New-onset atrial fibrillation in patients with worsening heart failure and coronary artery disease:an analysis from the COMMANDER-HF trial

Background: Atrial fibrillation (AF) in the presence of heart failure (HF) is associated with poor outcomes including a high-risk of stroke and other thromboembolic events. Identifying patients without AF who are at high-risk of developing this arrhythmia has important clinical implications. Aims: To develop a risk score to identify HF patients at high risk of developing AF. Methods: The COMMANDER-HF trial enrolled 5022 patients with HF and a LVEF ≤ 40%, history of coronary artery disease, and absence of AF at baseline (confirmed with an electrocardiogram). Patients were randomized to either rivaroxaban (2.5 mg bid) or placebo. New-onset AF was confirmed by the investigator at study visits. Results 241 (4.8%) patients developed AF during the follow-up (median 21 months). Older age (≥ 65 years), LVEF < 35%, history of PCI or CABG, White race, SBP < 110 mmHg, and higher BMI (≥ 25 kg/m2) were independently associated with risk of new-onset AF, whereas the use of DAPT was associated with a lower risk of new-onset AF. We then built a risk score from these variables (with good accuracy C-index = 0.71) and calibration across observed and predicted tertiles of risk. New-onset AF events rates increased steeply by increasing tertiles of the risk-score. Compared to tertile 1, the risk of new-onset AF was 2.5-fold higher in tertile 2, and 6.3-fold higher in tertile 3. Rivaroxaban had no effect in reducing new-onset AF. In time-updated models, new-onset AF was associated with a higher risk of subsequent all-cause death: HR (95%CI) 1.38 (1.11–1.73). Conclusions: A well-calibrated risk-score identified patients at risk of new-onset AF in the COMMANDER-HF trial. Patients who developed AF had a higher risk of subsequent death

Rivaroxaban in patients with heart failure, sinus rhythm, and coronary disease

Background: Heart failure is associated with activation of thrombin-related pathways, which predicts a poor prognosis. We hypothesized that treatment with rivaroxaban, a factor Xa inhibitor, could reduce thrombin generation and improve outcomes for patients with worsening chronic heart failure and underlying coronary artery disease. Methods: In this double-blind, randomized trial, 5022 patients who had chronic heart failure, a left ventricular ejection fraction of 40% or less, coronary artery disease, and elevated plasma concentrations of natriuretic peptides and who did not have atrial fibrillation were randomly assigned to receive rivaroxaban at a dose of 2.5 mg twice daily or placebo in addition to standard care after treatment for an episode of worsening heart failure. The primary efficacy outcome was the composite of death from any cause, myocardial infarction, or stroke. The principal safety outcome was fatal bleeding or bleeding into a critical space with a potential for causing permanent disability. Results: Over a median follow-up period of 21.1 months, the primary end point occurred in 626 (25.0%) of 2507 patients assigned to rivaroxaban and in 658 (26.2%) of 2515 patients assigned to placebo (hazard ratio, 0.94; 95% confidence interval [CI], 0.84 to 1.05; P=0.27). No significant difference in all-cause mortality was noted between the rivaroxaban group and the placebo group (21.8% and 22.1%, respectively; hazard ratio, 0.98; 95% CI, 0.87 to 1.10). The principal safety outcome occurred in 18 patients who took rivaroxaban and in 23 who took placebo (hazard ratio, 0.80; 95% CI, 0.43 to 1.49; P=0.48). Conclusions: Rivaroxaban at a dose of 2.5 mg twice daily was not associated with a significantly lower rate of death, myocardial infarction, or stroke than placebo among patients with worsening chronic heart failure, reduced left ventricular ejection fraction, coronary artery disease, and no atrial fibrillation. (Funded by Janssen Research and Development; COMMANDER HF ClinicalTrials.gov number, NCT01877915.

Liver Fat Assessment in Multiview Sonography Using Transfer Learning With Convolutional Neural Networks

ObjectivesTo develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/kidney).MethodsUS images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth. Transfer learning with a deep convolutional neural network (CNN) was applied to develop models for diagnosis of fatty liver (PDFF ≥ 5%), diagnosis of advanced steatosis (PDFF ≥ 10%), and PDFF quantification for each liver view separately. In addition, an ensemble model based on all four liver view models was investigated. Diagnostic performance was assessed using the area under the receiver operating characteristics curve (AUC), and quantification was assessed using the Spearman correlation coefficient (SCC).ResultsThe most accurate single view was the right posterior portal vein, with an SCC of 0.78 for quantifying PDFF and AUC values of 0.90 (PDFF ≥ 5%) and 0.79 (PDFF ≥ 10%). The ensemble of models achieved an SCC of 0.81 and AUCs of 0.91 (PDFF ≥ 5%) and 0.86 (PDFF ≥ 10%).ConclusionDeep learning-based analysis of US images from different liver views can help assess liver fat