Diagnostic algorithm for antineutrophil cytoplasmic antibody-associated systemic vasculitis

Area of Science:

• Rheumatology
• Immunology
• Internal Medicine

Background:

• Antineutrophil cytoplasm antibody (ANCA)-associated vasculitis (AAV) are rare autoimmune disorders with severe, potentially fatal, multi-organ damage.
• AAV encompasses granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA).
• Accurate diagnosis is challenging, particularly differentiating MPA due to the absence of granulomatous inflammation criteria.

Purpose of the Study:

• To develop and validate a diagnostic algorithm for classifying patients with AAV.
• To improve the diagnostic accuracy and nosological affiliation of AAV subtypes.
• To assess the clinical utility of the algorithm in differentiating GPA, MPA, and EGPA.

Main Methods:

• Analysis of 251 AAV patients (mean age 43.1 years, 40% male).
• Development of a diagnostic algorithm incorporating EGPA criteria and surrogate markers for granulomatous inflammation (SG) and vasculitis (SV).
• Classification of MPA based on the absence of EGPA criteria, presence of SV, and absence of SG.

Main Results:

• The algorithm determined the nosological affiliation of AAV in 99% of patients.
• GPA (53%) and MPA (37%) were most common; EGPA was rare (9%).
• MPA showed higher mortality (18%) vs. GPA/EGPA (7-5%). MPA with anti-proteinase 3 antibody had lower survival due to alveolar hemorrhage and glomerulonephritis. Relapse rates varied: EGPA (80%), GPA (64%), MPA (49%).

Conclusions:

• The proposed algorithm significantly enhances AAV diagnosis and classification.
• The algorithm's effectiveness is supported by observed differences in mortality and relapse rates among AAV subtypes.
• Systematic clinical examination remains crucial for accurate AAV diagnosis.