AIM: A total of 371 patients under stable warfarin therapy were retrospectively selected to develop a pharmacogenetic algorithm to identify the individual maintenance dose. MATERIALS & METHODS: The variables that were entered into the algorithm were: VKORC1, CYP2C9 and CYP4F2 polymorphisms, body surface area and age. RESULTS: The percentage of cases whose predicted mean weekly warfarin dose was within 20% of the actual maintenance dose was 51.8% considering patients overall, and were 36.2, 66.2 and 55.4%, respectively, taking into account patients requiring low (≤25 mg/week), intermediate (25-45 mg/week) and high (≥45 mg/week) doses. The algorithm could correctly assign 73.8 and 63.2% of patients to the low- and high-dose regimens, respectively. We developed and validated a pharmacogenetic algorithm in a series of Italian patients, we then tested, in the same series of italian patients, the formulas of three published algorithms. These three algorithms were developed and validated by their authors in a series of patients different from our own. The performance of our algorithm in our patients series was slightly higher than that achieved when using the three other algorithms in our patients series. CONCLUSION: The high predictive accuracy of low and high warfarin requirements of our algorithm warrants its application in prospective studies for clinical validation.
AIM: A total of 371 patients under stable warfarin therapy were retrospectively selected to develop a pharmacogenetic algorithm to identify the individual maintenance dose. MATERIALS & METHODS: The variables that were entered into the algorithm were: VKORC1, CYP2C9 and CYP4F2 polymorphisms, body surface area and age. RESULTS: The percentage of cases whose predicted mean weekly warfarin dose was within 20% of the actual maintenance dose was 51.8% considering patients overall, and were 36.2, 66.2 and 55.4%, respectively, taking into account patients requiring low (≤25 mg/week), intermediate (25-45 mg/week) and high (≥45 mg/week) doses. The algorithm could correctly assign 73.8 and 63.2% of patients to the low- and high-dose regimens, respectively. We developed and validated a pharmacogenetic algorithm in a series of Italian patients, we then tested, in the same series of italian patients, the formulas of three published algorithms. These three algorithms were developed and validated by their authors in a series of patients different from our own. The performance of our algorithm in our patients series was slightly higher than that achieved when using the three other algorithms in our patients series. CONCLUSION: The high predictive accuracy of low and high warfarin requirements of our algorithm warrants its application in prospective studies for clinical validation.
Authors: Elisa Danese; Sara Raimondi; Martina Montagnana; Angela Tagetti; Taimour Langaee; Paola Borgiani; Cinzia Ciccacci; Antonio J Carcas; Alberto M Borobia; Hoi Y Tong; Cristina Dávila-Fajardo; Mariana Rodrigues Botton; Stephane Bourgeois; Panos Deloukas; Michael D Caldwell; Jim K Burmester; Richard L Berg; Larisa H Cavallari; Katarzyna Drozda; Min Huang; Li-Zi Zhao; Han-Jing Cen; Rocio Gonzalez-Conejero; Vanessa Roldan; Yusuke Nakamura; Taisei Mushiroda; Inna Y Gong; Richard B Kim; Keita Hirai; Kunihiko Itoh; Carlos Isaza; Leonardo Beltrán; Enrique Jiménez-Varo; Marisa Cañadas-Garre; Alice Giontella; Marianne K Kringen; Kari Bente Foss Haug; Hye Sun Gwak; Kyung Eun Lee; Pietro Minuz; Ming Ta Michael Lee; Steven A Lubitz; Stuart Scott; Cristina Mazzaccara; Lucia Sacchetti; Ece Genç; Mahmut Özer; Anil Pathare; Rajagopal Krishnamoorthy; Andras Paldi; Virginie Siguret; Marie-Anne Loriot; Vijay Kumar Kutala; Guilherme Suarez-Kurtz; Jamila Perini; Josh C Denny; Andrea H Ramirez; Balraj Mittal; Saurabh Singh Rathore; Hersh Sagreiya; Russ Altman; Mohamed Hossam A Shahin; Sherief I Khalifa; Nita A Limdi; Charles Rivers; Aditi Shendre; Chrisly Dillon; Ivet M Suriapranata; Hong-Hao Zhou; Sheng-Lan Tan; Vacis Tatarunas; Vaiva Lesauskaite; Yumao Zhang; Anke H Maitland-van der Zee; Talitha I Verhoef; Anthonius de Boer; Monica Taljaard; Carlo Federico Zambon; Vittorio Pengo; Jieying Eunice Zhang; Munir Pirmohamed; Julie A Johnson; Cristiano Fava Journal: Clin Pharmacol Ther Date: 2019-02-17 Impact factor: 6.875