actice, EROA is the strongest independent FMR determinant of survival after diagnosis. Excess mortality increases exponentially above the threshold of 0.10cm
  , with a much steeper slope than in DMR, for any EROA increment. An expanded EROA-based stratification, superior to existing grading schemes in determining survival, should allow guideline harmonization.
 In HFrEF, FMR is skewed towards smaller EROA. Nevertheless, when measured in routine practice, EROA is the strongest independent FMR determinant of survival after diagnosis. Excess mortality increases exponentially above the threshold of 0.10 cm2, with a much steeper slope than in DMR, for any EROA increment. An expanded EROA-based stratification, superior to existing grading schemes in determining survival, should allow guideline harmonization.
  This study aimed to investigate mitral annular dynamics in atrial fibrillation (AF) and after sinus rhythm restoration, and to assess the relationship between annular dynamics and mitral regurgitation (MR).
 
  AF can be associated with MR that improves after sinus rhythm restoration. Mechanisms underlying this atrial functional MR (AFMR) are ill-understood and generally attributed to left atrial remodeling.
 
  Fifty-three patients with persistent AF and normal left ventricular ejection fraction were prospectively examined by means of 3-dimensional transesophageal echocardiography before, immediately after, and 6 weeks after electric cardioversion to sinus rhythm. Annular motion was assessed during AF and in sinus rhythm with the use of 3-dimensional analysis software, and the relationship with MR severity was explored.
 
  During AF and immediately after sinus rhythm restoration, the mitral annulus behaved relatively adynamically, with an overall change in annular area of 10.3% (95%CI 8.7%-11.8%) and 12.2% (95%tolic narrowing that contributes to AFMR.  https://www.selleckchem.com/products/trastuzumab-deruxtecan.html  Sinus rhythm restoration allows gradual recovery of presystolic annular dynamics. Improved annular dynamics decrease AFMR severity by optimizing annular-leaflet imbalance, regardless of LA remodeling.
 Mitral annular dynamics are impaired in AF, with blunted presystolic narrowing that contributes to AFMR. Sinus rhythm restoration allows gradual recovery of presystolic annular dynamics. Improved annular dynamics decrease AFMR severity by optimizing annular-leaflet imbalance, regardless of LA remodeling.
  The aim of this meta-analysis was to assess the diagnostic performance of various CMR imaging parameters for evaluating acute cardiac transplant rejection.
 
  Endomyocardial biopsy is the current gold standard for detection of acute cardiac transplant rejection. Cardiac magnetic resonance (CMR) is uniquely capable of myocardial tissue characterization and may be useful as a noninvasive alternative for the diagnosis of graft rejection.
 
  PubMed and Web of Science were searched for relevant publications reporting on the use of CMR myocardial tissue characterization for detection of acute cardiac transplant rejection with endomyocardial biopsy as the reference standard. Pooled sensitivity, specificity, and hierarchical modeling-based summary receiver-operating characteristic curves were calculated.
 
  Of 478 papers, 10 studies comprising 564 patients were included. The sensitivity and specificity for the detection of acute cardiac transplant rejection were 84.6 (95%CI 65.6-94.0) and 70.1 (95%CI 54.2-82.2) for T1tic use but lower diagnostic accuracy compared with T2, which was related primarily to lower specificity. LGE showed poor diagnostic performance for detection of rejection.
  This study aims to investigate the prognostic significance of late gadolinium enhancement (LGE) in patients without coronary artery disease and with normal range left ventricular (LV) volumes and ejection fraction.
 
  Nonischemic patterns of LGE with normal LV volumes and ejection fraction are increasingly detected on cardiovascular magnetic resonance, but their prognostic significance, and consequently management, is uncertain.
 
  Patients with midwall/subepicardial LGE and normal LV volumes, wall thickness, and ejection fraction on cardiovascular magnetic resonance were enrolled and compared to a control group without LGE. The primary outcome was actual or aborted sudden cardiac death (SCD).
 
  Of 748 patients enrolled, 401 had LGE and 347 did not. The median age was 50years (interquartile range 38 years-61 years), LV ejection fraction 66% (interquartile range 62%-70%), and 287 (38%) were women. Scan indications included chest pain (40%), palpitation (33%) and breathlessness (13%). No patient experienced SCDalthough the latter was associated with greater cardiovascular hospitalization for suspected myocarditis and symptomatic ventricular tachycardia.
  This study sought to develop and evaluate a novel, general purpose, explainable deep learning model (coronary artery disease-deep learning [CAD-DL]) for the detection of obstructive CAD following single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI).
 
  Explainable artificial intelligence (AI) can be integrated within standard clinical software to facilitate the acceptance of the diagnostic findings during clinical interpretation.
 
  A total of 3,578 patients with suspected CAD undergoing SPECT MPI and invasive coronary angiography within a 6 month interval from 9 centers were studied. CAD-DL computes the probability of obstructive CAD from stress myocardial perfusion, wall motion, and wall thickening maps, as well as left ventricular volumes, age, and sex. Myocardial regions contributing to the CAD-DL prediction are highlighted to explain the findings to the physician. A clinical prototype was integrated using a standard clinical workstation. Diagnostic performance by CAD-DL wl intelligence can be integrated within standard clinical software to facilitate acceptance of artificial intelligence diagnosis of CAD following MPI.
 The deep-learning model significantly surpasses the diagnostic accuracy of standard quantitative analysis and clinical visual reading for MPI. Explainable artificial intelligence can be integrated within standard clinical software to facilitate acceptance of artificial intelligence diagnosis of CAD following MPI.