American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) Diagnostic and Classification Criteria for Primary Systemic Vasculitis
- Conditions
- Polyarteritis NodosaChurg Strauss SyndromeWegener's GranulomatosisMicroscopic PolyangiitisGiant Cell ArteritisTakayasu Arteritis
- Registration Number
- NCT01066208
- Lead Sponsor
- University of Oxford
- Brief Summary
Vasculitis is group of diseases where inflammation of blood vessels is the common feature. Patients typically present with fever, fatigue, weakness and muscle and joint aches. These symptoms are very common among many different diseases, not just vasculitis. A clustering of other symptoms, physical examination findings, blood tests, radiology and biopsy help make the diagnosis. There are currently no criteria to help doctors make a diagnosis of vasculitis when a patient presents with these non specific symptoms and they are reliant on previous experience and disease definitions. One of the aims of this project is to develop diagnostic criteria for the primary systemic vasculitides (granulomatosis with polyangiitis (Wegener's), microscopic polyangiitis, Churg Strauss syndrome, polyarteritis nodosa, giant cell arteritis, Takayasu arteritis). We, the investigators, will do this by studying a large group of patients with vasculitis and comparing them to a large group of patients that present in a similar way, but do not have vasculitis. By comparing the 2 groups we will create a list of items to differentiate between vasculitis and 'vasculitis mimics'.
We also aim to update the current classification criteria. Classification criteria are used to group patients into different types of vasculitis, once a diagnosis of vasculitis has been made, and are useful for studying patients in clinical trials with similar or identical diseases. The current classification criteria (American college of Rheumatology 1990 criteria) were developed 20 years ago, before the availability of some important diagnostic tests (e.g. antineutrophil cytoplasmic antibodies \[ANCA\]), and are now not consistent with some of the current disease definitions. Therefore to progress future research in vasculitis, it is important that the classification criteria are updated. We will recruit 260 patients with each of the 6 types of vasculitis and compare them with 1300 controls (patients with the 5 other types of vasculitis), in order to determine the optimal combination of symptoms, signs and investigations that classify each person into the appropriate group.
- Detailed Description
The systemic vasculitides are a group of uncommon but important diseases whose prognosis has improved dramatically with the use of immunosuppressive therapy. However, long-term morbidity from recurrent disease flares, low-grade grumbling disease and/or accumulating damage from previous disease activity or drug therapy now characterise the long-term outlook for patients with vasculitis. There remains major controversy, and incompatibility between the ANCA-associated vasculitides: granulomatosis with polyangiitis (Wegener's), microscopic polyangiitis, and Churg Strauss Syndrome, as well as polyarteritis nodosa in the current classification criteria and disease definitions. Importantly, there are no diagnostic criteria for any of the primary systemic vasculitides.
We propose to improve existing classification criteria for the primary systemic vasculitides. As a starting point will include the following diseases: granulomatosis with polyangiitis (Wegener's) (GPA), microscopic polyangiitis (MPA), Churg Strauss syndrome (CSS), polyarteritis nodosa (PAN), giant cell arteritis (GCA) and Takayasu arteritis (TAK).
We propose to develop and validate classification and diagnostic criteria for primary systemic vasculitis using the guidelines suggested by the Classification and Response Criteria Subcommittee of the American College of Rheumatology Committee on Quality Measures. For all patients, a detailed medical history, physical examination, laboratory tests (including ANCA), radiology (including angiography), biopsy results, treatment, Birmingham Vasculitis Activity Score (BVAS)version 3, Vasculitis Damage Index (VDI), will be collected. The exact list of items to be recorded will be determined by the expert panel at the start of the study.
Classification criteria
We will study a minimum of 100 patients (new and existing patients) prospectively within each currently defined disease category (GPA, CSS, MPA, PAN, GCA, TAK) for the development of the classification criteria. We anticipate the need to recruit 130 patients to account for misdiagnosis and dropout to achieve the target of 100 with the confirmed reference diagnosis. This will include patients that have vasculitis which are assumed to be related to ANCA but do not fulfil the current definitions of any of the diseases, and patients with large vessel vasculitis which do not fulfil current definition for GCA or TAK. Therefore new categories of disease may be created as part of this process and some of the current disease categories may be changed to include or exclude certain patients.
The other diseases will be the controls. The same minimum number of patients will be used to validate the criteria. The 1st 100 patients with a formal reference diagnosis that are recruited for each disease will be used for development of the classification criteria; the next 100 consecutive patients recruited with a confirmed reference diagnosis for each disease will be used to validate the criteria. Again we anticipate the need to recruit 130 patients to account for misdiagnosis and dropout to achieve the 100 target. The majority of cases included will be the same as that used for the development of the diagnostic criteria.
In the absence of an established gold standard, we propose to develop a reference standard. Clinical vignettes using clustering of clinical features and investigations will be constructed from actual cases by the steering group. An expert panel will then be asked to classify each vignette. Hypothetical changes will then be made to components of each clinical vignette and the expert panel will be asked to re classify the case. This process will be repeated multiple times in an attempt to determine what key clinical feature influence the expert panel to change the diagnosis. Using this data driven process, a construct of important clinical features for each disease will be determined by the expert panel. Using this new construct, patients will be classified by the expert panel. This will form the reference standard against which the new criteria will be tested.
Diagnostic Criteria
We propose to develop and validate diagnostic criteria for primary systemic vasculitis. Based on current disease categories we will include GPA, MPA, CSS, PAN, GCA and TAK (but this may change depending on whether new categories are created or existing categories merged as part of the classification criteria component). For the development of diagnostic criteria, we will study a minimum of 100 patients (will require approx 130 patients to allow for dropout and misdiagnosis) for each disease category. Assuming 6 disease categories, the majority of these 780 patients will have already been identified from the classification criteria component of the study and will be re used for the development and validation of diagnostic criteria. However, for the diagnostic criteria to be clinically relevant we will only include patients that are seen at the time of 1st presentation, therefore not all the 780 patients recruited for the classification criteria section of the study will be suitable, and we will need to recruit additional new patients for each of the types of vasculitis being studied.
We will use a minimum of 400 context specific controls (patients that don't have vasculitis) for AAV and PAN that will cover the spectrum of different disease presentations and severity. In addition, we will recruit a minimum of 100 context specific controls for GCA and a similar number for TAK. Different control populations are needed for AAV, GCA and TAK as they have significantly different clinical presentations. In a similar manner to cases, we will recruit 30% more patients than the minimum required to account for misdiagnosis and drop out. The same minimum number of cases and controls will be needed to validate the criteria. The first half of the patients recruited would be used to develop the criteria, and the 2nd half to validate the criteria. We will allow inclusion of patients from previously studied prospective cohorts that meet all the appropriate inclusion / exclusion criteria and have had all the appropriate clinical information and mandatory investigation (to be defined later) recorded at time of their first presentation. This is to facilitate the recruitment of sufficient patients with PAN, CSS and TAK which are rare conditions.
Statistical analysis
We will follow the ACR recommended statistical methods for creating the classification criteria. Patients will have been classified into the different types of vasculitis according to the proposed EULAR/ACR schema by the expert panel or as a vasculitis mimic. The outcomes of interest are binary variables indicating whether or not a patient has been classified as having a particular type of vasculitis, such as GPA, MPA, etc. For each outcome, multivariable logistic regression modeling will be used to identify predictors of outcome based on the list of potential predictor variables described earlier. We will also explore the use of Classification And Regression Tree (CART) analysis. This is a tree-building technique ideally suited to the generation of clinical decision rules. Unlike conventional regression methods, patients are partitioned ("split") into different groups based on an exhaustive search of all possible predictor variables. The advantage of CART analysis over conventional methods is that it is non-parametric, so no assumptions are made about the underlying distribution of predictor variables. CART can handle many hundreds of possible predictor variables and can uncover complex interactions between predictors which may be difficult or impossible to uncover using traditional multivariate techniques that can suffer from model over fitting. In addition, clinicians generally do not think in terms of probability but rather in terms of categories, such as low versus high risk. Clinical decision rules generated using CART analysis are more likely to make clinical sense, and hence more likely to be followed in clinical practice.
Once the best items are identified, the expert panel will decide on the best short list of items to be included in each criteria and also choose the most appropriate decision tree. This will provide the best content validity.
The statistical methods to be used for diagnostic criteria will be very similar to that used for the classification criteria. The binary outcome for analysis is whether the person is a case or control (without vasculitis). We repeat the analyses for each of each type of vasculitis e.g. WG versus controls, then CSS versus controls etc
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 3588
Not provided
Not provided
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Develop new diagnostic and classification criteria for ANCA associated vasculitis and polyarteritis nodosa 3 years
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (120)
University of Alabama at Birmingham
🇺🇸Birmingham, Alabama, United States
Cedars-Sinai Medical Center, LA
🇺🇸Los Angeles, California, United States
University of California, San Francisco
🇺🇸San Francisco, California, United States
University of Maryland
🇺🇸Baltimore, Maryland, United States
Vasculitis Center, Boston University School of Medicine
🇺🇸Boston, Massachusetts, United States
Dartmouth-Hitchcock Medical Centre, Lebanon, NH
🇺🇸Lebanon, New Hampshire, United States
University of Michigan, Internal Medicine
🇺🇸Ann Arbor, Michigan, United States
Mayo Clinic
🇺🇸Rochester, Minnesota, United States
New York University Langone Medical Centre
🇺🇸New York, New York, United States
University of North Carolina
🇺🇸Chapel Hill, North Carolina, United States
Cleveland Clinic
🇺🇸Cleveland, Ohio, United States
University of Pennsylvania
🇺🇸Philadelphia, Pennsylvania, United States
University of Pittsburgh
🇺🇸Pittsburgh, Pennsylvania, United States
University Medical Center
🇺🇸Salt Lake City, Utah, United States
Ramos Mejia Hospital, University of Buenos Aires
🇦🇷Buenos Aires, Argentina
Hospital Interzonal San Juan Bautista
🇦🇷San Fernando del Valle de Catamarca, Catamarca, Argentina
Medical University Innsbruck
🇦🇹Innsbruck, Austria
Royal Brisbane and Women's Hospital
🇦🇺Herston, Queensland, Australia
ANU Medical Centre
🇦🇺Canberra, Australian Capital Territory, Australia
University Hospitals Leuven
🇧🇪Leuven, Belgium
University of Manitoba
🇨🇦Winnipeg, Manitoba, Canada
Mount Sinai Hospital, Toronto
🇨🇦Toronto, Ontario, Canada
St Joseph's Healthcare
🇨🇦Hamilton, Ontario, Canada
McGill University
🇨🇦Montreal, Quebec, Canada
University of Calgary
🇨🇦Calgary, Canada
St Joseph's Healthcare London, Ontario
🇨🇦Ontario, Canada
University of Ottawa
🇨🇦Ottawa, Ontario, Canada
Sherbrooke University Hospital Centre
🇨🇦Sherbrooke, Quebec, Canada
Peking Union Medical College Hospital, Beijing
🇨🇳Beijing, China
General University Hospital, Prague
🇨🇿Prague, Czech Republic
Cochin Hospital, Université Paris-descartes
🇫🇷Paris, France
Universitätsklinikum Jena
🇩🇪Jena, Germany
General University Hospital
🇨🇿Prague, Czech Republic
Assiut University, Assiut University Hospitals
🇪🇬Assiut, Egypt
Cairo University, Kasr El Ainy Hospital
🇪🇬Cairo, Egypt
Rigshospitalet
🇩🇰Copenhagen, Denmark
Helsinki University Central Hospital
🇫🇮Helsinki, Finland
University of Schleswig-Holstein
🇩🇪Luebeck, Germany
Universitätsklinikum Münster
🇩🇪Münster, Germany
Kreiskliniken Esslingen
🇩🇪Plochingen, Germany
University Hospital Tübingen
🇩🇪Tübingen, Germany
University of Debrecen Medical and Health Science Center
🇭🇺Debrecen, Hungary
Postgraduate Institute of Medical Education and Research, Chandigarh
🇮🇳Chandigarh, India
Chatrapathi Shahuji Maharaj Medical Center, Lucknow (IProcess)
🇮🇳Lucknow, Uttar Pradesh, India
Nizam's Institute of Medical Sciences, Hyderabad
🇮🇳Hyderabad, India
Medanta, Delhi
🇮🇳New Delhi, India
Christian Medical College & Hospital, Vellore
🇮🇳Vellore, India
Cork University Hospital
🇮🇪Cork, Ireland
St. Vincent's University Hospital, Dublin
🇮🇪Dublin 4, Ireland
Tsukuba University Hospital
🇯🇵Tsukuba, Ibaraki Prefecture, Japan
University of Parma
🇮🇹Parma, Italy
Santa Maria Nuova Hospital, Reggio Emilia
🇮🇹Reggio Emilia, Italy
Arcispedale Santa Maria Nuova
🇮🇹Reggio Emilia, Italy
Kameda Medical Centre, Kamogawa
🇯🇵Kamogawa City, Chiba prefecture, Japan
Miyazaki University Hospital
🇯🇵Miyazaki City, Miyazaki Prefecture, Japan
Kyorin University Hospital
🇯🇵Mitaka, Tokyo Prefecture, Japan
Saitama Medical University
🇯🇵Kawagoe, Saitama Prefecture, Japan
Chiba University
🇯🇵Chiba, Japan
Kagawa University Hospital
🇯🇵Kagawa, Japan
St. Marianna University Hospital
🇯🇵Kanagawa, Japan
Kanazawa University Hospital
🇯🇵Kanazawa, Japan
Okayama University Hospital
🇯🇵Okayama, Japan
Kitano Hospital
🇯🇵Osaka, Japan
Juntendo University Koshigaya Hospital
🇯🇵Saitama, Japan
Jichi Medical University Hospital
🇯🇵Tochigi-ken, Japan
University Tokyo Hospital
🇯🇵Tokyo, Japan
Instituto Nacional de Enfermedades Respiratorias
🇲🇽Mexico City, Mexico
Seoul National University Hospital
🇰🇷Seoul, Korea, Republic of
Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran
🇲🇽Mexico City, Mexico
VU University Medical Center
🇳🇱Amsterdam, Netherlands
University Medical Center Groningen
🇳🇱Groningen, Netherlands
University of Otago, Christchurch
🇳🇿Christchurch, Canterbury, New Zealand
Auckland District Health Board
🇳🇿Auckland, New Zealand
Waitemata District Health Board, North Shore Hospital
🇳🇿Auckland, New Zealand
Waikato District Health Board
🇳🇿Hamilton, New Zealand
Hospital of Southern Norway
🇳🇴Kristiansand, Norway
The University Hospital of Northern Norway, Tromsø
🇳🇴Tromsø, Norway
University of Jagiellonian
🇵🇱Kraków, Poland
Hospital Garcia de Orta, Almada
🇵🇹Almada, Portugal
First Moscow State Medical University
🇷🇺Moscow, Russian Federation
Santa Maria Hospital, Lisbon
🇵🇹Lisbon, Portugal
Hospital Santo Antonio, Porto
🇵🇹Porto, Portugal
University Medical Centre Ljubljana
🇸🇮Ljubljana, Slovenia
Clinic Barcelona Hospital Universitari
🇪🇸Barcelona, Catalonia, Spain
University of Colombo
🇱🇰Columbo 8, Sri Lanka
Lund University
🇸🇪Lund, Sweden
Karolinska Institute, Stockholm
🇸🇪Stockholm, Sweden
Linköping University
🇸🇪Stockholm, Sweden
Umeå University
🇸🇪Umeå, Sweden
Uppsala University Hospital
🇸🇪Uppsala, Sweden
University Hospital Basel
🇨🇭Basel, Switzerland
Immunologie-Zentrum Zurich
🇨🇭Zurich, Switzerland
Hacettepe University
🇹🇷Ankara, Turkey
Istanbul University, Cerrahpasa Medical School
🇹🇷Istanbul, Turkey
Marmara University Medical School
🇹🇷Istanbul, Turkey
Fatih University Medical Faculty
🇹🇷Istanbul, Turkey
Haydarpasa Education and Research Hospital
🇹🇷Istanbul, Turkey
Istanbul University, Istanbul Medical School
🇹🇷Istanbul, Turkey
North Cumbria University Hospitals, The Cumberland Infirmary
🇬🇧Carlisle, Cumbria, United Kingdom
Basildon and Thurrock University Hospitals NHS Foundation Trust
🇬🇧Basildon, Essex, United Kingdom
Queen's Hospital
🇬🇧Romford, Essex, United Kingdom
NHS Fife, Whyteman's Brae Hospital, Windygates
🇬🇧Kirkcaldy, Fife, United Kingdom
Southend University Hospital NHS Trust
🇬🇧Westcliff-on-Sea, Essex, United Kingdom
Ipswich Hospital NHS Trust
🇬🇧Ipswich, Suffolk, United Kingdom
Aberdeen Royal Infirmary
🇬🇧Aberdeen, Scotland, United Kingdom
NHS Greater Glasgow & Clyde, Gartnavel Hospital
🇬🇧Glasgow, Scotland, United Kingdom
Epsom and St Helier University Hospitals NHS Trust
🇬🇧Carshalton, Surrey, United Kingdom
University of Birmingham
🇬🇧Birmingham, United Kingdom
Addenbrooke's Hospital
🇬🇧Cambridge, United Kingdom
Dudley Group of Hospitals, NHS FT
🇬🇧Dudley, United Kingdom
Imperial College Healthcare NHS Trust, Hammersmith Hospital
🇬🇧London, United Kingdom
Norfolk and Norwich University Hospital
🇬🇧Norwich, United Kingdom
Nottingham University Hospitals NHS Trust (QMC)
🇬🇧Nottingham, United Kingdom
University of Manchester, Manchester Royal Infirmary
🇬🇧Manchester, United Kingdom
Nuffield Orthopaedic Centre
🇬🇧Oxford, United Kingdom
Oxford University Hospitals NHS Trust (The Churchill Hospital)
🇬🇧Oxford, United Kingdom
Royal Berkshire NHS Trust
🇬🇧Reading, United Kingdom
Heatherwood & Wexham Park Hospitals NHS Foundation Trust
🇬🇧Slough, United Kingdom
Southampton University Hospitals NHS Trust
🇬🇧Southampton, United Kingdom
York Hospital NHS Foundation Trust
🇬🇧York, United Kingdom