Characteristics and Drug Utilization of Patients with Hereditary Angioedema in Italy, a Real-World Analysis

Abstract

This real-world analysis investigated the characteristics and treatment patterns of patients with hereditary angioedema (HAE) in Italy using the administrative data of health units across Italy. Patients were identified via exemption code or HAE-specific treatments (thus, all known forms, type I, II and, III, were included). The index date was that of first prescription of HAE treatments within the inclusion period (01/2010–06/2021) or of the date of exemption. The number of HAE patients included was 148 (43.2% male, mean age 43.3 years). Gastrointestinal disorders affected 36.5% patients, hypertension affected 28.4%, hypercholesterolemia affected 11.5%, and depression affected 9.5%. The frequent gastrointestinal involvement was further confirmed by the use of antiemetics and systemic antihistamines that doubled after the index date. Among patients enrolled by treatment (n = 125), n = 105 (84%) were receiving a treatment for acute attacks. This analysis provided insights into the characterization of patients with HAE and their management in Italian clinical practice, suggesting that an unmet therapeutic need could be present for such patients in terms of the clinical burden.

Letter in reply: network meta-analysis for indirect comparison of lanadelumab and for the treatment of hereditary angioedema

“…There is limited literature reporting on prognostic factors in HAE. A search in Pubmed ( https://pubmed.ncbi.nlm.nih.gov/ ) identified two studies reporting on prognostic factors (Banerji et al [ 5 ], Sundler Björkman et al [ 6 ]), as well as three studies reporting on confounders (Longhurst et al [ 7 ], Giacomini et al [ 8 ], Wang et al [ 9 ]). A study protocol published by the Paul Ehrlich Institute (by CENTOGENE AG 2020 [ 10 ]) reported on which variables were adjusted for in the statistical analysis.…”

“…The CENTOGENE AG study protocol [ 10 ] describes using statistical analysis such as Poisson regression adjusting for differences in follow-up duration, and potential confounders age, sex and number of attacks per year on standard therapy. Giacomini et al [ 8 ] mention disease severity as a potential confounding factor. Wang et al [ 9 ] conducted a multivariate linear regression adjusting for age, sex, smoking habit, alcohol consumption and long-term prophylaxis as potential confounding factors.…”