Abstract
Introduction: Post-marketing data on the risks associated with direct oral anticoagulants (DOACs) are conflicting and only few studies evaluated a comparison between each different DOAC. Real-world data from pharmacovigilance databases can help to better define the safety profile of each DOAC and warfarin. However, Correspondence Analysis (CA) could represent a useful tool in this context. Objective: In the attempt to assess the usefulness of CA as a signal detection pharmacovigilance tool, we applied this method to the Italian Pharmacovigilance Database (RNF, Rete Nazionale di Farmacovigilanza), by comparing with disproportionality analysis on warfarin and DOACs. Methods: Study based on AEs sent to RNF by Campania Region from 2008 to 2021, in which warfarin, dabigatran, apixaban, edoxaban or rivaroxaban were reported as suspected drug. AEs were clustered into three Standardized MedDRA Queries (SMQs): Central Nervous System Haemorrhages and Conditions (CNSH), GastroIntestinal Perforation, Ulceration, Obstruction or Haemorrhages (GIPUOH) and other Haemorrhages (HH). Non-haemorrhagic AEs were included in a fourth cluster (nHH). Results: We retrieved 1,161 reports: 41.5% are associated to warfarin, 21.0% to dabigatran, 17.8% to rivaroxaban, 13.9% to apixaban and 5.8% to edoxaban. No significant differences in age distribution were observed. Results of CA showed that dabigatran and warfarin have the highest contribution (44.910 and 47.656, respectively) to the inertia of Dimension 1 as well as apixaban and dabigatran to the inertia of Dimension 2 (53.768 and 30.488, respectively). Edoxaban and rivaroxaban showed a negligible total contribution. CA biplot showed positive associations between warfarin and HH, apixaban and CNSH and dabigatran and nHH. Conclusion: Results seem to confirm that DOACs are not interchangeable. Apixaban was surprisingly associated with a higher risk of cerebral haemorrhage. As expected, our data support the better safety profile of DOACs than warfarin in terms of skin and respiratory tract hemorrhagic risks. Finally, we showed how CA could play a complementary role in analyzing data from pharmacovigilance databases.
Highlights
Post-marketing data on the risks associated with direct oral anticoagulants (DOACs) are conflicting and only few studies evaluated a comparison between each different DOAC
Results from a study based on reports of suspected adverse drug reactions held in VigiBase have shown, as well as premarketing authorization randomized clinical trials (RCTs), a reduced risk of intracranial haemorrhage, but an increased risk of gastrointestinal haemorrhage in patients treated with DOACs compared to warfarin (Monaco et al, 2017)
Another study based on spontaneous reporting data from the Japanese database of Pharmaceuticals and Medical Devices Agency (PMDA) has shown that differences in drug safety aspects may exist between dabigatran and FXa inhibitors, especially in terms of hemorrhagic and ischemic cerebrovascular events (Terayama, 2017)
Summary
Post-marketing data on the risks associated with direct oral anticoagulants (DOACs) are conflicting and only few studies evaluated a comparison between each different DOAC. Results from a study based on reports of suspected adverse drug reactions held in VigiBase have shown, as well as premarketing authorization RCTs, a reduced risk of intracranial haemorrhage, but an increased risk of gastrointestinal haemorrhage in patients treated with DOACs compared to warfarin (Monaco et al, 2017). Another study based on spontaneous reporting data from the Japanese database of Pharmaceuticals and Medical Devices Agency (PMDA) has shown that differences in drug safety aspects may exist between dabigatran and FXa inhibitors (apixaban, edoxaban and rivaroxaban), especially in terms of hemorrhagic and ischemic cerebrovascular events (Terayama, 2017). Its first use in health sciences was proposed by Greenacre: he introduced CA in an initial simple example using data on the relationship between headache types and age; he illustrated a more complex situation when several categorical variables are involved using test data on a collection of bacterial isolates, with the aim of comparing bacterial types and understanding the interrelationships of the different tests (Greenacre, 1992)
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