Real-time edge computing design for physiological signal analysis and classification.

Physiological Signals like Electromography (EMG) and Electroencephalography (EEG) can be analysed and decoded to provide vital information that can be used in a range of applications like rehabilitative robotics and remote device control. The process of acquiring and using these signals requires many compute-intensive tasks like signal acquisition, signal processing, feature extraction, and machine learning. Performing these activities on a PC-based system with well-established software tools like Python and Matlab is the first step in designing solutions based upon these signals. In the application domain of rehabilitative robotics, one of the main goals is to develop solutions that can be deployed for the use of individuals who need them in improving their Acitivities-for-Daily Living (ADL). To achieve this objective, the final solution must be deployed onto an embedded solution that allows high portability and ease-of-use. Porting a solution from a PC-based environment onto a resource-constraint one such as a microcontroller poses many challenges. In this research paper, we propose the use of an ARM-based Corex M-4 processor. We explore the various stages of the design from the initial testing and validation, to the deployment of the proposed algorithm on the controller, and further investigate the use of Cepstrum features to obtain a high classification accuracy with minimal input features. The proposed solution is able to achieve an average classification accuracy of 95.34% for all five classes in the EMG domain and 96.16% in the EEG domain on the embedded board.

Stroke frequency, associated factors, and clinical features in primary systemic vasculitis: a multicentric observational study.

OBJECTIVES: The cerebral vessels may be affected in primary systemic vasculitis (PSV), but little is known about cerebrovascular events (CVEs) in this population. This study aimed to determine the frequency of CVEs at the time of diagnosis of PSV, to identify factors associated with CVEs in PSV, and to explore features and outcomes of stroke in patients with PSV. METHODS: Data from adults newly diagnosed with PSV within the Diagnostic and Classification Criteria in VASculitis (DCVAS) study were analysed. Demographics, risk factors for vascular disease, and clinical features were compared between patients with PSV with and without CVE. Stroke subtypes and cumulative incidence of recurrent CVE during a prospective 6-month follow-up were also assessed. RESULTS: The analysis included 4828 PSV patients, and a CVE was reported in 169 (3.50%, 95% CI 3.00-4.06): 102 (2.13% 95% CI 1.73-2.56) with stroke and 81 (1.68% 95% CI 1.33-2.08) with transient ischemic attack (TIA). The frequency of CVE was highest in Behçet's disease (9.5%, 95% CI 5.79-14.37), polyarteritis nodosa (6.2%, 95% CI 3.25-10.61), and Takayasu's arteritis (6.0%, 95% CI 4.30-8.19), and lowest in microscopic polyangiitis (2.2%, 95% CI 1.09-3.86), granulomatosis with polyangiitis (2.0%, 95% CI 1.20-3.01), cryoglobulinaemic vasculitis (1.9%, 95% CI 0.05-9.89), and IgA-vasculitis (Henoch-Schönlein) (0.4%, 95% CI 0.01-2.05). PSV patients had a 11.9% cumulative incidence of recurrent CVE during a 6-month follow-up period. CONCLUSION: CVEs affect a significant proportion of patients at time of PSV diagnosis, and the frequency varies widely among different vasculitis, being higher in Behçet's. Overall, CVE in PSV is not explained by traditional vascular risk factors and has a high risk of CVE recurrence.

2022 American College of Rheumatology/EULAR Classification Criteria for Takayasu Arteritis.

OBJECTIVE: To develop and validate new classification criteria for Takayasu arteritis (TAK). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in 6 phases: 1) identification of candidate criteria items, 2) collection of candidate items present at diagnosis, 3) expert panel review of cases, 4) data-driven reduction of candidate items, 5) derivation of a points-based classification score in a development data set, and 6) validation in an independent data set. RESULTS: The development data set consisted of 316 cases of TAK and 323 comparators. The validation data set consisted of an additional 146 cases of TAK and 127 comparators. Age ≤60 years at diagnosis and imaging evidence of large-vessel vasculitis were absolute requirements to classify a patient as having TAK. The final criteria items and weights were as follows: female sex (+1), angina (+2), limb claudication (+2), arterial bruit (+2), reduced upper extremity pulse (+2), reduced pulse or tenderness of a carotid artery (+2), blood pressure difference between arms of ≥20 mm Hg (+1), number of affected arterial territories (+1 to +3), paired artery involvement (+1), and abdominal aorta plus renal or mesenteric involvement (+3). A patient could be classified as having TAK with a cumulative score of ≥5 points. When these criteria were tested in the validation data set, the model area under the curve was 0.97 (95% confidence interval [95% CI] 0.94-0.99) with a sensitivity of 93.8% (95% CI 88.6-97.1%) and specificity of 99.2% (95% CI 96.7-100.0%). CONCLUSION: The 2022 American College of Rheumatology/EULAR classification criteria for TAK are now validated for use in research.

2022 American College of Rheumatology/EULAR Classification Criteria for Giant Cell Arteritis.

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 6 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/liter (+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% confidence interval [95% CI] 0.88-0.94) with a sensitivity of 87.0% (95% CI 82.0-91.0%) and specificity of 94.8% (95% CI 91.0-97.4%). CONCLUSION: The 2022 American College of Rheumatology/EULAR GCA classification criteria are now validated for use in clinical research.

2022 American College of Rheumatology/EULAR classification criteria for Takayasu arteritis.

OBJECTIVE: To develop and validate new classification criteria for Takayasu arteritis (TAK). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in six phases: (1) identification of candidate criteria items, (2) collection of candidate items present at diagnosis, (3) expert panel review of cases, (4) data-driven reduction of candidate items, (5) derivation of a points-based classification score in a development data set and (6) validation in an independent data set. RESULTS: The development data set consisted of 316 cases of TAK and 323 comparators. The validation data set consisted of an additional 146 cases of TAK and 127 comparators. Age ≤60 years at diagnosis and imaging evidence of large-vessel vasculitis were absolute requirements to classify a patient as having TAK. The final criteria items and weights were as follows: female sex (+1), angina (+2), limb claudication (+2), arterial bruit (+2), reduced upper extremity pulse (+2), reduced pulse or tenderness of a carotid artery (+2), blood pressure difference between arms of ≥20 mm Hg (+1), number of affected arterial territories (+1 to +3), paired artery involvement (+1) and abdominal aorta plus renal or mesenteric involvement (+3). A patient could be classified as having TAK with a cumulative score of ≥5 points. When these criteria were tested in the validation data set, the model area under the curve was 0.97 (95% CI 0.94 to 0.99) with a sensitivity of 93.8% (95% CI 88.6% to 97.1%) and specificity of 99.2% (95% CI 96.7% to 100.0%). CONCLUSION: The 2022 American College of Rheumatology/EULAR classification criteria for TAK are now validated for use in research.

2022 American College of Rheumatology/EULAR classification criteria for giant cell arteritis.

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.

Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis.

A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. The reliability of the application is highly dependent on an ability to decode intentions accurately based on captured neuromuscular signals. Physiological signals such as Electromyography (EMG) and Electroencephalography (EEG) generated by neuromuscular activities contain intrinsic patterns for users' particular actions. Such actions can generally be classified as motor states, such as Forward, Reverse, Hand-Grip, and Hand-Release. To classify these motor states truthfully, the signals must be captured and decoded correctly. This paper proposes a novel classification technique using a Fuzzy Inference System (FIS) and a Long Short-Term Memory (LSTM) network to classify the motor states based on EMG signals. Existing EMG signal classification techniques generally rely on features derived from data captured at a specific time instance. This typical approach does not consider the temporal correlation of the signal in the entire window. This paper proposes an LSTM with a Fuzzy Logic method to classify four major hand movements: forward, reverse, raise, and lower. Features associated with the pattern generated throughout the motor state movement were extracted by exploring published data within a given time window. The classification results can achieve a 91.3% accuracy for the 4-way action (Forward/Reverse/GripUp/RelDown) and 95.1% (Forward/Reverse Action) and 96.7% (GripUp/RelDown action) for 2-way actions. The proposed mechanism demonstrates high-level, human-interpretable results that can be employed in rehabilitation or medical-device industries.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Granulomatosis With Polyangiitis.

OBJECTIVE: To develop and validate revised classification criteria for granulomatosis with polyangiitis (GPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in 5 phases: 1) identification of candidate criteria items using consensus methodology, 2) prospective collection of candidate items present at the time of diagnosis, 3) data-driven reduction of the number of candidate items, 4) expert panel review of cases to define the reference diagnosis, and 5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for GPA consisted of 578 cases of GPA and 652 comparators. The validation set consisted of an additional 146 cases of GPA and 161 comparators. From 91 candidate items, regression analysis identified 26 items for GPA, 10 of which were retained. The final criteria and their weights were as follows: bloody nasal discharge, nasal crusting, or sino-nasal congestion (+3); cartilaginous involvement (+2); conductive or sensorineural hearing loss (+1); cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3 ANCA positivity (+5); pulmonary nodules, mass, or cavitation on chest imaging (+2); granuloma or giant cells on biopsy (+2); inflammation or consolidation of the nasal/paranasal sinuses on imaging (+1); pauci-immune glomerulonephritis (+1); perinuclear ANCA or antimyeloperoxidase ANCA positivity (-1); and eosinophil count ≥1 × 109 /liter (-4). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having GPA if the cumulative score was ≥5 points. When these criteria were tested in the validation data set, the sensitivity was 93% (95% confidence interval [95% CI] 87-96%) and the specificity was 94% (95% CI 89-97%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for GPA demonstrate strong performance characteristics and are validated for use in research.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Eosinophilic Granulomatosis With Polyangiitis.

OBJECTIVE: To develop and validate revised classification criteria for eosinophilic granulomatosis with polyangiitis (EGPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in 5 phases: 1) identification of candidate criteria items using consensus methodology, 2) prospective collection of candidate items present at the time of diagnosis, 3) data-driven reduction of the number of candidate items, 4) expert panel review of cases to define the reference diagnosis, and 5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for EGPA consisted of 107 cases of EGPA and 450 comparators. The validation set consisted of an additional 119 cases of EGPA and 437 comparators. From 91 candidate items, regression analysis identified 11 items for EPGA, 7 of which were retained. The final criteria and their weights were as follows: maximum eosinophil count ≥1 × 109 /liter (+5), obstructive airway disease (+3), nasal polyps (+3), cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3 ANCA positivity (-3), extravascular eosinophilic predominant inflammation (+2), mononeuritis multiplex/motor neuropathy not due to radiculopathy (+1), and hematuria (-1). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having EGPA if the cumulative score was ≥6 points. When these criteria were tested in the validation data set, the sensitivity was 85% (95% confidence interval [95% CI] 77-91%) and the specificity was 99% (95% CI 98-100%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for EGPA demonstrate strong performance characteristics and are validated for use in research.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Microscopic Polyangiitis.

OBJECTIVE: To develop and validate classification criteria for microscopic polyangiitis (MPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in 5 phases: 1) identification of candidate items using consensus methodology, 2) prospective collection of candidate items present at the time of diagnosis, 3) data-driven reduction of the number of candidate items, 4) expert panel review of cases to define the reference diagnosis, and 5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for MPA consisted of 149 cases of MPA and 408 comparators. The validation set consisted of an additional 142 cases of MPA and 414 comparators. From 91 candidate items, regression analysis identified 10 items for MPA, 6 of which were retained. The final criteria and their weights were as follows: perinuclear antineutrophil cytoplasmic antibody (ANCA) or anti-myeloperoxidase-ANCA positivity (+6), pauci-immune glomerulonephritis (+3), lung fibrosis or interstitial lung disease (+3), sino-nasal symptoms or signs (-3), cytoplasmic ANCA or anti-proteinase 3 ANCA positivity (-1), and eosinophil count ≥1 × 109 /liter (-4). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having MPA with a cumulative score of ≥5 points. When these criteria were tested in the validation data set, the sensitivity was 91% (95% confidence interval [95% CI] 85-95%) and the specificity was 94% (95% CI 92-96%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for MPA are now validated for use in clinical research.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for microscopic polyangiitis.

OBJECTIVE: To develop and validate classification criteria for microscopic polyangiitis (MPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for MPA consisted of 149 cases of MPA and 408 comparators. The validation set consisted of an additional 142 cases of MPA and 414 comparators. From 91 candidate items, regression analysis identified 10 items for MPA, 6 of which were retained. The final criteria and their weights were as follows: perinuclear antineutrophil cytoplasmic antibody (ANCA) or anti-myeloperoxidase-ANCA positivity (+6), pauci-immune glomerulonephritis (+3), lung fibrosis or interstitial lung disease (+3), sino-nasal symptoms or signs (-3), cytoplasmic ANCA or anti-proteinase 3 ANCA positivity (-1) and eosinophil count ≥1×109/L (-4). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having MPA with a cumulative score of ≥5 points. When these criteria were tested in the validation data set, the sensitivity was 91% (95% CI 85% to 95%) and the specificity was 94% (95% CI 92% to 96%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for MPA are now validated for use in clinical research.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for granulomatosis with polyangiitis.

OBJECTIVE: To develop and validate revised classification criteria for granulomatosis with polyangiitis (GPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate criteria items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for GPA consisted of 578 cases of GPA and 652 comparators. The validation set consisted of an additional 146 cases of GPA and 161 comparators. From 91 candidate items, regression analysis identified 26 items for GPA, 10 of which were retained. The final criteria and their weights were as follows: bloody nasal discharge, nasal crusting or sino-nasal congestion (+3); cartilaginous involvement (+2); conductive or sensorineural hearing loss (+1); cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3 ANCA positivity (+5); pulmonary nodules, mass or cavitation on chest imaging (+2); granuloma or giant cells on biopsy (+2); inflammation or consolidation of the nasal/paranasal sinuses on imaging (+1); pauci-immune glomerulonephritis (+1); perinuclear ANCA or antimyeloperoxidase ANCA positivity (-1); and eosinophil count ≥1×109 /L (-4). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having GPA if the cumulative score was ≥5 points. When these criteria were tested in the validation data set, the sensitivity was 93% (95% CI 87% to 96%) and the specificity was 94% (95% CI 89% to 97%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for GPA demonstrate strong performance characteristics and are validated for use in research.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Eosinophilic Granulomatosis with Polyangiitis.

OBJECTIVE: To develop and validate revised classification criteria for eosinophilic granulomatosis with polyangiitis (EGPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate criteria items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for EGPA consisted of 107 cases of EGPA and 450 comparators. The validation set consisted of an additional 119 cases of EGPA and 437 comparators. From 91 candidate items, regression analysis identified 11 items for EPGA, 7 of which were retained. The final criteria and their weights were as follows: maximum eosinophil count ≥1×109/L (+5), obstructive airway disease (+3), nasal polyps (+3), cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3-ANCA positivity (-3), extravascular eosinophilic predominant inflammation (+2), mononeuritis multiplex/motor neuropathy not due to radiculopathy (+1) and haematuria (-1). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having EGPA if the cumulative score was ≥6 points. When these criteria were tested in the validation data set, the sensitivity was 85% (95% CI 77% to 91%) and the specificity was 99% (95% CI 98% to 100%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Eosinophilic Granulomatosis with Polyangiitis demonstrate strong performance characteristics and are validated for use in research.

Clinicopathologic Associations in a Large International Cohort of Patients With Giant Cell Arteritis.

OBJECTIVE: In addition to aiding in diagnosis, histopathologic findings from temporal artery biopsy (TAB) specimens in giant cell arteritis (GCA) may be valuable for their associations with clinical features of the disease. This study was undertaken to compare histopathologic findings on TAB with biopsy interpretation and demographic, clinical, and imaging features at time of diagnosis. METHODS: Patients with a clinical diagnosis of GCA who had a TAB were selected from an international, multicenter observational cohort of vasculitis. Associations between demographic, clinical, radiographic, and histopathologic features were identified using bivariate testing and multivariate regression modeling. RESULTS: Of 705 patients with GCA who underwent TAB, 69% had histopathologic evidence of definite vasculitis. Specific histopathologic findings included the presence of giant cells (51%), fragmentation of the internal elastic lamina (41%), intimal thickening (33%), and predominantly mononuclear leukocyte infiltration (32%). Histopathologic interpretation of definite vasculitis was independently associated with giant cells (odds ratio [OR] 151.8 [95% confidence interval (95% CI) 60.2-551.6]), predominantly mononuclear leukocyte infiltration (OR 11.8 [95% CI 5.9-24.9]), and fragmentation of the internal elastic lamina (OR 3.7 [95% CI 1.9-7.4]). A halo sign on temporal artery ultrasound and luminal damage of large arteries on angiography were significantly associated with presence of giant cells (OR 2.6 [95% CI 1.1-6.5] and OR 2.4 [95% CI 1.1-5.2], respectively). Specific histopathologic findings were associated with older age, but no associations were identified with vision loss or other clinical features. CONCLUSION: Histopathologic findings in GCA are strongly associated with the clinical diagnosis of GCA but have a limited role in identifying patterns of disease.

Pulmonary involvement in primary systemic vasculitides.

OBJECTIVES: This study describes the spectrum and initial impact of pulmonary manifestations in the primary systemic vasculitides. METHODS: Description and comparison of pulmonary manifestations in adults with Takayasu's arteritis (TAK), GCA, granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), eosinophilic GPA (EGPA), polyarteritis nodosa (PAN) and IgA vasculitis (IgAV), using data collected within the Diagnostic and Classification Criteria in Vasculitis study. RESULTS: Data from 1952 patients with primary vasculitides were included: 170 TAK, 657 GCA, 555 GPA, 223 MPA, 146 EGPA, 153 IgAV and 48 PAN. Pulmonary manifestations were observed in patients with TAK (21.8%), GCA (15.8%), GPA (64.5%), MPA (65.9%), EGPA (89.0%), PAN (27.1%) and IgAV (5.9%). Dyspnoea occurred in patients with TAK (14.7%), GCA (7.8%), GPA (41.8%), MPA (43.5%), EGPA (65.8%), PAN (18.8%) and IgAV (2.6%). Cough was reported in TAK (7.6%), GCA (9.3%), GPA (34.8%), MPA (37.7%), EGPA (55.5%), PAN (16.7%) and IgAV (3.3%). Haemoptysis occurred mainly in patients with ANCA-associated vasculitis (AAV). Fibrosis on imaging at diagnosis was documented in GPA (1.9%), MPA (24.9%) and EGPA (6.3%). Only patients with AAV (GPA 2.7%, MPA 2.7% and EGPA 3.4%) required mechanical ventilation. At 6 months, the presence of at least one pulmonary item in the Vasculitis Damage Index was observed in TAK (4.1%), GCA (3.3%), GPA (15.4%), MPA (28.7%), EGPA (52.7%), PAN (6.2%) and IgAV (1.3%). CONCLUSION: Pulmonary manifestations can occur in all primary systemic vasculitides, but are more frequent and more often associated with permanent damage in AAV.

Patterns of Arterial Disease in Takayasu Arteritis and Giant Cell Arteritis.

OBJECTIVE: To identify and validate, using computer-driven methods, patterns of arterial disease in Takayasu arteritis (TAK) and giant cell arteritis (GCA). METHODS: Patients with TAK or GCA were studied from the Diagnostic and Classification Criteria for Vasculitis (DCVAS) cohort and a combined North American cohort. Case inclusion required evidence of large-vessel involvement, defined as stenosis, occlusion, or aneurysm by angiography/ultrasonography, or increased 18 F-fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET) in at least 1 of 11 specified arterial territories. K-means cluster analysis identified groups of patients based on the pattern of arterial involvement. Cluster groups were identified in the DCVAS cohort and independently validated in the North American cohort. RESULTS: A total of 1,068 patients were included (DCVAS cohort: TAK = 461, GCA = 217; North American cohort: TAK = 225, GCA = 165). Six distinct clusters of patients were identified in DCVAS and validated in the North American cohort. Patients with TAK were more likely to have disease in the abdominal vasculature, bilateral disease of the subclavian and carotid arteries, or focal disease limited to the left subclavian artery than GCA (P < 0.01). Patients with GCA were more likely to have diffuse disease, involvement of bilateral axillary/subclavian arteries, or minimal disease without a definable pattern than TAK (P < 0.01). Patients with TAK were more likely to have damage by angiography, and patients with GCA were more likely to have arterial FDG uptake by PET without associated vascular damage. CONCLUSION: Arterial patterns of disease highlight both shared and divergent vascular patterns between TAK and GCA and should be incorporated into classification criteria for large-vessel vasculitis.

Diagnostic Assessment Strategies and Disease Subsets in Giant Cell Arteritis: Data From an International Observational Cohort.

OBJECTIVE: Diagnostic assessment in giant cell arteritis (GCA) is rapidly changing as vascular imaging becomes more available. This study was undertaken to determine if clinical GCA subsets have distinct profiles or reflect differential diagnostic assessments. METHODS: Patients were recruited from an international cohort and divided into 4 subsets based on a temporal artery (TA) abnormality (positive TA biopsy [TAB] or halo sign on TA ultrasound [TA-US]) and/or evidence of large vessel (LV) involvement on imaging: 1) both TA abnormality and LV involvement (TA+/LV+ GCA); 2) TA abnormality without LV involvement (TA+/LV- GCA); 3) LV involvement without TA abnormality (TA-/LV+ GCA); and 4) clinically diagnosed GCA without LV involvement or TA abnormality (TA-/LV- GCA). RESULTS: Nine hundred forty-one patients with GCA were recruited from 72 international study sites. Most patients received multiple forms of diagnostic assessment, including TAB (n = 705 [75%]), TA-US (n = 328 [35%]), and LV imaging (n = 534 [57%]). Assessment using TAB, TA-US, and LV imaging confirmed the diagnosis of GCA in 66%, 79%, and 40% of cases, respectively. GCA subsets had distinct profiles independent of diagnostic assessment strategies. TA+/LV- were the most common subset (51%), with a high burden of cranial ischemia. Those in the TA-/LV- subset (26%) had a high prevalence of cranial ischemia and musculoskeletal symptoms. Patients in the TA-/LV+ subset (12%) had prevalent upper extremity vascular abnormalities and a low prevalence of vision loss, and those in the TA+/LV+ subset (11%) were older and had a high prevalence of cranial ischemia, constitutional symptoms, and elevated acute-phase reactant levels. CONCLUSION: Vascular imaging is increasingly incorporated into the diagnostic assessment of GCA and identifies clinical subsets of patients based on involvement of temporal and extracranial arteries.