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This study aims at identifying risk-related patterns of left ventricular contraction dynamics via novel volume transient characterization. A multicenter cohort of AMI survivors (n = 1021) who underwent Cardiac Magnetic Resonance (CMR) after infarction was considered for the study. The clinical endpoint was the 12-month rate of major adverse cardiac events (MACE, n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. Cardiac function was characterized from CMR in 3 potential directions: by (1) volume temporal transients (i.e. contraction dynamics); (2) feature tracking strain analysis (i.e. bulk tissue peak contraction); and (3) 3D shape analysis (i.e. 3D contraction morphology). A fully automated pipeline was developed to extract conventional and novel artificial-intelligence-derived metrics of cardiac contraction, and their relationship with MACE was investigated. Any of the 3 proposed directions demonstrated its additional prognostic value on top of established CMR indexes, myocardial injury markers, basic characteristics, and cardiovascular risk factors (P < 0.001). The combination of these 3 directions of enhancement towards a final CMR risk model improved MACE prediction by 13% compared to clinical baseline (0.774 (0.771—0.777) vs. 0.683 (0.681—0.685) cross-validated AUC, P < 0.001). The study evidences the contribution of the novel contraction characterization, enabled by a fully automated pipeline, to post-infarction assessment. [ABSTRACT FROM AUTHOR]

BACKGROUND: It remains unknown to what extent intrinsic atrial cardiomyopathy or left ventricular diastolic dysfunction drive atrial remodeling and functional failure in heart failure with preserved ejection fraction (HFpEF). Computational 3-dimensional (3D) models fitted to cardiovascular magnetic resonance allow state-of-the-art anatomic and functional assessment, and we hypothesized to identify a phenotype linked to HFpEF. METHODS: Patients with exertional dyspnea and diastolic dysfunction on echocardiography (E/e', >8) were prospectively recruited and classified as HFpEF or noncardiac dyspnea based on right heart catheterization. All patients underwent rest and exercise-stress right heart catheterization and cardiovascular magnetic resonance. Computational 3D anatomic left atrial (LA) models were generated based on short-axis cine sequences. A fully automated pipeline was developed to segment cardiovascular magnetic resonance images and build 3D statistical models of LA shape and find the 3D patterns discriminant between HFpEF and noncardiac dyspnea. In addition, atrial morphology and function were quantified by conventional volumetric analyses and deformation imaging. A clinical follow-up was conducted after 24 months for the evaluation of cardiovascular hospitalization. RESULTS: Beyond atrial size, the 3D LA models revealed roof dilation as the main feature found in masked HFpEF (diagnosed during exercise-stress only) preceding a pattern shift to overall atrial size in overt HFpEF (diagnosed at rest). Characteristics of the 3D model were integrated into the LA HFpEF shape score, a biomarker to characterize the gradual remodeling between noncardiac dyspnea and HFpEF. The LA HFpEF shape score was able to discriminate HFpEF (n=34) to noncardiac dyspnea (n=34; area under the curve, 0.81) and was associated with a risk for atrial fibrillation occurrence (hazard ratio, 1.02 [95% CI, 1.01-1.04]; P=0.003), as well as cardiovascular hospitalization (hazard ratio, 1.02 [95% CI, 1.00-1.04]; P=0.043). CONCLUSIONS: LA roof dilation is an early remodeling pattern in masked HFpEF advancing to overall LA enlargement in overt HFpEF. These distinct features predict the occurrence of atrial fibrillation and cardiovascular hospitalization. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular magnetic resonance imaging is considered the reference standard for assessing cardiac morphology and function and has demonstrated prognostic utility in patients undergoing transcatheter aortic valve replacement (TAVR). Novel fully automated analyses may facilitate data analyses but have not yet been compared against conventional manual data acquisition in patients with severe aortic stenosis (AS).Methods: Fully automated and manual biventricular assessments were performed in 139 AS patients scheduled for TAVR using commercially available software (suiteHEART®, Neosoft; QMass®, Medis Medical Imaging Systems). Volumetric assessment included left ventricular (LV) mass, LV/right ventricular (RV) end-diastolic/end-systolic volume, LV/RV stroke volume, and LV/RV ejection fraction (EF). Results of fully automated and manual analyses were compared. Regression analyses and receiver operator characteristics including area under the curve (AUC) calculation for prediction of the primary study endpoint cardiovascular (CV) death were performed.Results: Fully automated and manual assessment of LVEF revealed similar prediction of CV mortality in univariable (manual: hazard ratio (HR) 0.970 (95% CI 0.943-0.997) p=0.032; automated: HR 0.967 (95% CI 0.939-0.995) p=0.022) and multivariable analyses (model 1: (including significant univariable parameters) manual: HR 0.968 (95% CI 0.938-0.999) p=0.043; automated: HR 0.963 [95% CI 0.933-0.995] p=0.024; model 2: (including CV risk factors) manual: HR 0.962 (95% CI 0.920-0.996) p=0.027; automated: HR 0.954 (95% CI 0.920-0.989) p=0.011). There were no differences in AUC (LVEF fully automated: 0.686; manual: 0.661; p=0.21). Absolute values of LV volumes differed significantly between automated and manual approaches (p < 0.001 for all). Fully automated quantification resulted in a time saving of 10 minutes per patient.Conclusion: Fully automated biventricular volumetric assessments enable efficient and equal risk prediction compared to conventional manual approaches. In addition to significant time saving, this may provide the tools for optimized clinical management and stratification of patients with severe AS undergoing TAVR. [ABSTRACT FROM AUTHOR]

Cardiovascular magnetic resonance (CMR) imaging provides reliable assessments of biventricular morphology and function. Since manual post-processing is time-consuming and prone to observer variability, efforts have been directed towards novel artificial intelligence-based fully automated analyses. Hence, we sought to investigate the impact of artificial intelligence-based fully automated assessments on the inter-study variability of biventricular volumes and function. Eighteen participants (11 with normal, 3 with heart failure and preserved and 4 with reduced ejection fraction (EF)) underwent serial CMR imaging at in median 63 days (range 49–87) interval. Short axis cine stacks were acquired for the evaluation of left ventricular (LV) mass, LV and right ventricular (RV) end-diastolic, end-systolic and stroke volumes as well as EF. Assessments were performed manually (QMass, Medis Medical Imaging Systems, Leiden, Netherlands) by an experienced (3 years) and inexperienced reader (no active reporting, 45 min of training with five cases from the SCMR consensus data) as well as fully automated (suiteHEART, Neosoft, Pewaukee, WI, USA) without any manual corrections. Inter-study reproducibility was overall excellent with respect to LV volumetric indices, best for the experienced observer (intraclass correlation coefficient (ICC) > 0.98, coefficient of variation (CoV, < 9.6%) closely followed by automated analyses (ICC > 0.93, CoV < 12.4%) and lowest for the inexperienced observer (ICC > 0.86, CoV < 18.8%). Inter-study reproducibility of RV volumes was excellent for the experienced observer (ICC > 0.88, CoV < 10.7%) but considerably lower for automated and inexperienced manual analyses (ICC > 0.69 and > 0.46, CoV < 22.8% and < 28.7% respectively). In this cohort, fully automated analyses allowed reliable serial investigations of LV volumes with comparable inter-study reproducibility to manual analyses performed by an experienced CMR observer. In contrast, RV automated quantification with current algorithms still relied on manual post-processing for reliability. [ABSTRACT FROM AUTHOR]

Background: Right heart catheterization using exercise stress is the reference standard for the diagnosis of heart failure with preserved ejection fraction (HFpEF) but carries the risk of the invasive procedure. We hypothesized that real-time cardiac magnetic resonance (RT-CMR) exercise imaging with pathophysiologic data at excellent temporal and spatial resolution may represent a contemporary noninvasive alternative for diagnosing HFpEF.Methods: The HFpEF-Stress trial (CMR Exercise Stress Testing in HFpEF; URL: https://www.clinicaltrials.gov; Unique identifier: NCT03260621. URL: https://dzhk.de/; Unique identifier: DZHK-17) prospectively recruited 75 patients with echocardiographic signs of diastolic dysfunction and dyspnea on exertion (E/e'>8, New York Heart Association class ≥II) to undergo echocardiography, right heart catheterization, and RT-CMR at rest and during exercise stress. HFpEF was defined according to pulmonary capillary wedge pressure (≥15 mm Hg at rest or ≥25 mm Hg during exercise stress). RT-CMR functional assessments included time-volume curves for total and early (1/3) diastolic left ventricular filling, left atrial (LA) emptying, and left ventricular/LA long axis strain.Results: Patients with HFpEF (n=34; median pulmonary capillary wedge pressure at rest, 13 mm Hg; at stress, 27 mm Hg) had higher E/e' (12.5 versus 9.15), NT-proBNP (N-terminal pro-B-type natriuretic peptide; 255 versus 75 ng/L), and LA volume index (43.8 versus 36.2 mL/m2) compared with patients with noncardiac dyspnea (n=34; rest, 8 mm Hg; stress, 18 mm Hg; P≤0.001 for all). Seven patients were excluded because of the presence of non-HFpEF cardiac disease causing dyspnea on imaging. There were no differences in RT-CMR left ventricular total and early diastolic filling at rest and during exercise stress (P≥0.164) between patients with HFpEF and noncardiac dyspnea. RT-CMR revealed significantly impaired LA total and early (P<0.001) diastolic emptying in patients with HFpEF during exercise stress. RT-CMR exercise stress LA long axis strain was independently associated with HFpEF (adjusted odds ratio, 0.657 [95% CI, 0.516-0.838]; P=0.001) after adjustment for clinical and imaging measures and emerged as the best predictor for HFpEF (area under the curve at rest 0.82 versus exercise stress 0.93; P=0.029).Conclusions: RT-CMR allows highly accurate identification of HFpEF during physiologic exercise and qualifies as a suitable noninvasive diagnostic alternative. These results will need to be confirmed in multicenter prospective research studies to establish widespread routine clinical use. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03260621. URL: https://dzhk.de/; Unique identifier: DZHK-17. [ABSTRACT FROM AUTHOR]

Myocardial deformation assessment by cardiovascular magnetic resonance-feature tracking (CMR-FT) has incremental prognostic value over volumetric analyses. Recently, atrial functional analyses have come to the fore. However, to date recommendations for optimal resolution parameters for accurate atrial functional analyses are still lacking. CMR-FT was performed in 12 healthy volunteers and 9 ischemic heart failure (HF) patients. Cine sequences were acquired using different temporal (20, 30, 40 and 50 frames/cardiac cycle) and spatial resolution parameters (high 1.5 × 1.5 mm in plane and 5 mm slice thickness, standard 1.8 × 1.8 × 8 mm and low 3.0 × 3.0 × 10 mm). Inter- and intra-observer reproducibility were calculated. Increasing temporal resolution is associated with higher absolute strain and strain rate (SR) values. Significant changes in strain assessment for left atrial (LA) total strain occurred between 20 and 30 frames/cycle amounting to 2,5–4,4% in absolute changes depending on spatial resolution settings. From 30 frames/cycle onward, absolute strain values remained unchanged. Significant changes of LA strain rate assessment were observed up to the highest temporal resolution of 50 frames/cycle. Effects of spatial resolution on strain assessment were smaller. For LA total strain a general trend emerged for a mild decrease in strain values obtained comparing the lowest to the highest spatial resolution at temporal resolutions of 20, 40 and 50 frames/cycle (p = 0.006–0.046) but not at 30 frames/cycle (p = 0.140). Temporal and to a smaller extent spatial resolution affect atrial functional assessment. Consistent strain assessment requires a standard spatial resolution and a temporal resolution of 30 frames/cycle, whilst SR assessment requires even higher settings of at least 50 frames/cycle. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Recently introduced fast strain-encoded (SENC) cardiac magnetic resonance (CMR) imaging (fast-SENC) provides real-time acquisition of myocardial performance in a single heartbeat. We aimed to test the ability and accuracy of real-time strain-encoded CMR imaging to estimate left ventricular volumes, ejection fraction and mass.Methods: Thirty-five subjects (12 healthy volunteers and 23 patients with known or suspected coronary artery disease) were investigated. All study participants were imaged at 1.5 Tesla MRI scanner (Achieva, Philips) using an advanced CMR study protocol which included conventional cine and fast-SENC imaging. A newly developed real-time free-breathing SENC imaging technique based on the acquisition of two images with different frequency modulation was employed.Results: All parameters were successfully derived from fast-SENC images with total study time of 105 s (a 15 s scan time and a 90 s post-processing time). There was no significant difference between fast-SENC and cine imaging in the estimation of LV volumes and EF, whereas fast-SENC underestimated LV end-diastolic mass by 7%.Conclusion: The single heartbeat fast-SENC technique can be used as a good alternative to cine imaging for the precise calculation of LV volumes and ejection fraction while the technique significantly underestimates LV end-diastolic mass. [ABSTRACT FROM AUTHOR]