Background: Diagnosis of hereditary angioedema (HAE) poses challenges because of its rarity and its overlapping symptoms with allergic and gastrointestinal conditions, resulting in misdiagnosis.
Objective: We developed a predictive score using clinical variables for suspected HAE patients with C1 inhibitor deficiency (HAE-C1INH) to increase suspicion of HAE and thus improve diagnosis.
Methods: The HADES (HAE diagnosis evaluation score) study used a nationwide retrospective cohort of individuals with suspected HAE-C1INH in Argentina. A questionnaire was designed to collect relevant clinical information on possible predictors for HAE. Blood samples were analyzed for C1-INH/C1q levels and C1-INH function. A predictive score was developed from the odds ratios derived from multivariate logistic regression analysis.
Results: The study included 2423 individuals (1642 suspected index cases and 781 family cases). Only patients with confirmed HAE types I or II (n = 499) were included in the final analysis; acquired angioedema/F12 gene variants were excluded. Eight clinical variables were identified as independent predictors of HAE: age at onset ≤20 years, recurrent limb edema, abdominal pain, vomiting, trauma as a trigger, absence of wheals, family history of angioedema, and recurrent edema lasting ≥24 hours. The predictive score demonstrated favorable performance in identifying HAE cases within the index population (range, 0-18.5), with low scores (1.5-6.5) associated with high sensitivity (100%) and negative predictive value (100%), and high scores (≥15) associated with high specificity (99.4%) and positive predictive value (75.0%).
Conclusions: The predictive HADES offers a simple and efficient method for improving testing for suspicion of HAE by using clinical parameters. Further validation studies are required to confirm its reliability and accuracy.
Authors:
Zwiener∙R, Zamora R, Galmarini CM, Brion L, Arias L, Pino A, Rozenfeld P
Affiliations:
Hospital Universitario Austral, Pilar, Buenos Aires, Argentina
Medicus, Buenos Aires, Argentina,
Topazium Artificial Intelligence, Madrid, Spain,
Takeda Argentina SA, Buenos Aires, Argentina,
Takeda Pharmaceuticals International AG Singapore Branch Singapore
Instituto de Estudios Inmunológicos y Fisiopatológicos, UNLP, La Plata, Argentina
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