Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with an increased risk of ischemic stroke (IS) and systemic embolism (SE). Stroke prevention is a key element for the overall management of AF patients. The non-vitamin K antagonist oral anticoagulants (NOACs), such as dabigatran, rivaroxaban, apixaban and edoxaban, are at least as effective as warfarin in reducing IS/SE with a lower rate of major bleeding. Various analyses from the large Phase III randomized trials demonstrated different efficacy and safety of NOACs in specific subgroups of patients. The randomized trials are supplemented by effectiveness and safety data from real-world observational cohorts following the availability of these drugs for use in everyday clinical practice. Given the clinical heterogeneity of AF patients, the available data from trials and real-world studies allow us to fit the right NOAC to the particular patient's characteristics, with the aim of optimizing outcomes for the individual patient. This review article aims to provide a summary of the evidence on the performance of NOACs in AF patients with specific clinical characteristics. Evidence-based suggestions are presented to provide a simple and viable strategy for clinicians for the choice of a particular NOAC. KEY MESSAGE Given the different performance of the new-oral anticoagulants in patients with the different clinical situation, evidence-based choice of fitting the right new-oral anticoagulants to the patients is provided in this review article.
The use of risk markers has transformed cardiovascular medicine, exemplified by the routine assessment of troponin, for both diagnosis and assessment of prognosis in patients with chest pain. Clinical risk factors form the basis for risk assessment of cardiovascular disease and the addition of biochemical, cellular, and imaging parameters offers further refinement. Identifying novel risk factors may allow greater risk stratification and a steady, but gradual progression toward precision medicine. Indeed, the generation of data in this area of research is explosive and when combined with new technologies and techniques provides the potential for more refined, targeted approaches to cardiovascular medicine. Although discussing the most recent developments in this field, this review article aims to strike a balance between novelty and validity by focusing on recent large sample-size studies that have been validated in a separate cohort in most cases. Risk markers related to atherosclerosis, thrombosis, inflammation, cardiac injury, and fibrosis are introduced in the context of their pathophysiology. Rapidly developing new areas, such as assessment of micro-RNA, are also explored. Subsequently the prognostic ability of these risk markers in coronary artery disease, heart failure, and atrial fibrillation is discussed in detail.
Background We aim to determine which electrocardiogram (ECG) data format is optimal for ML modelling, in the context of myocardial infarction prediction. We will also address the auxiliary objective of evaluating the viability of using digitised ECG signals for ML modelling. Methods Two ECG arrangements displaying 10s and 2.5 s of data for each lead were used. For each arrangement, conservative and speculative data cohorts were generated from the PTB-XL dataset. All ECGs were represented in three different data formats: Signal ECGs, Image ECGs, and Extracted Signal ECGs, with 8358 and 11,621 ECGs in the conservative and speculative cohorts, respectively. ML models were trained using the three data formats in both data cohorts. Results For ECGs that contained 10s of data, Signal and Extracted Signal ECGs were optimal and statistically similar, with AUCs [95% CI] of 0.971 [0.961, 0.981] and 0.974 [0.965, 0.984], respectively, for the conservative cohort; and 0.931 [0.918, 0.945] and 0.919 [0.903, 0.934], respectively, for the speculative cohort. For ECGs that contained 2.5 s of data, the Image ECG format was optimal, with AUCs of 0.960 [0.948, 0.973] and 0.903 [0.886, 0.920], for the conservative and speculative cohorts, respectively. Conclusion When available, the Signal ECG data should be preferred for ML modelling. If not, the optimal format depends on the data arrangement within the ECG: If the Image ECG contains 10s of data for each lead, the Extracted Signal ECG is optimal, however, if it only uses 2.5 s, then using the Image ECG data is optimal for ML performance.
Patients with severely reduced renal function have been excluded from randomized controlled trials of oral anticoagulation in atrial fibrillation (AF). Warfarin treatment in this population is controversial and data on anticoagulation control and the impact on adverse outcomes are needed. By individual-level linkage of nationwide registries, we identified all patients discharged from hospitals with AF in Denmark between 1997 and 2011. Patients with available serum creatinine tests were categorized according to the estimated glomerular filtration rate (eGFR). Time in therapeutic range (TTR) was calculated using the Rosendaal method. The risk of stroke and bleeding was estimated using multivariable Cox regression analyses with eGFR and TTR estimated time dependently throughout follow-up. We identified 10,423 warfarin-treated AF patients with available international normalized ratio and creatinine tests; 5,527 with eGFR > 60 mL/min/1.73 m2, 4,524 with eGFR 30–60 mL/min/1.73 m2 and 372 with eGFR < 30 mL/min/1.73 m2. Median TTR was 66.7, 61.2 and 49.7% in patients with eGFR > 60, 30–59 and <30 mL/min/1.73 m2, respectively. A TTR < 70% was associated with a higher risk of stroke/thromboembolism (hazard ratio [HR]: 1.39; 95% confidence interval [CI]: 1.20–1.60) and bleeding (HR: 1.22; 95% CI: 1.05–1.42) among patients with eGFR of 30 to 59 and a trend towards higher risk of stroke/thromboembolism (HR: 1.24; 95% CI: 0.86–1.80) and bleeding (HR: 1.17; 95% CI: 0.83–1.65) among patients with eGFR < 30 mL/min/1.73 m2. In conclusion, warfarin-treated AF patients with reduced renal function have suboptimal anticoagulation control which was related to the risk of adverse outcomes.
In this Editor's choice, we highlight last year's papers from Thrombosis and Haemostasis as well as from its open access companion journal THOpen, which found most resonance among our readers' community, holding promise for mechanistic understanding and improved clinical management in the fields of Thrombosis and Haemostasis. Building on the research investigations from this undoubtedly memorable year may be particularly useful to overcome the current pandemic situation. As it became apparent that coronavirus disease 2019 (COVID-19) was not merely a pulmonary disease but that thrombosis was a key element involved in its severity and complications, the thrombosis research community deployed intensive research efforts in the hope to optimize treatment and/or prophylaxis as well as to decipher the mechanisms and characteristics of COVID-19 thrombosis as rapidly as possible. Due to the urgency of the pandemic development, it was indeed not surprising that publications relating to COVID-19 received by far the most attention in 2020.
