Indexed left ventricular mass to QRS voltage ratio is associated with heart failure hospitalizations in patients with cardiac amyloidosis

In cardiac amyloidosis (CA), amyloid infiltration results in increased left ventricular (LV) mass disproportionate to electrocardiographic (EKG) voltage. We assessed the relationship between LV mass-voltage ratio with subsequent heart failure hospitalization (HHF) and mortality in CA. Patients with confirmed CA and comprehensive cardiovascular magnetic resonance (CMR) and EKG exams were included. CMR-derived LV mass was indexed to body surface area. EKG voltage was assessed using Sokolow, Cornell, and Limb-voltage criteria. The optimal LV mass-voltage ratio for predicting outcomes was determined using receiver operating characteristic curve analysis. The relationship between LV mass-voltage ratio and HHF was assessed using Cox proportional hazards analysis adjusting for significant covariates. A total of 85 patients (mean 69 ± 11 years, 22% female) were included, 42 with transthyretin and 43 with light chain CA. At a median of 3.4-year follow-up, 49% of patients experienced HHF and 60% had died. In unadjusted analysis, Cornell LV mass-voltage ratio was significantly associated with HHF (HR, 1.05; 95% CI 1.02-1.09, p = 0.001) and mortality (HR, 1.05; 95% CI 1.02-1.07, p = 0.001). Using ROC curve analysis, the optimal cutoff value for Cornell LV mass-voltage ratio to predict HHF was 6.7 gm/m2/mV. After adjusting for age, NYHA class, BNP, ECV, and LVEF, a Cornell LV mass-voltage ratio > 6.7 gm/m2/mV was significantly associated with HHF (HR 2.25, 95% CI 1.09-4.61; p = 0.03) but not mortality. Indexed LV mass-voltage ratio is associated with subsequent HHF and may be a useful prognostic marker in cardiac amyloidosis

ATTR Gene Variants in HCM

Hypertrophic cardiomyopathy is the most common inherited cardiomyopathy, with a prevalence of 1:200 to 1:500. Cardiac amyloidosis, another cardiomyopathy caused by myocardial deposition of abnormally folded TTR protein, can be acquired or hereditary. The presence of pathogenic TTR gene variants in patients with phenotypic HCM is an underrecognized and clinically important entity

Impairment in quantitative microvascular function in non-ischemic cardiomyopathy as demonstrated using cardiovascular magnetic resonance

Background: Microvascular dysfunction (MVD) is present in various cardiovascular diseases and portends worse outcomes. We assessed the prevalence of MVD in patients with non-ischemic cardiomyopathy (NICM) as compared to subjects with preserved ejection fraction (EF) using stress cardiovascular magnetic resonance (CMR). Methods: We retrospectively studied consecutive patients with NICM and 58 subjects with preserved left ventricular (LV) EF who underwent stress CMR between 2011–2016. MVD was defined visually as presence of a subendocardial perfusion defect and semiquantitatively by myocardial perfusion reserve index (MPRI Results: In total, 41 patients with NICM (mean age 51 ± 14, 59% male) and 58 subjects with preserved LVEF (mean age 51 ± 13, 31% male) were identified. In the NICM group, MVD was present in 23 (56%) and 11 (27%) by semiquantitative and visual evaluation respectively. Compared to those with preserved LVEF, NICM patients had lower rest slope (3.9 vs 4.9, p = 0.05) and stress perfusion slope (8.8 vs 11.7, p Conclusions: MVD—as assessed using CMR—is highly prevalent in NICM as compared to subjects with preserved LVEF even after controlling for covariates. Semiquantitative is able to detect a greater number of incidences of MVD compared to visual methods alone. Further studies are needed to determine whether treatment of MVD is beneficial in NICM.</p

Artificial intelligence based left ventricular ejection fraction and global longitudinal strain in cardiac amyloidosis

Background: Assessment of left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) plays a key role in the diagnosis of cardiac amyloidosis (CA). However, manual measurements are time consuming and prone to variability. We aimed to assess whether fully automated artificial intelligence (AI) calculation of LVEF and GLS provide similar estimates and can identify abnormalities in agreement with conventional manual methods, in patients with pre-clinical and clinical CA. Methods: We identified 51 patients (age 80±10 years, 53% male) with confirmed CA according to guidelines, who underwent echocardiography before and/or at the time of CA diagnosis (median (IQR) time between observations 3.87 (1.93, 5.44) yrs). LVEF and GLS were quantified from the apical 2- and 4-chamber views using both manual and fully automated methods (EchoGo Core 2.0, Ultromics). Inter-technique agreement was assessed using linear regression and Bland-Altman analyses and two-way ANOVA. The diagnostic accuracy and time for detecting abnormalities (defined as LVEF ≤50% and GLS≥-15.1%, respectively) using AI was assessed by comparisons to manual measurements as a reference. Results: There were no significant differences in manual and automated LVEF and GLS values in either pre-CA (p=0.791 and p=0.105, respectively) or at diagnosis (p=0.463 and p=0.722). The two methods showed strong correlation on both the pre-CA (r=0.78 and r=0.83) and CA echoes (r=0.74 and r=0.80) for LVEF and GLS, respectively. The sensitivity and specificity of AI-derived indices for detecting abnormal LVEF were 83% and 86%, respectively, in the pre-CA echo and 70% and 79% at CA diagnosis. The sensitivity and specificity of AI-derived indices for detecting abnormal GLS was 82% and 86% in the pre-CA echo and 100% and 67% at the time of CA diagnosis. There was no significant difference in the relationship between LVEF (p=0.99) and GLS (p=0.19) and time to abnormality between the two methods. Conclusion: Fully automated AI-calculated LVEF and GLS are comparable to manual measurements in patients pre-CA and at the time of CA diagnosis. The widespread implementation of automated LVEF and GLS may allow for more rapid assessment in different disease states with comparable accuracy and reproducibility to manual methods

Fibrosing Mediastinitis Caused by Histoplasmosis in an Adolescent

Fibrosing mediastinitis (FM) is a rare, potentially progressive disease resulting from an idiosyncratic immune response to a variety of stimuli that lead to fibrous infiltration of the mediastinum and possible narrowing of the bronchovascular structures. We report an unusual case of FM in a pediatric patient presenting as myopericarditis and progressing to pericardial thickening and encasement of the mediastinal vascular structures needing surgical intervention. Imaging, including transthoracic echocardiography, cardiac computed tomography, and cardiac magnetic resonance played a crucial role in the diagnosis, assessment, and follow-up. Contrast-enhanced computed tomography can be especially helpful to demonstrate potential findings associated with FM

Cardiovascular computed tomography for the detection of quadricuspid aortic valve: A case report

Aortic regurgitation (AR) represents a significant cause of morbidity and mortality. Due to its low cost and widespread availability, echocardiography remains the frontline for aortic valve (AV) assessment. However, poor sonographic windows may limit the assessment of valve morphology with this technique. Cardiovascular computed tomography (CCT) is increasingly utilized prior to structural AV interventions. Due to its excellent spatial resolution, CCT provides exceptional characterization of aortic leaflets. Accordingly, we present a case of a quadricuspid valve diagnosed by CCT. Here, CCT led to a new diagnosis of quadricuspid valve, highlighting the potential for CCT for the characterization of aortic leaflet morphology. CCT may be particularly useful in patients with contraindications to transesophageal echocardiography or those undergoing structural or robotic interventions

Comparison of rates of medical therapy utilization in NICM patients vs those with preserved LVEF.

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Determining myocardial perfusion reserve index.

Panel A, B: First pass perfusion images with endocardial, epicardial, and blood pool contours. Panel C-F: Time intensity curve graphs at rest (C, D) and stress (E, F) first pass perfusion with maximal upslopes of blood pool (orange line) and myocardium (blue line). MPRI is calculated as the ratio of RUstress/RUrest where RU is ratio of maximal upslope of myocardium divided by blood pool.</p

Myocardial perfusion reserve index (MPRI) in the non-ischemic cardiomyopathy (NICM) vs the preserved LVEF cohorts.

Patients with NICM have significantly more impaired MPRI.</p

Clinical and CMR characteristics in NICM patients with vs. without impaired myocardial perfusion reserve index.

Clinical and CMR characteristics in NICM patients with vs. without impaired myocardial perfusion reserve index.</p