STUDY OBJECTIVE: To investigate reports of thrombotic events associated with the use of C1 esterase inhibitor products in patients with hereditary angioedema in the United States. DESIGN: Retrospective data mining analysis. SOURCE: The United States Food and Drug Administration (FDA) adverse event reporting system (AERS) database. MEASUREMENTS AND MAIN RESULTS: Case reports of C1 esterase inhibitor products, thrombotic events, and C1 esterase inhibitor product-associated thrombotic events (i.e., combination cases) were extracted from the AERS database, using the time frames of each respective product's FDA approval date through the second quarter of 2011. Bayesian statistical methodology within the neural network architecture was implemented to identify potential signals of a drug-associated adverse event. A potential signal is generated when the lower limit of the 95% 2-sided confidence interval of the information component, denoted by IC₀₂₅ , is greater than zero. This suggests that the particular drug-associated adverse event was reported to the database more often than statistically expected from reports available in the database. Ten combination cases of thrombotic events associated with the use of one C1 esterase inhibitor product (Cinryze) were identified in patients with hereditary angioedema. A potential signal demonstrated by an IC₀₂₅ value greater than zero (IC₀₂₅ = 2.91) was generated for these combination cases. CONCLUSION: The extracted cases from the AERS indicate continuing reports of thrombotic events associated with the use of one C1 esterase inhibitor product among patients with hereditary angioedema. The AERS is incapable of establishing a causal link and detecting the true frequency of an adverse event associated with a drug; however, potential signals of C1 esterase inhibitor product-associated thrombotic events among patients with hereditary angioedema were identified in the extracted combination cases.
STUDY OBJECTIVE: To investigate reports of thrombotic events associated with the use of C1 esterase inhibitor products in patients with hereditary angioedema in the United States. DESIGN: Retrospective data mining analysis. SOURCE: The United States Food and Drug Administration (FDA) adverse event reporting system (AERS) database. MEASUREMENTS AND MAIN RESULTS: Case reports of C1 esterase inhibitor products, thrombotic events, and C1 esterase inhibitor product-associated thrombotic events (i.e., combination cases) were extracted from the AERS database, using the time frames of each respective product's FDA approval date through the second quarter of 2011. Bayesian statistical methodology within the neural network architecture was implemented to identify potential signals of a drug-associated adverse event. A potential signal is generated when the lower limit of the 95% 2-sided confidence interval of the information component, denoted by IC₀₂₅ , is greater than zero. This suggests that the particular drug-associated adverse event was reported to the database more often than statistically expected from reports available in the database. Ten combination cases of thrombotic events associated with the use of one C1 esterase inhibitor product (Cinryze) were identified in patients with hereditary angioedema. A potential signal demonstrated by an IC₀₂₅ value greater than zero (IC₀₂₅ = 2.91) was generated for these combination cases. CONCLUSION: The extracted cases from the AERS indicate continuing reports of thrombotic events associated with the use of one C1 esterase inhibitor product among patients with hereditary angioedema. The AERS is incapable of establishing a causal link and detecting the true frequency of an adverse event associated with a drug; however, potential signals of C1 esterase inhibitor product-associated thrombotic events among patients with hereditary angioedema were identified in the extracted combination cases.
Authors: Stephen Betschel; Jacquie Badiou; Karen Binkley; Jacques Hébert; Amin Kanani; Paul Keith; Gina Lacuesta; Bill Yang; Emel Aygören-Pürsün; Jonathan Bernstein; Konrad Bork; Teresa Caballero; Marco Cicardi; Timothy Craig; Henriette Farkas; Hilary Longhurst; Bruce Zuraw; Henrik Boysen; Rozita Borici-Mazi; Tom Bowen; Karen Dallas; John Dean; Kelly Lang-Robertson; Benoît Laramée; Eric Leith; Sean Mace; Christine McCusker; Bill Moote; Man-Chiu Poon; Bruce Ritchie; Donald Stark; Gordon Sussman; Susan Waserman Journal: Allergy Asthma Clin Immunol Date: 2014-10-24 Impact factor: 3.406
Authors: B L Zuraw; M Cicardi; H J Longhurst; J A Bernstein; H H Li; M Magerl; I Martinez-Saguer; S M M Rehman; P Staubach; H Feuersenger; R Parasrampuria; J Sidhu; J Edelman; T Craig Journal: Allergy Date: 2015-08-11 Impact factor: 13.146
Authors: Ingrid Stroo; Jack Yang; Adam A Anas; J Daan de Boer; Gerard van Mierlo; Dorina Roem; Diana Wouters; Ruchira Engel; Joris J T H Roelofs; Cornelis van 't Veer; Tom van der Poll; Sacha Zeerleder Journal: PLoS One Date: 2017-10-16 Impact factor: 3.240
Authors: Jon A Kenniston; Ryan R Faucette; Diana Martik; Stephen R Comeau; Allison P Lindberg; Kris J Kopacz; Gregory P Conley; Jie Chen; Malini Viswanathan; Niksa Kastrapeli; Janja Cosic; Shauna Mason; Mike DiLeo; Jan Abendroth; Petr Kuzmic; Robert C Ladner; Thomas E Edwards; Christopher TenHoor; Burt A Adelman; Andrew E Nixon; Daniel J Sexton Journal: J Biol Chem Date: 2014-06-26 Impact factor: 5.157