Left Ventricular Mass, Myocardial Structure, and Function in Severe Aortic Stenosis: an Echocardiographic and Cardiac Magnetic Resonance Imaging Study.

In severe aortic stenosis (AS), there are conflicting data on the prognostic implications of left ventricular (LV) hypertrophy (LVH). We aimed to characterize the LV geometry, myocardial matrix structural changes, and prognostic stratification using cardiac magnetic resonance imaging (CMR) and echocardiography in subjects with severe AS with and without LVH. Consecutive patients who had severe isolated AS and sufficient quality echocardiography and CMR within 6 months of each other were evaluated for LVH, cardiac structure, morphology, and late gadolinium-enhancement imaging. Kaplan-Meier curves, linear models, and proportional hazards models were used for prognostic stratification. There were 93 patients enrolled (mean age 74 ± 11 years, 48% female), of whom 38 (41%) had a normal LV mass index (LVMI), 41 (44%) had LVH defined at CMR by LVMI >2 SD higher than normal, and 14 (15% of the total) with >4 SD higher than the reference LVMI (severely elevated). The Society of Thoracic Surgeons scores were similar among the LVMI groups. Compared with those with normal LVMI, patients with LVH had higher LV end-diastolic and end-systolic volumes, increased late gadolinium-enhancement burden, and lower LV ejection fraction. Most notably, CMR feature-tracking global radial strain, 2-dimensional speckle-tracking echocardiography global longitudinal strain, and left atrial reservoir function were significantly worse. On the survival analyses, LVMI was not associated with a composite of all-cause mortality and/or heart failure hospitalization. In conclusion, compared with normal LVMI, elevated LVMI was not associated with a higher risk of adverse outcomes.

A deep learning approach to real-time volumetric measurements without image reconstruction for cardiovascular magnetic resonance.

Objective.Cardiovascular magnetic resonance (CMR) can measure ventricular volumes for the quantitative assessment of cardiac function in clinical cardiology. Conventionally, CMR volumetric measurements require image reconstruction and segmentation. There are limited clinical applications of real-time CMR for volumetric measurements because real-time images cannot provide sufficient quality for accurate segmentation. The presented work aims to develop a new deep learning approach to measuring ventricular volumes without image reconstruction and demonstrate that this 'imageless' approach would improve volumetric measurements with real-time CMR.Approach. We have developed a deep learning model for measuring ventricular volumes directly from real-time CMR raw data without image reconstruction. This novel 'imageless' deep learning model, not being as sensitive to image quality, provided reliable volumetric measurements for real-time CMR. To demonstrate 'imageless' volumetric measurements, we conducted a real-time CMR study with healthy volunteers. Several performance metrics, including mean absolute error (MAE), the Pearson correlation coefficient, and Bland-Altman analysis, were used to evaluate the proposed 'imageless' deep learning model in reference to U-net and fully convolutional neural network (FCNN) models based on conventional image reconstruction and segmentation.Main results. With the same raw data, the 'imageless' deep learning model gave a lower MAE ('imageless' ≤9.6 ml; 'image-based' ≥12.1 ml), a higher correlation coefficient ('imageless' ≥0.75; 'image-based' ≤0.51) and smaller measurement difference ranges in Bland-Altman analysis ('imageless' ≤23.1 ml; 'image-based' ≥33.8 ml). To achieve comparable performance, the 'imageless' deep learning model needed 2/3 of the raw data used in image reconstruction for U-net and FCNN models, indicating there was a gain in imaging acceleration for real-time CMR.Significance. We have demonstrated a novel deep learning framework that can provide reliable volumetric measurements from real-time CMR raw data without image reconstruction. This 'imageless' approach to real-time volumetric measurements will improve the quantitative assessment of cardiac function in clinical cardiology.

Association of Unrecognized Myocardial Infarction With Long-term Outcomes in Community-Dwelling Older Adults: The ICELAND MI Study.

Importance: Cardiac magnetic resonance (CMR) imaging can identify unrecognized myocardial infarction (UMI) in the general population. Unrecognized myocardial infarction by CMR portends poor prognosis in the short term but, to our knowledge, long-term outcomes are not known. Objective: To determine the long-term outcomes of UMI by CMR compared with clinically recognized myocardial infarction (RMI) and no myocardial infarction (MI). Design, Setting, and Participants: Participants of the population-based, prospectively enrolled ICELAND MI cohort study (aged 67-93 years) were characterized with CMR at baseline (from January 2004-January 2007) and followed up for up to 13.3 years. Kaplan-Meier time-to-event analyses and a Cox regression were used to assess the association of UMI at baseline with death and future cardiovascular events. Main Outcomes and Measures: The primary outcome was all-cause mortality. Secondary outcomes were a composite of major adverse cardiac events (MACE: death, nonfatal MI, and heart failure). Results: Of 935 participants, 452 (48.3%) were men; the mean (SD) age of participants with no MI, UMI, and RMI was 75.6 (5.3) years, 76.8 (5.2) years, and 76.8 (4.7) years, respectively. At 3 years, UMI and no MI mortality rates were similar (3%) and lower than RMI rates (9%). At 5 years, UMI mortality rates (13%) increased and were higher than no MI rates (8%) but still lower than RMI rates (19%). By 10 years, UMI and RMI mortality rates (49% and 51%, respectively) were not statistically different; both were significantly higher than no MI (30%) (P < .001). After adjusting for age, sex, and diabetes, UMI by CMR had an increased risk of death (hazard ratio [HR], 1.61; 95% CI, 1.27-2.04), MACE (HR, 1.56; 95% CI, 1.26-1.93), MI (HR, 2.09; 95% CI, 1.45-3.03), and heart failure (HR, 1.52; 95% CI, 1.09-2.14) compared with no MI and statistically nondifferent risk of death (HR, 0.99; 95% CI, 0.71-1.38) and MACE (HR, 1.23; 95% CI, 0.91-1.66) vs RMI. Conclusions and Relevance: In this study, all-cause mortality of UMI was higher than no MI, but within 10 years from baseline evaluation was equivalent with RMI. Unrecognized MI was also associated with an elevated risk of nonfatal MI and heart failure. Whether secondary prevention can alter the prognosis of UMI will require prospective testing.