Country and health expenditure are major predictors of withholding anticoagulation in atrial fibrillation patients at high risk of stroke.

BACKGROUND: Guidelines for patients with atrial fibrillation (AF) at high thromboembolic risk recommend oral anticoagulants (OACs) for preventing stroke and systemic embolism (SE). The reasons for guideline non-adherence are still unclear. AIM: The aim is to identify clinical, demographic and non-patient characteristics associated with withholding OAC in patients with AF at high stroke risk. METHODS: Patients in the Global Anticoagulant Registry in the FIELD-AF, newly diagnosed with AF between March 2010 and August 2016, and with CHA2DS2-VASc Score≥2 (excluding sex), were grouped by OAC treatment at enrolment. Factors associated with OAC non-use were analysed by multivariable logistic regression. RESULTS: Of 40 416 eligible patients, 12 126 (30.0%) did not receive OACs at baseline. Globally, OAC prescription increased over time, from 60.4% in 2010-2011 to 74.7% in 2015-2016. Country of enrolment was the major predictor for OAC withholding (χ2-df=2576). Clinical predictors of OAC non-use included type of AF (χ2-df=404), history of bleeding (χ2-df=263) and vascular disease (χ2-df=99). OACs were used most frequently around the age of 75 years and decreasingly with younger as well as older age beyond 75 years (χ2-df=148). Non-cardiologists (χ2-df=201) and emergency room physicians (χ2-df=14) were less likely to prescribe OACs. OAC prescription correlated positively with country health expenditure. CONCLUSIONS: Approximately one out of three AF patients did not receive OAC, while eligible according to the guidelines. Country of enrolment was the major determinant of anticoagulation strategy, while higher country health expenditure was associated with lower likelihood of withholding anticoagulation.

Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England.

OBJECTIVE: The PULsE-AI trial sought to determine the effectiveness of a screening strategy that included a machine learning risk prediction algorithm in conjunction with diagnostic testing for identification of undiagnosed atrial fibrillation (AF) in primary care. This study aimed to evaluate the cost-effectiveness of implementing the screening strategy in a real-world setting. METHODS: Data from the PULsE-AI trial - a prospective, randomized, controlled trial conducted across six general practices in England from June 2019 to February 2021 - were used to inform a cost-effectiveness analysis that included a hybrid screening decision tree and Markov AF disease progression model. Model outcomes were reported at both individual- and population-level (estimated UK population ≥30 years of age at high-risk of undiagnosed AF) and included number of patients screened, number of AF cases identified, mean total and incremental costs (screening, events, treatment), quality-adjusted-life-years (QALYs), and incremental cost-effectiveness ratio (ICER). RESULTS: The screening strategy was estimated to result in 45,493 new diagnoses of AF across the high-risk population in the UK (3.3 million), and an estimated additional 14,004 lifetime diagnoses compared with routine care only. Per-patient costs for high-risk individuals who underwent the screening strategy were estimated at £1,985 (vs £1,888 for individuals receiving routine care only). At a population-level, the screening strategy was associated with a cost increase of approximately £322 million and an increase of 81,000 QALYs. The screening strategy demonstrated cost-effectiveness versus routine care only at an accepted ICER threshold of £20,000 per QALY-gained, with an ICER of £3,994/QALY. CONCLUSIONS: Compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF, through an AF risk prediction algorithm, who should then undergo diagnostic testing. This AF risk prediction algorithm can reduce the number of patients needed to be screened to identify undiagnosed AF, thus alleviating primary care burden.

Epidemiology, Pathophysiology, and Management of Native Atrioventricular Valve Regurgitation in Heart Failure Patients.

Atrioventricular regurgitation is frequent in the setting of heart failure. It is due to atrial and ventricular remodelling, as well as rhythmic disturbances and loss of synchrony. Once atrioventricular regurgitation develops, it can aggravate the underlying heart failure, and further participate and aggravate its own severity. Its presence is therefore concomitantly a surrogate of advance disease and a predictor of mortality. Heart failure management, including medical therapy, cardiac resynchronization therapy, and restoration of sinus rhythm, are the initial steps to reduce atrioventricular regurgitation. In the current review, we analyse the current data assessing the epidemiology, pathophysiology, and impact of non-valvular intervention on atrioventricular regurgitation including medical treatment, cardiac resynchronization and atrial fibrillation ablation.

Ticagrelor reversal: in vitro assessment of four haemostatic agents.

AIM: Management of ticagrelor-induced bleeding is challenging as platelet transfusion is ineffective. An effective strategy is needed. This study aimed to investigate in vitro the efficacy of four haemostatic drugs (HDs), namely recombinant activated factor VII (rFVIIa), fibrinogen concentrate (Fib), tranexamic acid (TXA) and factor XIII concentrate (FXIII) to improve the haemostatic capacity in the presence of ticagrelor. METHODS: Blood was spiked with ticagrelor then supplemented by either HD or control. Several assays were performed: ADP-induced platelet aggregation measured by impedance aggregometry, light transmission and two global assays, thrombolastography with the platelet mapping device (TEG-PM) and a platelet-dependent thrombin generation assay (TGA). RESULTS: Ticagrelor inhibited ADP-induced platelet aggregation and decreased the clot strength maximum amplitude (MA) in TEG-PMADP. None of the HDs corrected these parameters. However, rFVIIa shortened the coagulation time R using TEG-PMthrombin and the time to peak prolonged by ticagrelor in TGA. Fib increased MAthrombin and FXIII decreased LY30. TXA had no effects. CONCLUSIONS: Whereas none of the HDs corrected ticagrelor-induced platelet inhibition, rFVIIa shortened coagulation times, Fib increased clot firmness and FXIII decreased fibrinolysis. Consequently, they may bypass ticagrelor effects by acting on fibrin formation or fibrinolysis. Further studies are needed to confirm these data in vivo.