End-stage heart failure in congenitally corrected transposition of the great arteries:a multicentre study

BACKGROUND AND AIMS: For patients with congenitally corrected transposition of the great arteries (ccTGA), factors associated with progression to end-stage congestive heart failure (CHF) remain largely unclear. METHODS: This multicentre, retrospective cohort study included adults with ccTGA seen at a congenital heart disease centre. Clinical data from initial and most recent visits were obtained. The composite primary outcome was mechanical circulatory support, heart transplantation, or death. RESULTS: From 558 patients (48% female, age at first visit 36 ± 14.2 years, median follow-up 8.7 years), the event rate of the primary outcome was 15.4 per 1000 person-years (11 mechanical circulatory support implantations, 12 transplantations, and 52 deaths). Patients experiencing the primary outcome were older and more likely to have a history of atrial arrhythmia. The primary outcome was highest in those with both moderate/severe right ventricular (RV) dysfunction and tricuspid regurgitation (n = 110, 31 events) and uncommon in those with mild/less RV dysfunction and tricuspid regurgitation (n = 181, 13 events, P &lt; .001). Outcomes were not different based on anatomic complexity and history of tricuspid valve surgery or of subpulmonic obstruction. New CHF admission or ventricular arrhythmia was associated with the primary outcome. Individuals who underwent childhood surgery had more adverse outcomes than age- and sex-matched controls. Multivariable Cox regression analysis identified older age, prior CHF admission, and severe RV dysfunction as independent predictors for the primary outcome. CONCLUSIONS: Patients with ccTGA have variable deterioration to end-stage heart failure or death over time, commonly between their fifth and sixth decades. Predictors include arrhythmic and CHF events and severe RV dysfunction but not anatomy or need for tricuspid valve surgery.</p

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation