URL: http://www.clinicaltrials.gov. Unique identifiers: NCT01358513, NCT02132026, NCT00338676, NCT00647088, NCT01679431.

Obesity is a worldwide health problem with epidemic proportions that has been associated with atrial fibrillation (AF). Even though the underlying pathophysiological mechanisms have not been completely elucidated, several experimental and clinical studies implicate obesity in the initiation and perpetuation of AF. Of note, hypertension, diabetes mellitus, metabolic syndrome, coronary artery disease, and obstructive sleep apnea, represent clinical correlates between obesity and AF. In addition, ventricular adaptation, diastolic dysfunction, and epicardial adipose tissue appear to be implicated in atrial electrical and structural remodeling, thereby promoting the arrhythmia in obese subjects. The present article provides a concise overview of the association between obesity and AF, and highlights the underlying pathophysiological mechanisms.

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Background For two decades, bright-blood late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been considered the reference standard for the non-invasive assessment of myocardial viability. While bright-blood LGE can clearly distinguish areas of myocardial infarction from viable myocardium, it often suffers from poor scar-to-blood contrast, making subendocardial scar difficult to detect. Recently, we proposed a novel dark-blood LGE approach that increases scar-to-blood contrast and thereby improves subendocardial scar conspicuity. In the present study we sought to assess the clinical value of this novel approach in a large patient cohort with various non-congenital ischemic and non-ischemic cardiomyopathies on both 1.5 T and 3 T CMR scanners of different vendors. Methods Three hundred consecutive patients referred for clinical CMR were randomly assigned to a 1.5 T or 3 T scanner. An entire short-axis stack and multiple long-axis views were acquired using conventional phase sensitive inversion recovery (PSIR) LGE with TI set to null myocardium (bright-blood) and proposed PSIR LGE with TI set to null blood (dark-blood), in a randomized order. The bright-blood LGE and dark-blood LGE images were separated, anonymized, and interpreted in a random order at different time points by one of five independent observers. Each case was analyzed for the type of scar, per-segment transmurality, papillary muscle enhancement, overall image quality, observer confidence, and presence of right ventricular scar and intraventricular thrombus. Results Dark-blood LGE detected significantly more cases with ischemic scar compared to conventional bright-blood LGE (97 vs 89, p = 0.008), on both 1.5 T and 3 T, and led to a significantly increased total scar burden (3.3 ± 2.4 vs 3.0 ± 2.3 standard AHA segments, p = 0.015). Overall image quality significantly improved using dark-blood LGE compared to bright-blood LGE (81.3% vs 74.0% of all segments were of highest diagnostic quality, p = 0.006). Furthermore, dark-blood LGE led to significantly higher observer confidence (confident in 84.2% vs 78.4%, p = 0.033). Conclusions The improved detection of ischemic scar makes the proposed dark-blood LGE method a valuable diagnostic tool in the non-invasive assessment of myocardial scar. The applicability in routine clinical practice is further strengthened, as the present approach, in contrast to other recently proposed dark- and black-blood LGE techniques, is readily available without the need for scanner adjustments, extensive optimizations, or additional training.

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BackgroundClinical evaluation of stress perfusion cardiovascular magnetic resonance (CMR) is currently based on visual assessment and has shown high diagnostic accuracy in previous clinical trials, when performed by expert readers or core laboratories. However, these results may not be generalizable to clinical practice, particularly when less experienced readers are concerned. Other factors, such as the level of training, the extent of ischemia, and image quality could affect the diagnostic accuracy. Moreover, the role of rest images has not been clarified.The aim of this study was to assess the diagnostic accuracy of visual assessment for operators with different levels of training and the additional value of rest perfusion imaging, and to compare visual assessment and automated quantitative analysis in the assessment of coronary artery disease (CAD).MethodsWe evaluated 53 patients with known or suspected CAD referred for stress-perfusion CMR. Nine operators (equally divided in 3 levels of competency) blindly reviewed each case twice with a 2-week interval, in a randomised order, with and without rest images. Semi-automated Fermi deconvolution was used for quantitative analysis and estimation of myocardial perfusion reserve as the ratio of stress to rest perfusion estimates.ResultsLevel-3 operators correctly identified significant CAD in 83.6% of the cases. This percentage dropped to 65.7% for Level-2 operators and to 55.7% for Level-1 operators (p < 0.001). Quantitative analysis correctly identified CAD in 86.3% of the cases and was non-inferior to expert readers (p = 0.56). When rest images were available, a significantly higher level of confidence was reported (p = 0.022), but no significant differences in diagnostic accuracy were measured (p = 0.34).ConclusionsOur study demonstrates that the level of training is the main determinant of the diagnostic accuracy in the identification of CAD. Level-3 operators performed at levels comparable with the results from clinical trials. Rest images did not significantly improve diagnostic accuracy, but contributed to higher confidence in the results. Automated quantitative analysis performed similarly to level-3 operators. This is of increasing relevance as recent technical advances in image reconstruction and analysis techniques are likely to permit the clinical translation of robust and fully automated quantitative analysis into routine clinical practice.

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