The most commonly prescribed oral anticoagulants worldwide are the vitamin K antagonists (VKAs) such as warfarin. Factors affecting the pharmacokinetics of VKAs are important because deviations from their narrow therapeutic window can result in bleedings due to over-anticoagulation or thrombosis because of under-anticoagulation. In addition to pharmacodynamic interactions (e.g., augmented bleeding risk for concomitant use of NSAIDs), interactions with drugs, foods, herbs, and over-the-counter medications may affect the risk/benefit ratio of VKAs. Direct oral anticoagulants (DOACs) including Factor Xa inhibitors (rivaroxaban, apixaban and edoxaban) and thrombin inhibitor (dabigatran) are poised to replace warfarin. Phase-3 studies and real-world evaluations have established that the safety profile of DOACs is superior to those of VKAs. However, some pharmacokinetic and pharmacodynamic interactions are expected. Herein we present a critical review of VKAs and DOACs with focus on their potential for interactions with drugs, foods, herbs and over-the-counter medications.

Background: The lack of specific vaccines or drugs against coronavirus disease 2019 (COVID-19) warrants studies focusing on alternative clinical approaches to reduce the spread of this pandemic disease. In this study, we investigated whether anti-influenza vaccination plays a role in minimizing the diffusion of COVID-19 in the Italian population aged 65 and over. Methods: Four COVID-19 outcomes were used: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence, hospitalizations for COVID-19 symptoms, admissions to intensive care units for reasons related to SARS-CoV-2, and deaths attributable to COVID-19. Results: At univariate analyses, the influenza vaccination coverage rates correlated negatively with all COVID-19 outcomes (Beta ranging from −134 to −0.61; all p < 0.01). At multivariable analyses, influenza vaccination coverage rates correlated independently with SARS-CoV-2 seroprevalence (Beta (95% C.I.): −130 (−198, −62); p = 0.001), hospitalizations for COVID-19 symptoms (Beta (95% C.I.): −4.16 (−6.27, −2.05); p = 0.001), admission to intensive care units for reasons related to SARS-CoV-2 (Beta (95% C.I.): −0.58 (−1.05, −0.12); p = 0.017), and number of deaths attributable to COVID-19 (Beta (95% C.I.): −3.29 (−5.66, −0.93); p = 0.010). The R2 observed in the unadjusted analysis increased from 82% to 159% for all the considered outcomes after multivariable analyses. Conclusions: In the Italian population, the coverage rate of the influenza vaccination in people aged 65 and over is associated with a reduced spread and a less severe clinical expression of COVID-19. This finding warrants ad hoc studies to investigate the role of influenza vaccination in preventing the spread of COVID-19.

Carotid atherosclerosis correlates better with coronary atherosclerosis when both circulations are investigated by the same technique (ultrasound) using the same parameter (IMT). This supports the concept that carotid IMT is a good surrogate marker of coronary atherosclerosis.

Background and aimsCarotid plaque size and the mean common carotid intima-media thickness measured in plaque-free areas (PF CC-IMTmean) have been identified as predictors of vascular events (VEs), but their complementarity in risk prediction and stratification is still unresolved. The aim of this study was to evaluate the independence of carotid plaque thickness and PF CC-IMTmean in cardiovascular risk prediction and risk stratification.MethodsThe IMPROVE-study is a European cohort (n = 3703), where the thickness of the largest plaque detected in the whole carotid tree was indexed as cIMTmax. PF CC-IMTmean was also assessed. Hazard Ratios (HR) comparing the top quartiles of cIMTmax and PF CC-IMTmeanversus their respective 1–3 quartiles were calculated using Cox regression.ResultsAfter a 36.2-month follow-up, there were 215 VEs (125 coronary, 73 cerebral and 17 peripheral). Both cIMTmax and PF CC-IMTmean were mutually independent predictors of combined-VEs, after adjustment for center, age, sex, risk factors and pharmacological treatment [HR (95% CI) = 1.98 (1.47, 2.67) and 1.68 (1.23, 2.29), respectively]. Both variables were independent predictors of cerebrovascular events (ischemic stroke, transient ischemic attack), while only cIMTmax was an independent predictor of coronary events (myocardial infarction, sudden cardiac death, angina pectoris, angioplasty, coronary bypass grafting). In reclassification analyses, PF CC-IMTmean significantly adds to a model including both Framingham Risk Factors and cIMTmax (Integrated Discrimination Improvement; IDI = 0.009; p = 0.0001) and vice-versa (IDI = 0.02; p < 0.0001).ConclusionscIMTmax and PF CC-IMTmean are independent predictors of VEs, and as such, they should be used as additive rather than alternative variables in models for cardiovascular risk prediction and reclassification.