Impact of Right Atrial Physiology on Heart Failure and Adverse Events after Myocardial Infarction

Background: Right ventricular (RV) function is a known predictor of adverse events in heart failure and following acute myocardial infarction (AMI). While right atrial (RA) involvement is well characterized in pulmonary arterial hypertension, its relative contributions to adverse events following AMI especially in patients with heart failure and congestion need further evaluation. Methods: In this cardiovascular magnetic resonance (CMR)-substudy of AIDA STEMI and TATORT NSTEMI, 1235 AMI patients underwent CMR after primary percutaneous coronary intervention (PCI) in 15 centers across Germany (n = 795 with ST-elevation myocardial infarction and 440 with non-ST-elevation MI). Right atrial (RA) performance was evaluated using CMR myocardial feature tracking (CMR-FT) for the assessment of RA reservoir (total strain εs), conduit (passive strain εe), booster pump function (active strain εa), and associated strain rates (SR) in a blinded core-laboratory. The primary endpoint was the occurrence of major adverse cardiac events (MACE) 12 months post AMI. Results: RA reservoir (εs p = 0.061, SRs p = 0.049) and conduit functions (εe p = 0.006, SRe p = 0.030) were impaired in patients with MACE as opposed to RA booster pump (εa p = 0.579, SRa p = 0.118) and RA volume index (p = 0.866). RA conduit function was associated with the clinical onset of heart failure and MACE independently of RV systolic function and atrial fibrillation (AF) (multivariable analysis hazard ratio 0.95, 95% confidence interval 0.92 to 0.99, p = 0.009), while RV systolic function and AF were not independent prognosticators. Furthermore, RA conduit strain identified low- and high-risk groups within patients with reduced RV systolic function (p = 0.019 on log rank testing). Conclusions: RA impairment is a distinct feature and independent risk factor in patients following AMI and can be easily assessed using CMR-FT-derived quantification of RA strain

Cardiovascular Magnetic Resonance Imaging Feature Tracking: Impact of Training on Observer Performance and Reproducibility

BACKGROUND: Cardiovascular magnetic resonance feature tracking (CMR-FT) is increasingly used for myocardial deformation assessment including ventricular strain, showing prognostic value beyond established risk markers if used in experienced centres. Little is known about the impact of appropriate training on CMR-FT performance. Consequently, this study aimed to evaluate the impact of training on observer variance using different commercially available CMR-FT software. METHODS: Intra- and inter-observer reproducibility was assessed prior to and after dedicated one-hour observer training. Employed FT software included 3 different commercially available platforms (TomTec, Medis, Circle). Left (LV) and right (RV) ventricular global longitudinal as well as LV circumferential and radial strains (GLS, GCS and GRS) were studied in 12 heart failure patients and 12 healthy volunteers. RESULTS: Training improved intra- and inter-observer reproducibility. GCS and LV GLS showed the highest reproducibility before (ICC \u3e0.86 and \u3e0.81) and after training (ICC \u3e0.91 and \u3e0.92). RV GLS and GRS were more susceptible to tracking inaccuracies and reproducibility was lower. Inter-observer reproducibility was lower than intra-observer reproducibility prior to training with more pronounced improvements after training. Before training, LV strain reproducibility was lower in healthy volunteers as compared to patients with no differences after training. Whilst LV strain reproducibility was sufficient within individual software solutions inter-software comparisons revealed considerable software related variance. CONCLUSION: Observer experience is an important source of variance in CMR-FT derived strain assessment. Dedicated observer training significantly improves reproducibility with most profound benefits in states of high myocardial contractility and potential to facilitate widespread clinical implementation due to optimized robustness and diagnostic performance