Multicenter evaluation of left ventricular assist device implantation with or without ECMO bridge in cardiogenic shock.

BACKGROUND: The efficacy of extracorporeal membrane oxygenation (ECMO) as a bridge to left ventricular assist device (LVAD) remains unclear, and recipients of the more contemporary HeartMate 3 (HM3) LVAD are not well represented in previous studies. We therefore undertook a multicenter, retrospective study of this population. METHODS AND RESULTS: INTERMACS 1 LVAD recipients from five U.S. centers were included. In-hospital and one-year outcomes were recorded. The primary outcome was the overall mortality hazard comparing ECMO versus non-ECMO patients by propensity-weighted survival analysis. Secondary outcomes included survival by LVAD type, as well as postoperative and one-year outcomes. One hundred and twenty-seven patients were included; 24 received ECMO as a bridge to LVAD. Mortality was higher in patients bridged with ECMO in the primary analysis (HR 3.22 [95%CI 1.06-9.77], p = 0.039). Right ventricular assist device was more common in the ECMO group (ECMO: 54.2% vs non-ECMO: 11.7%, p < 0.001). Ischemic stroke was higher at one year in the ECMO group (ECMO: 25.0% vs non-ECMO: 4.9%, p = 0.006). Among the study cohort, one-year mortality was lower in HM3 than in HeartMate II (HMII) or HeartWare HVAD (10.5% vs 46.9% vs 31.6%, respectively; p < 0.001) recipients. Pump thrombosis at one year was lower in HM3 than in HMII or HVAD (1.8% vs 16.1% vs 16.2%, respectively; p = 0.026) recipients. CONCLUSIONS: Higher mortality was observed with ECMO as a bridge to LVAD, likely due to higher acuity illness, yet acceptable one-year survival was seen compared with historical rates. The receipt of the HM3 was associated with improved survival compared with older generation devices.

Quantification of Epicardial Adipose Tissue Volume and Attenuation for Cardiac CT Scans Using Deep Learning in a Single Multi-Task Framework.

Background: Recent studies have shown that epicardial adipose tissue (EAT) is an independent atrial fibrillation (AF) prognostic marker and has influence on the myocardial function. In computed tomography (CT), EAT volume (EATv) and density (EATd) are parameters that are often used to quantify EAT. While increased EATv has been found to correlate with the prevalence and the recurrence of AF after ablation therapy, higher EATd correlates with inflammation due to arrest of lipid maturation and with high risk of plaque presence and plaque progression. Automation of the quantification task diminishes the variability in readings introduced by different observers in manual quantification and results in high reproducibility of studies and less time-consuming analysis. Our objective is to develop a fully automated quantification of EATv and EATd using a deep learning (DL) framework. Methods: We proposed a framework that consists of image classification and segmentation DL models and performs the task of selecting images with EAT from all the CT images acquired for a patient, and the task of segmenting the EAT from the output images of the preceding task. EATv and EATd are estimated using the segmentation masks to define the region of interest. For our experiments, a 300-patient dataset was divided into two subsets, each consisting of 150 patients: Dataset 1 (41,979 CT slices) for training the DL models, and Dataset 2 (36,428 CT slices) for evaluating the quantification of EATv and EATd. Results: The classification model achieved accuracies of 98% for precision, recall and F 1 scores, and the segmentation model achieved accuracies in terms of mean ( ± std.) and median dice similarity coefficient scores of 0.844 ( ± 0.19) and 0.84, respectively. Using the evaluation set (Dataset 2), our approach resulted in a Pearson correlation coefficient of 0.971 ( R 2 = 0.943) between the label and predicted EATv, and the correlation coefficient of 0.972 ( R 2 = 0.945) between the label and predicted EATd. Conclusions: We proposed a framework that provides a fast and robust strategy for accurate EAT segmentation, and volume (EATv) and attenuation (EATd) quantification tasks. The framework will be useful to clinicians and other practitioners for carrying out reproducible EAT quantification at patient level or for large cohorts and high-throughput projects.

Repeat catheter ablation for atrial fibrillation.

Catheter ablation of atrial fibrillation (AF) is an established therapy that reduces AF burden, improves quality of life, and reduces the risks of cardiovascular outcomes. Although there are clear guidelines for the application of de novo catheter ablation, there is less evidence to guide recommendations for repeat catheter ablation in patients who experience recurrent AF. In this review, we examine the rationale for repeat ablation, mechanisms of recurrence, patient selection, optimal timing, and procedural strategies. We discuss additional important considerations, including treatment of comorbidities and risk factors, risk of complications, and effectiveness. Mechanisms of recurrent AF are often due to non-pulmonary vein (non-PV) triggers; however, there is insufficient evidence supporting the routine use of empiric lesion sets during repeat ablation. The emergence of pulsed field ablation may alter the safety and effectiveness of de novo and repeat ablation. Extrapolation of data from randomized trials of de novo ablation does not optimally inform efficacy in cases of redo ablation. Additional large, randomized controlled trials are needed to address important clinical questions regarding procedural strategies and timing of repeat ablation.

Machine Learning of Cardiac Anatomy and the Risk of New-Onset Atrial Fibrillation After TAVR.

BACKGROUND: New-onset atrial fibrillation (NOAF) occurs in 5% to 15% of patients who undergo transfemoral transcatheter aortic valve replacement (TAVR). Cardiac imaging has been underutilized to predict NOAF following TAVR. OBJECTIVES: The objective of this analysis was to compare and assess standard, manual echocardiographic and cardiac computed tomography (cCT) measurements as well as machine learning-derived cCT measurements of left atrial volume index and epicardial adipose tissue as risk factors for NOAF following TAVR. METHODS: The study included 1,385 patients undergoing elective, transfemoral TAVR for severe, symptomatic aortic stenosis. Each patient had standard and machine learning-derived measurements of left atrial volume and epicardial adipose tissue from cardiac computed tomography. The outcome of interest was NOAF within 30 days following TAVR. We used a 2-step statistical model including random forest for variable importance ranking, followed by multivariable logistic regression for predictors of highest importance. Model discrimination was assessed by using the C-statistic to compare the performance of the models with and without imaging. RESULTS: Forty-seven (5.0%) of 935 patients without pre-existing atrial fibrillation (AF) experienced NOAF. Patients with pre-existing AF had the largest left atrial volume index at 76.3 ± 28.6 cm3/m2 followed by NOAF at 68.1 ± 26.6 cm3/m2 and then no AF at 57.0 ± 21.7 cm3/m2 (P < 0.001). Multivariable regression identified the following risk factors in association with NOAF: left atrial volume index ≥76 cm2 (OR: 2.538 [95% CI: 1.165-5.531]; P = 0.0191), body mass index <22 kg/m2 (OR: 4.064 [95% CI: 1.500-11.008]; P = 0.0058), EATv (OR: 1.007 [95% CI: 1.000-1.014]; P = 0.043), aortic annulus area ≥659 mm2 (OR: 6.621 [95% CI: 1.849-23.708]; P = 0.004), and sinotubular junction diameter ≥35 mm (OR: 3.891 [95% CI: 1.040-14.552]; P = 0.0435). The C-statistic of the model was 0.737, compared with 0.646 in a model that excluded imaging variables. CONCLUSIONS: Underlying cardiac structural differences derived from cardiac imaging may be useful in predicting NOAF following transfemoral TAVR, independent of other clinical risk factors.