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.

Quantitative Ultrasound and B-Mode Image Texture Features Correlate with Collagen and Myelin Content in Human Ulnar Nerve Fascicles.

We investigate the usefulness of quantitative ultrasound and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30-MHz probe. Next, the nerves were extracted to prepare histology sections. Eighty-five fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin and ultrasound data to calculate the backscatter coefficient (-24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 db/cm-MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83), as well as B-mode texture features obtained via the gray-level co-occurrence matrix algorithm. Significant Spearman rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R = -0.68), entropy (R = -0.51) and several texture features. Our study indicates that quantitative ultrasound may potentially provide information on structural components of nerve fascicles

Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radiofrequency Ultrasound Data Using One-dimensional Convolutional Neural Networks.

Background Radiofrequency ultrasound data from the liver contain rich information about liver microstructure and composition. Deep learning might exploit such information to assess nonalcoholic fatty liver disease (NAFLD). Purpose To develop and evaluate deep learning algorithms that use radiofrequency data for NAFLD assessment, with MRI-derived proton density fat fraction (PDFF) as the reference. Materials and Methods A HIPAA-compliant secondary analysis of a single-center prospective study was performed for adult participants with NAFLD and control participants without liver disease. Participants in the parent study were recruited between February 2012 and March 2014 and underwent same-day US and MRI of the liver. Participants were randomly divided into an equal number of training and test groups. The training group was used to develop two algorithms via cross-validation: a classifier to diagnose NAFLD (MRI PDFF ≥ 5%) and a fat fraction estimator to predict MRI PDFF. Both algorithms used one-dimensional convolutional neural networks. The test group was used to evaluate the classifier for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, and linearity between predicted fat fraction and MRI PDFF. Results A total of 204 participants were analyzed, 140 had NAFLD (mean age, 52 years ± 14 [standard deviation]; 82 women) and 64 were control participants (mean age, 46 years ± 21; 42 women). In the test group, the classifier provided 96% (95% confidence interval [CI]: 90%, 99%) (98 of 102) accuracy for NAFLD diagnosis (sensitivity, 97% [95% CI: 90%, 100%], 68 of 70; specificity, 94% [95% CI: 79%, 99%], 30 of 32; positive predictive value, 97% [95% CI: 90%, 99%], 68 of 70; negative predictive value, 94% [95% CI: 79%, 98%], 30 of 32). The estimator-predicted fat fraction correlated with MRI PDFF (Pearson r = 0.85). The mean bias was 0.8% (P = .08), and 95% limits of agreement were -7.6% to 9.1%. The predicted fat fraction was linear with an MRI PDFF of 18% or less (r = 0.89, slope = 1.1, intercept = 1.3) and nonlinear with an MRI PDFF greater than 18%. Conclusion Deep learning algorithms using radiofrequency ultrasound data are accurate for diagnosis of nonalcoholic fatty liver disease and hepatic fat fraction quantification when other causes of steatosis are excluded. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Lockhart and Smith in this issue