Background
United States of America
9 (16.4)
Clinician
11 (20.0)
Clinician and researcher
44 (80.0)
Characteristics
N=56 (%)
Characteristics
N=56 (%)
Country
Specialty
Australia
1 (1.8)
Rheumatology
42 (75.0)
Austria
1 (1.8)
Internal Medicine
5 (8.9)
Belgium
1 (1.8)
Immunology
3 (5.4)
Canada
3 (5.4)
Nephrology
2 (3.6)
Czech Republic
1 (1.8)
Neurology
2 (3.6)
Denmark
4 (7.1)
Dermatology
1 (1.8)
Egypt
1 (1.8)
Pathology
1 (1.8)
France
2 (3.6)
Germany
8 (14.3)
Years in specialty
Iceland
1 (1.8)
0-5
1 (1.8)
India
2 (3.6)
6-10
16 (28.6)
Ireland
1 (1.8)
11-15
16 (28.6)
Italy
6 (10.7)
16-20
7 (12.5)
Japan
1 (1.8)
>20
16 (28.6)
Mexico
2 (3.6)
Netherlands
1 (1.8)
Sex
Norway
1 (1.8)
Male
40 (71.4)
Poland
1 (1.8)
Female
16 (28.6)
Portugal
2 (3.6)
Russia
1 (1.8)
Background
Slovenia
1 (1.8)
Clinician
9 (16.1)
Spain
2 (3.6)
Clinician and researcher
47 (83.9)
Switzerland
2 (3.6)
Turkey
2 (3.6)
United Kingdom
1 (1.8)
United States of America
7 (12.5)
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Supplementary Materials 7. Expert panel reviewers
7A. List of expert panel reviewers - 2016 review
7B. List of expert panel reviewers - 2018 review
Alba, Marco
Gewurz-Singer, Ora
Khalidi, Nader
Quincey, Vicki
Barra, Lillian
Guillevin, Loïc
Lamprecht, Peter
Rajasekhar, Liza
Baslund, Bo
Hammam, Nevin
Langford, Carol
Salama, Alan
Basu, Neil
Hauser, Thomas
Little, Mark
Salvarani, Carlo
Brown, Nina
Hellmich, Bernhard
Macieira, Carla
Schmidt, Wolfgang
Cid, Maria
Henes, Jörg
Matsui, Kazuo
Sharma, Aman
Daikeler, Thomas
Hinojosa-Azaola, Andrea
Matteson, Eric
Smith, Rona
Direskeneli, Haner
Hočevar, Alojzija
Micheletti, Robert
Springer, Jason
Emmi, Giamoco
Holle, Julia
Milman, Nataliya
Sunderkötter, Cord
Flores-Suárez, Luis Felipe
Hruskova, Zdenka
Moiseev, Sergey
Sznajd, Jan
Fujimoto, Shouichi
Jayne, David
Molloy, Eamonn
Teng, Yko
Gatenby, Paul
Jennette, Charles
Monach, Paul
Tesar, Vladimir
Geetha, Duvuru
Kallenberg, Cees
Neumann, Thomas
Vaglio, Augusto
Geraldes, Ruth
Karadağ, Ömer
Novikov, Pavel
Alba, Marco
Direskeneli, Haner
Hočevar, Alojzija
Nielsen, Berit
Barra, Lillian
Duftner, Christina
Holle, Julia
Novikov, Pavel
Basu, Neil
Emmi, Giamoco
Jennette, Charles
Pagnoux, Christian
Blockmans, Daniel
Faurschou, Mikkel
Juche, Aaron
Salvarani, Carlo
Brouwer, Elisabeth
Flores-Suárez, Luis Felipe
Karadağ, Ömer
Schmidt, Wolfgang
Buttgereit, Frank
Gatenby, Paul
Kermani, Tanaz
Sharma, Aman
Camellino, Dario
Geraldes, Ruth
Khalidi, Nader
Sivakumar, Rajappa
Chrysidis, Stavros
Gewurz-Singer, Ora
Koster, Matthew
Springer, Jason
Cid, Maria
Guillevin, Loïc
Macieira, Carla
Sunderkötter, Cord
Daikeler, Thomas
Hammam, Nevin
Matsui, Kazuo
Terslev, Lene
de Boysson, Hubert
Hauser, Thomas
Milchert, Marcin
Tesar, Vladimir
de Miguel, Eugenio
Hellmich, Bernhard
Molloy, Eamonn
Tomasson, Gunnar
Dejaco, Christian
Henes, Jörg
Monti, Sara
Vaglio, Augusto
Diamantopoulos, Andreas
Hinojosa-Azaola, Andrea
Neumann, Thomas
Warrington, Kenneth
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Supplementary Materials 8. Example of a clinical vignette extracted from the case report form
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Supplementary Materials 9. Flow chart of expert review process to create the large-vessel vasculitis Diagnosis and Classification of
Vasculitis Study dataset
Two expert panel reviews were conducted. In 2016, with the aim of deriving the classification criteria for ANCA-associated vasculitis, in which a
total of 2871 cases were reviewed, and 2072 (72%) cases passed the process, including 174 (8.4%) cases of large-vessel vasculitis. In 2018, with
the aim of deriving the classification criteria for large-vessel vasculitis, flow chart below:
DCVAS cases with submitting physician diagnosis high/ moderate confidence (N=2131)
56 external expert reviewers
7 DCVAS committee reviewers
No agreement
REJECTED N= 436
Committee agrees
(Expert OR DCVAS)
Agreement
PASSED N= 1695 (80% all cases)
Expert does not agree N= 867 (41%)
Expert agrees N=1264 (59%)
TOTAL CASES AVAILABLE N= 2068
(Including 373 cases passing review in 2016)
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Supplementary Materials 10. Age distribution in Takayasu arteritis and giant cell arteritis
10A. Graphic and table with distribution overlap in age at diagnosis for TAK and GCA
Age at diagnosis
TAK
N=462
GCA*
N=941
Total
N=1404
< 40
355
3
358
40 to 49
78
4
82
50 to 59
26
70
96
≥ 60
3
864
867
GCA: giant cell arteritis; TAK: Takayasu arteritis
* Age at diagnosis missing for one patient with GCA
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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<50 years
50-60 years
>60 years
10B. Cluster distribution of giant cell arteritis vs. Takayasu arteritis and different categories of age at diagnosis
GCA: giant cell arteritis; TAK: Takayasu arteritis
An overlap between both graphics can be seen (≥ 60 years almost exclusive of GCA, and ≤ 50 years almost exclusive of TAK)
GCA vs. TAK
Age at diagnosis
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
26
10C. Kernel density distribution plots for age at diagnosis and age at disease onset in patients
with giant cell arteritis and Takayasu arteritis
The distribution plots show almost a perfect overlap between age at diagnosis and age at disease
onset
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
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Supplementary Materials 11. Frequency of damage (stenosis, occlusion, or aneurysm) in key
arterial territories in TAK and LV-GCA
TAK
n = 462
LV-GCA
n = 225
P value
Thoracic aorta
107 (23.2)
13 (5.8)
<0.0001
Abdominal aorta
116 (25.1)
7 (3.1)
<0.0001
Left carotid
198 (42.9)
22 (9.8)
<0.0001
Right carotid
163 (35.3)
23 (10.2)
<0.0001
Left subclavian
248 (53.7)
31 (13.8)
<0.0001
Right subclavian
173 (37.5)
26 (11.6)
<0.0001
Left renal
108 (23.4)
2 (0.9)
<0.0001
Right renal
102 (22.1)
2 (0.9)
<0.0001
Mesenteric
132 (28.6)
3 (1.33)
<0.0001
Left axillary
22 (4.8)
43 (19.1)
<0.0001
Right axillary
21 (4.6)
47 (20.9)
<0.0001
Mesenteric: celiac, superior, and inferior mesenteric arteries
LV-GCA: large-vessel giant cell arteritis defined as any vasculitic involvement of the
large arteries assessed by ultrasonography, angiography, or positron emission
tomography. Note that 225 patients with LV-GCA had damage in 219 arterial
territories.
TAK: Takayasu arteritis
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Supplementary Materials 12. Incorporation of unique imaging patterns
Unique imaging patterns of disease in Takayasu arteritis (TAK) and large-vessel giant cell arteritis (GCA) identified by K-means clustering
Patients with TAK were more likely to have vasculitic
manifestations on the abdominal aorta and renal or mesenteric
arteries, bilateral disease involvement in paired branch arteries,
and more damage by angiography (computed tomography,
magnetic resonance, or catheter-based angiography) or
ultrasonography, Patients with GCA were more likely to have
diffuse FDG-PET activity throughout the aorta or bilateral axillary
involvement of the disease.
Cluster One - renal and mesenteric arteries, and abdominal aorta
Cluster Two - carotid and subclavian arteries
Cluster Three - left subclavian artery
Cluster Four - low burden of disease rather than a specific pattern of arterial involvement
Cluster Five - descending and abdominal aorta and subclavian and carotid arteries
Cluster Six - axillary and subclavian arteries.
Gribbons KB, et al. Patterns of Arterial Disease in Takayasu Arteritis and Giant Cell Arteritis. Arthritis Care Res (Hoboken). 2020 Nov;72(11):1615-1624.
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Supplementary Materials 13. Final candidate items used within each regression analysis to
derive classification criteria for giant cell arteritis and
Takayasu arteritis
N refers to number of patients and % refers to percentage relative to the comparator group
(i.e. frequencies of items higher than 50% indicate that they are more prevalent in patients
with giant cell arteritis or Takayasu arteritis than in the comparator group) Significant
differences between giant cell arteritis or Takayasu arteritis and the comparator group are
noted as *p<0.05, **p<0.01.
Item
Description
Composite
Items
GCA
N= 756
TAK
N=462
DEMOGRAPHIC
Sex
Female
511 (53.3) **
391 (61.4) **
CLINICAL
GenSym1
Light-headedness
57 (26.5) **
139 (70.9) **
GenSym2
Syncope / Fainting
14 (18.4) **
76 (80.9) **
GenSym4
Night sweats
208 (66.2) **
41 (33.9) **
GenSym5
Rigors
26 (49.1)
8 (36.4)
GenCF6
Fever ≥ 38ºC (≥ 100.4F)
137 (43.8) *
97 (49.7)
MskSym1
Arthralgia (Joint pain)
151 (39.6) **
98 (43.6) *
MskSym2
Morning stiffness ≥ 1 hour
124 (75.6) **
12 (22.6) **
MskSym6
Myalgia (muscle pain) or muscle cramps
214 (66.1) **
43 (35.5) **
MskCF2
Muscle tenderness
62 (64.6) **
8 (22.2) **
MskCF3
Muscle weakness
45 (55.6)
14 (40.0)
MskSym3
Morning stiffness neck/torso
88 (85.4) **
7 (21.9) **
MskSym4
Morning stiffness shoulders/ arms
174 (88.3) **
12 (22.6) **
MskSym5
Morning stiffness hips/ thighs
122 (89.1) **
3 (9.1) **
EyeSym1
Amaurosis fugax (transient / temp loss)
75 (78.1) **
21 (60.0)
EyeSym2
Sudden visual loss - ongoing
102 (77.9) **
4 (8.3) **
EyeSym3
Blurred vision in either eye
148 (57.1) *
30 (24.4) **
EyeSym6
Diplopia (double vision)
74 (80.4) **
5 (17.2) **
ENTSym1
Jaw claudication
356 (94.9) **
14 (15.4) **
ENTSym2
Tongue claudication
21 (95.5) **
1 (14.3)
CPSym1
Dyspnea / Shortness of Breath
46 (24.3) **
114 (67.9) **
CPSym2
Non-productive cough
57 (47.9)
24 (40.0)
CVSym1
Angina / ischemic cardiac pain
9 (22.0) **
56 (88.9) **
CVSym2
Arm claudication
29 (17.1) **
233 (95.5) **
CVSym3
Leg claudication
33 (34.4) **
88 (83.8) **
CVCF1
Any cardiac murmur
20 (33.9) *
42 (75.0) **
GISym2
Postprandial abdominal pain / ischemic abdominal pain
3 (12.0) **
14 (48.3)
NeurSym6
New persistent headache - frontal
169 (70.4) **
25 (26.6) **
NeurSym7
New persistent headache - occipital or cervical
161 (74.2) **
26 (32.5) **
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
30
NeurSym8
New persistent headache - temporal
475 (84.1) **
28 (17.4) **
NeurSym9
New persistent headache - other (please specify)
57 (54.3)
15 (27.8) **
NeurSym4
Scalp tenderness
260 (91.2) **
5 (6.4) **
LABORATORY
TstHaem1
Significant anemia (hemoglobin < 10g/dL or 100g/L)
109 (47.6)
101 (60.5) **
TstHaem3
Significant thrombocythemia (platelets > 500 x 109/L)
141 (74.6) **
32 (38.6) *
TstHaem5
Significant elevation of WBC (total WBC > 15 x 109/L)
78 (50.7)
37 (43.5)
TstChem8
Albumin below 30g/L
102 (62.6) **
16 (25.0) **
TstChem1Dn
Maximum CRP:
**
**
- ≤10 mg/L
73 (19.4)
185 (56.2)
- 10 to 49 mg/L
234 (47.9)
179 (55.9)
- 50 to 99 mg/L
207 (70.7)
63 (45.3)
- 100 to 149 mg/L
119 (73.9)
23 (36.5)
- ≥150 mg/L
123 (66.5)
12 (19.7)
TstHaem9Dn
Maximum ESR:
**
*
- ≤10 mm/hr
19 (12.9)
45 (38.5)
- 10 to 49 mm/hr
179 (35.2)
199 (55.6)
- 50 to 74 mm/hr
198 (56.9)
115 (53.5)
- 75 to 99 mm/hr
196 (70.8)
61 (47.3)
- ≥100 mm/hr
164 (73.2)
42 (45.2)
VASCULAR EXAM
Any Bruit
(carotid, subclavian, axillary, brachial, radial, renal,
abdominal aorta, or iliofemoral)
Y
65 (28.9) **
263 (89.2) **
Diminished or absent pulse of the lower limbs
(femoral, popliteal, posterior tibial, or dorsalis pedis)
Y
61 (38.1) **
134 (80.7) **
Diminished or absent pulse of upper limbs
(axillary, brachial, or radial arteries)
Y
35 (16.0) **
309 (95.1) **
Carotid abnormality
(absent/diminished pulse, or tenderness/carotidynia)
Y
41 (29.9) **
171 (91.4) **
Temporal artery abnormality
(absent/diminished pulse, tenderness, or hard ‘cord-like’)
Y
354 (91.0) **
4 (5.1) **
Absent upper extremity blood pressure:
**
**
- No abnormality
753 (52.6)
351 (44.0)
- Absent in one arm
3 (5.8)
80 (98.8)
- Absent in both arms
0 (0.0)
31 (93.9)
Difference in upper extremity blood pressure:
**
**
- <10 mmHg
654 (53.4)
224 (36.1)
- 10 to 20 mmHg
73 (58.9)
48 (56.5)
- ≥20 mmHg
29 (18.5)
190 (92.2)
IMAGING
Abdominal aorta and renal/mesenteric
(damage on angiography or US only)
Y
0 (0.0) **
83 (94.3) **
Paired artery involvement of the carotid, subclavian, or
renal arteries (damage on angiography or US only)
Y
13 (13.3) **
140 (92.1) **
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
31
Number territories with damage on angiography or US
(from nine possible territories: thoracic aorta, abdominal
aorta, mesenteric, carotid, subclavian, or renal arteries):
Y
**
**
- 0 territories
Y
698 (61.1)
22 (5.4)
- 1 territory
Y
26 (24.1)
76 (67.9)
- 2 territories
Y
24 (22.6)
114 (90.5)
- 3 territories
Y
6 (10.9)
89 (94.7)
- 4 territories
Y
2 (4.2)
80 (95.2)
- 5 territories
Y
0 (0.0)
47 (95.9)
- 6 territories
Y
0 (0.0)
20 (95.2)
- 7 territories
Y
0 (0.0)
12 (92.3)
- 8 territories
Y
0 (0.0)
1 (100.0)
Temporal artery – US halo sign
211 (99.5) **
0 (0.0) **
Bilateral axillary involvement (damage on angiography
/damage or halo sign on US/ FDG uptake on PET)
Y
57 (82.6) **
12 (40.0)
FDG-PET activity throughout the descending thoracic and
abdominal aorta
Y
52 (85.3) **
6 (26.1) *
BIOPSY
Definitive vasculitis on temporal artery biopsy
335 (99.7) **
0 (0.0) **
CRP: C-reactive protein; ESR - erythrocyte sedimentation rate; FDG - fluorodeoxyglucose; GCA - giant cell
arteritis; PET - positron emission tomography; US - ultrasound; TAK - Takayasu arteritis; WBC - white blood count
Damage on image: presence of stenosis, occlusion, or aneurysm
Angiography: computed tomography, magnetic resonance, or catheter-based angiography
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
32
Supplementary Materials 14: Mimics used to develop the giant cell arteritis and Takayasu
arteritis criteria
Mimics used to develop the GCA criteria
N
Cardiovascular
60
Arterial dissection
4
Atheroembolic disease
5
Atherosclerosis
26
Fibromuscular dysplasia
2
IgG4-related arterial disease
1
Other vasculopathies
13
Relapsing polychondritis
3
Temporal artery aneurysm
2
Thromboangiitis obliterans (Buerger's disease)
2
Venous disease
2
Hematologic
2
Systemic amyloidosis
2
Infectious Disease
35
Bacterial endocarditis / bacteremia
13
Bacterial or viral pneumonia
9
Tuberculosis
13
Malignancy
29
Hematologic
15
Solid Malignancy
14
Neurologic
62
Central nervous system vasculopathy associated to hepatitis C
1
Cranial nerve lesion
7
Migraine or other headache syndromes
29
Multiple sclerosis
5
Myelopathy
1
Myopathy
1
Neurofibromatosis
1
Neuropathy not due to vasculitis
9
Reversible cerebral vasoconstriction syndrome
2
Stroke / transient ischemic attack
6
Non-primary vasculitis after panel review
35
Initially diagnosed as Behҫet's disease
5
Initially diagnosed as GCA
18
Initially diagnosed as other LVV
1
Initially diagnosed as PAN
4
Initially diagnosed as primary vasculitis with no specific vessel size
3
Initially diagnosed as TAK
4
Ophthalmologic
27
Birdshot retinochoroidopathy
1
Central retinal / ophthalmic artery occlusion
2
Central retinal vein occlusion
2
Glaucoma
3
Non-arteritic anterior ischemic optic neuropathy
5
Optic neuritis
2
Other non-vasculitic vision loss
10
Uveitis
2
TOTAL
250
Mimics used to develop the TAK criteria
N
Cardiovascular
39
Arterial dissection
1
Atheroembolic disease
4
Atherosclerosis
19
Idiopathic retroperitoneal fibrosis (M. Ormond)
1
IgG4-related arterial disease
1
Other vasculopathies
9
Relapsing polychondritis
2
Temporal artery aneurysm
1
Venous disease
1
Hematologic
1
Systemic amyloidosis
1
Infectious Disease
16
Bacterial endocarditis / bacteremia
6
Bacterial or viral pneumonia
5
Tuberculosis
5
Malignancy
23
Hematologic
16
Solid Malignancy
7
Neurologic
34
Cranial nerve lesion
5
Migraine or other headache syndromes
14
Multiple sclerosis
2
Myopathy
2
Neurofibromatosis
1
Neuropathy not due to vasculitis
6
Reversible cerebral vasoconstriction syndrome
1
Sneddon's syndrome
1
Stroke / transient ischemic attack
2
Non-primary vasculitis after panel review
22
Initially diagnosed as Behҫet's disease
4
Initially diagnosed as GCA
12
Initially diagnosed as other LVV
1
Initially diagnosed as PAN
2
Initially diagnosed as primary vasculitis with no specific vessel size
1
Initially diagnosed as TAK
2
Ophthalmologic
15
Birdshot retinochoroidopathy
1
Central retinal / ophthalmic artery occlusion
1
Glaucoma
2
Non-arteritic anterior ischemic optic neuropathy
2
Optic neuritis
2
Other non-vasculitic vision loss
5
Uveitis
2
TOTAL
150
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33
GCA: giant cell arteritis, TAK: Takayasu arteritis; PAN: polyarteritis nodosa; LVV: large-vessel vasculitis
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
34
Supplementary Materials 15A. Results of regression analysis for giant cell arteritis
Predictor variables
Odds Ratio (95%CI)
P-value
CLINICAL
Light-headedness
0.42 (0.19 - 0.92)
0.030
Syncope / Fainting
0.62 (0.20 - 1.90)
0.401
Night sweats
1.38 (0.78 - 2.46)
0.268
Fever ≥ 38ºC (≥ 100.4F)
0.58 (0.33 - 1.04)
0.068
Arthralgia
0.39 (0.22 - 0.68)
0.001
Myalgia or muscle cramps
2.27 (1.30 - 3.97)
0.004
Morning stiffness shoulders/ neck
8.96 (3.83 - 20.92)
<0.001
Sudden visual loss - ongoing
14.31 (5.67 - 36.11)
<0.001
Jaw or tongue claudication
13.83 (6.54 - 29.22)
<0.001
Dyspnea
0.60 (0.29 - 1.25)
0.173
Arm claudication
0.91 (0.34 - 2.44)
0.845
New persistent headache - occipital or cervical
2.63 (1.37 - 5.05)
0.004
New persistent headache - temporal
6.33 (3.80 - 10.55)
<0.001
New persistent headache - other
1.73 (0.76 - 3.95)
0.192
Scalp tenderness
5.33 (2.56 - 11.08)
<0.001
LABORATORY
Significant thrombocythemia
3.01 (1.60 - 5.67)
0.001
Maximum ESR (>50 mm/hr) or maximum CRP (>10 mg/L)
13.46 (6.46 - 28.07)
<0.001
VASCULAR EXAM
Diminished or absent pulse of upper limbs
0.57 (0.24 - 1.38)
0.213
Carotid absent/reduced pulse or tenderness
0.46 (0.16 - 1.31)
0.147
Temporal artery abnormality on vascular exam
3.71 (2.01 – 6.84)
<0.001
Difference in upper extremity blood pressure “10 – 20mgHg”
2.16 (0.97 – 4.80)
0.059
Difference in upper extremity blood pressure “≥20mmHg”
0.88 (0.35 – 2.23)
0.788
IMAGING
Bilateral disease of the large vessels (angiography /US, without PET)
0.40 (0.13 – 1.23)
0.112
Bilateral axillary involvement (angiography /US/PET)
9.04 (3.08 – 26.52)
<0.001
Aorta Involvement on PET
8.84 (2.69 – 29.07)
<0.001
CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; PET: positron emission tomography; US: ultrasound
Angiography: computed tomography, magnetic resonance, or catheter-based angiography
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
35
Supplementary Materials 15B. Results of regression analysis for Takayasu arteritis
Predictor variables
Odds Ratio (95%CI)
P-value
CLINICAL
Female sex
2.57 (1.31 – 5.05)
0.006
Arthralgia
0.67 (0.33 – 1.36)
0.267
Myalgia or muscle cramps
0.53 (0.21 – 1.36)
0.189
Vision (sudden vision loss, blurred vision, or diplopia)
0.20 (0.07 – 0.56)
0.002
Jaw or tongue claudication
0.15 (0.03 – 0.71)
0.017
Dyspnea
2.05 (0.90 – 4.67)
0.090
Angina / ischemic cardiac pain
6.22 (1.09 – 35.60)
0.040
Arm or leg claudication
6.53 (2.89 – 14.76)
<0.001
New persistent headache – temporal
1.23 (0.49 – 3.08)
0.655
Scalp tenderness
0.10 (0.01 – 0.72)
0.022
LABORATORY
Albumin below 30g/L
0.27 (0.07 – 1.00)
0.050
Maximum ESR (>50 mm/hr) or maximum CRP (>10 mg/L)
2.26 (1.1 – 4.65)
0.027
VASCULAR EXAM
Any Bruit (thorax or abdomen or limbs)
5.73 (2.66 – 12.32)
<0.001
Diminished or absent pulse of lower limbs
1.61 (0.66 – 3.90)
0.295
Diminished or absent pulse of upper limbs
5.32 (2.22 – 12.73)
<0.001
Carotid absent/reduced pulse or tenderness
7.02 (2.33 – 21.17)
0.001
Temporal artery abnormality on vascular exam
0.20 (0.03 – 1.23)
0.082
Difference in upper extremity blood pressure “10 – 20mgHg”
1.57 (0.60 – 4.06)
0.356
Difference in upper extremity blood pressure “≥20mmHg”
4.39 (1.47 – 13.08)
0.008
IMAGING
Abdominal aorta and renal/mesenteric arteries
(angiography /US, without PET)
14.29 (4.08 – 50.05)
<0.001
Bilateral disease of the large vessels (angiography /US, without PET)
2.02 (0.73 – 5.59)
0.175
CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; PET: positron emission tomography; US: ultrasound
Angiography: computed tomography, magnetic resonance, or catheter-based angiography
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
37
Supplementary Table 17A. Data-driven and clinically-selected models for giant cell arteritis with associated risk scored based off
beta coefficient weighting
Description
OR (95% CI)
Risk Score
P-value
Vasculitis on TAB or TA halo on ultrasound *
5
ESR ≥50 mm or CRP ≥10 mg/L
16.25 (7.96 – 33.17)
3
<0.001
Sudden visual loss
13.52 (5.72 – 31.96)
3
<0.001
Jaw or tongue claudication
11.24 (5.66 – 22.33)
2
<0.001
FDG-PET activity throughout aorta
8.97 (2.96 – 27.17)
2
<0.001
Bilateral axillary disease on imaging (angiography /US/PET)
8.75 (3.57 – 21.47)
2
<0.001
Morning stiffness in shoulders/neck
7.78 (3.61 – 16.76)
2
<0.001
New temporal headache
7.21 (4.54 – 11.46)
2
<0.001
Scalp tenderness
6.76 (3.35 – 13.64)
2
<0.001
TA abnormality on vascular exam§
5.33 (2.99 – 9.51)
2
<0.001
CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; FDG: fluorodeoxyglucose; OR: odds ratio; PET: positron emission tomography;
TA: temporal artery; TAB: temporal artery biopsy; US: ultrasound
* Added after cluster analysis (Supplementary Materials 16)
§ Tenderness, hard ‘cord-like’, diminished or absent pulse
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
38
Supplementary Table 17B. Data-driven and clinically-selected models for Takayasu arteritis with associated risk scored based
off beta coefficient weighting
Description
OR (95% CI)
Risk Score
P-value
Abdominal aorta and renal/mesenteric involvement (angiography /US)
23.06 (7.35 - 72.39)
3
<0.001
Three or more affected arteries on imaging (angiography /US, without PET) *
3
Diminished or absent pulse in upper extremity
7.89 (3.54 - 17.56)
2
<0.001
Arm or leg claudication
7.45 (3.74 - 14.81)
2
<0.001
Angina or ischemic cardiac pain
7.39 (1.80 - 30.31)
2
<0.001
Arterial bruit
5.09 (2.66 - 9.75)
2
<0.001
Carotid absent/reduced pulse or tenderness
4.65 (1.9 - 10.89)
2
<0.001
Two affected arteries on imaging (angiography /US, without PET) *
2
SBP difference in arms ≥ 20mmHg
3.56 (1.40 - 9.07)
1
0.008
Female sex
2.45 (1.34 - 4.49)
1
0.004
Imaging involvement of paired branch arteries (angiography /US, without PET)
2.36 (0.92 - 6.05)
1
0.074
One affected artery on imaging (angiography /US, without PET) *
1
SBP: systolic blood pressure; US: ultrasound
* Added after cluster analysis (Supplementary Materials 16)
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
39
Supplementary Materials 18A: Performance characteristics of a points-based risk score for
giant cell arteritis with different thresholds (development
dataset)
Threshold Score
Sensitivity (%)
Specificity (%)
AUC (95%CI)
3
98.84
72.76
0.86 (0.84-0.88)
4
95.37
88.99
0.92 (0.91-0.94)
5
95.17
90.49
0.93 (0.91-0.94)
6
84.75
94.96
0.90 (0.88-0.92)
7
83.59
96.83
0.90 (0.88-0.92)
8
75.48
99.07
0.87 (0.85-0.89)
A total score of ≥ 6 was considered the best cut-point to provide high enough specificity for
purposes of enrolling patients into clinical trials without losing too much sensitivity. If a higher
total score is chosen, specificity increases but there is a corresponding disproportionate drop in
sensitivity. When scoring an individual patient, the higher the score, the higher the specificity
for giant cell arteritis.
AUC: area under the curve; CI: confidence interval
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
40
Supplementary Materials 18B: Performance characteristics of a points-based risk score for
Takayasu arteritis with different thresholds (development
dataset)
Threshold
Sensitivity (%)
Specificity (%)
AUC (95%CI)
2
97.47
93.19
0.95 (0.94-0.97)
3
96.20
94.74
0.95 (0.94-0.97)
4
93.35
96.28
0.95 (0.93-0.97)
5
89.87
96.59
0.93 (0.91-0.95)
6
85.44
98.45
0.92 (0.90-0.94)
A threshold score of ≥ 4 or ≥ 5 was considered equivalent to maximize specificity while retaining
good sensitivity in the development dataset. In the validation dataset, the specificity for a cut-
point of ≥ 5 remained greater than for a cut-point of ≥ 4 (99.2 vs 98.4%). Therefore, a cut-point
of ≥ 5 was chosen to maximize specificity for the purpose of enrolling patients into clinical trials.
If a higher total score is chosen, specificity increases but there is a corresponding
disproportionate drop in sensitivity. When scoring an individual patient, the higher the score,
the higher the specificity for Takayasu arteritis.
AUC: area under the curve; CI: confidence interval
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
41
Supplementary Materials 19A. Discrimination curves for the classification criteria for giant
cell arteritis
Classification criteria applied to 1,505 cases confirmed by Expert Review, 756 (50.2%) with giant
cell arteritis and 749 (49.8%) comparators divided into a development dataset (70%) and
validation dataset (30%). The Area Under Curve (AUC) for the development dataset is shown
(solid line) and the AUC for the validation dataset is shown (dotted line).
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42
Supplementary Materials 19B. Discrimination curves for the classification criteria for Takayasu
arteritis
Classification criteria applied to 912 cases confirmed by Expert Review, 462 with Takayasu
arteritis (50.7%) and 450 (49.3%) comparators divided into a development dataset (70%) and a
validation dataset (30%). The Area Under Curve (AUC) for the development dataset is shown
(solid line) and the AUC for the validation dataset is shown (dotted line).
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2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
43
Supplementary Materials 20A. Performance characteristics of the 2022 ACR-EULAR and the 1990 ACR classification criteria for giant
cell arteritis in the complete DCVAS database (development and validation datasets)
ACR: American College of Rheumatology; AUC: Area under the curve; CI: Confidence interval; EULAR: European Alliance of Associations for Rheumatology; GCA: giant cell arteritis.
GCA subtypes: biopsy-proven GCA (definite vasculitis on TAB) and large-vessel GCA (involvement of the aorta or its branch arteries on either angiography [computed tomography,
magnetic resonance, or catheter-based angiography], ultrasound or PET, without vasculitis on TAB)
N total (N GCA): N of total cases used in the model (number of GCA cases); for the world region analysis all the available cases and comparators were selected for each region.
2022 ACR-EULAR classification criteria for GCA
1990 ACR classification criteria for GCA
Subset of patients
N total (N GCA)
Sensitivity (95% CI)
Specificity (95% CI)
AUC (95% CI)
Sensitivity (95% CI)
Specificity (95% CI)
AUC (95% CI)
GCA subtypes
Biopsy-proven GCA
1104 (355)
100.0% (99.0-100.0%)
94.9% (93.1-96.4%)
0.97 (0.97-0.98)
93.0% (89.8-95.4%)
92.8% (90.7-94.5%)
0.93 (0.91-0.94)
Large-vessel GCA
873 (124)
55.7% (46.5-64.6%)
94.9% (93.1-96.4%)
0.75 (0.71-0.80)
37.1% (28.6-46.2%)
92.8% (90.7-94.5%)
0.65 (0.61-0.69)
World regions
North America
226 (90)
77.8% (67.8-85.9%)
95.6% (90.6-98.4%)
0.87 (0.82-0.91)
70.0% (59.4-79.2%)
91.9% (86.0-95.9%)
0.81 (0.76-0.86)
Europe
973 (642)
87.2% (84.4-89.7%)
88.8% (84.9-92.0%)
0.88 (0.86-0.90)
81.0% (77.7-84.0%)
88.2% (84.3-91.5%)
0.85 (0.82-0.87)
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis
doi: 10.1136/ard-2022-223480–1653.:1647 81 2022;Ann Rheum Dis, et al. Ponte C
2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
44
Supplementary Materials 20B. Performance Characteristics of the 2022 ACR-EULAR and the 1990 ACR Criteria for Takayasu arteritis
in the complete DCVAS database (development and validation datasets)
ACR: American College of Rheumatology; AUC: Area under the curve; CI: Confidence interval; EULAR: European Alliance of Associations for Rheumatology; TAK: Takayasu arteritis
N total (N TAK): N of total cases used in the model (number of TAK cases); for the world region analysis all the available cases and comparators were selected for each region.
2022 ACR-EULAR classification criteria for TAK
1990 ACR classification criteria for TAK
Subset of patients
N total (N TAK)
Sensitivity (95% CI)
Specificity (95% CI)
AUC (95% CI)
Sensitivity (95% CI)
Specificity (95% CI)
AUC (95% CI)
Age intervals
Age 18–39 years
437 (351)
94.0% (91.0-96.3%)
97.7% (91.9-99.7%)
0.96 (0.94-0.98)
89.2% (85.4-92.2%)
97.7% (91.9-99.7%)
0.93 (0.91-0.96)
Age 40–60 years
226 (104)
83.7% (75.1-90.2%)
91.8% (85.4-96.0%)
0.88 (0.83-0.92)
62.5% (52.5-71.8%)
96.7% (91.8-99.1%)
0.80 (0.75-0.93)
World regions
North America
127 (28)
85.7% (67.3-96.0%)
92.9% (86.0-97.1%)
0.89 (0.82-0.96)
85.7% (67.3-96.0%)
93.94% (87.3-97.7%)
0.90 (0.83-0.97)
Europe
422 (130)
91.5% (85.4-95.7%)
94.9% (91.7-97.1%)
0.93 (0.90-0.96)
80.8% (72.9-87.2%)
98.63% (96.5-99.6%)
0.90 (0.86-0.93)
North America/Europe
549 (158)
90.5% (84.8-94.6%)
94.4% (91.6-96.4%)
0.92 (0.90-0.95)
81.7% (67.3-96.0%)
97.44% (95.4-98.8%)
0.90 (0.86-0.97)
Asia
357 (298)
92.0% (88.3-94.8%)
93.2% (83.5-98.1%)
0.94 (0.89-0.96)
83.9% (79.3-87.4%)
96.61% (88.3-99.6%)
0.90 (0.87-0.93)
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis
doi: 10.1136/ard-2022-223480–1653.:1647 81 2022;Ann Rheum Dis, et al. Ponte C
2022 ACR-EULAR Classification Criteria for Large-Vessel Vasculitis
45
Supplementary Materials 21. Age in the new classification criteria – the 50-60 years interval
In all the DCVAS dataset there were 1451 patients diagnosed with large vessel vasculitis (942
GCA and 509 TAK) after expert panel review. A total of 96/1451 patients (6.6%) were aged
between 50 and 60 years, 26/96 (27.1%) with the diagnosis of TAK and 70/96 (72.9%) with the
diagnosis of GCA.
Patients with Large-Vessel Vasculitis Diagnosed Between 50-60 Years of Age
Takayasu arteritis (n=26)
Giant cell arteritis (n=70)
Patients who meet the TAK criteria
23 (88.5%)
Patients who meet the GCA criteria
44 (62.9%)
Patients who meet the GCA criteria
1 (3.9%)
Patients who meet the TAK criteria
9 (12.9%)
Patients who meet both TAK and
GCA criteria
1 (3.9%)
Patients who meet both GCA and TAK
criteria
2 (2.9%)
GCA: giant cell arteritis; TAK: Takayasu arteritis
In this age interval only 3/96 (3.1%) patients fulfilled both TAK and GCA 2022 ACR-EULAR
classification criteria.
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis
doi: 10.1136/ard-2022-223480–1653.:1647 81 2022;Ann Rheum Dis, et al. Ponte C
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis
doi: 10.1136/ard-2022-223480–1653.:1647 81 2022;Ann Rheum Dis, et al. Ponte C
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis
doi: 10.1136/ard-2022-223480–1653.:1647 81 2022;Ann Rheum Dis, et al. Ponte C
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis
doi: 10.1136/ard-2022-223480–1653.:1647 81 2022;Ann Rheum Dis, et al. Ponte C
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Ann Rheum Dis
doi: 10.1136/ard-2022-223480–1653.:1647 81 2022;Ann Rheum Dis, et al. Ponte C
