BackgroundNon-invasive cardiac imaging allows detection of cardiac amyloidosis (CA) in patients with aortic stenosis (AS). Our objective was to estimate the prevalence of clinically suspected CA in patients with moderate and severe AS referred for cardiovascular magnetic resonance (CMR) in age and gender categories, and assess associations between AS-CA and all-cause mortality.MethodsWe retrospectively identified consecutive AS patients defined by echocardiography referred for further CMR assessment of valvular, myocardial, and aortic disease. CMR identified CA based on typical late-gadolinium enhancement (LGE) patterns, and ancillary clinical evaluation identified suspected CA. Survival analysis with the Log rank test and Cox regression compared associations between CA and mortality.ResultsThere were 113 patients (median age 74 years, Q1-Q3: 62–82 years), 96 (85%) with severe AS. Suspected CA was present in 9 patients (8%) all > 80 years. Among those over the median age of 74 years, the prevalence of CA was 9/57 (16%), and excluding women, the prevalence was 8/25 (32%). Low-flow, low-gradient physiology was very common in CA (7/9 patients or 78%). Over a median follow-up of 18 months, 40 deaths (35%) occurred. Mortality in AS + CA patients was higher than AS alone (56% vs. 20% at 1-year, log rank 15.0, P < 0.0001). Adjusting for aortic valve replacement modeled as a time-dependent covariate, Society of Thoracic Surgery predicted risk of mortality, left ventricular ejection fraction, CA remained associated with all-cause mortality (HR = 2.92, 95% CI = 1.09–7.86, P = 0.03).ConclusionsSuspected CA appears prevalent among older male patients with AS, especially with low flow, low gradient AS, and associates with all-cause mortality. The importance of screening for CA in older AS patients and optimal treatment strategies in those with CA warrant further investigation, especially in the era of transcatheter aortic valve implantation.
Among myriad changes occurring during the apparent evolution of HFpEF where elevated BNP is prevalent, MF was similarly prevalent in those with or at risk for HFpEF. Conceivably, MF might precede clinical HFpEF diagnosis. Regardless, MF was associated with disease severity (ie, BNP) and outcomes. Whether cells and secretomes mediating MF represent therapeutic targets in HFpEF warrants further evaluation.
Since risk stratification data represents a key domain of biomarker validation, we compared associations between outcomes and various cardiovascular magnetic resonance (CMR) metrics quantifying myocardial fibrosis (MF) in noninfarcted myocardium: extracellular volume fraction (ECV), native T1, post contrast T1, and partition coefficient. Background: MF associates with vulnerability to adverse events e.g., mortality and hospitalization for heart failure (HHF), but investigators still debate its optimal measurements; most histologic validation data show strongest ECV correlations with MF. Methods: We enrolled 1714 consecutive patients without amyloidosis or hypertrophic cardiomyopathy from a single CMR referral center serving an integrated healthcare network. We measured T1 (MOLLI) in noninfarcted myocardium, averaged from 2 short axis slices (basal and mid) before and 15-20 minutes after a gadolinium contrast bolus. We compared chi square (χ 2) values from CMR MF measures in univariable and multivariable Cox regression models. We assessed "dose-response" relationships in Kaplan Meier curves using log-rank statistics for quartile strata. We also computed net reclassification improvement (NRI) and integrated discrimination improvement (IDI for Cox models with ECV vs. native T1. Results: Over a median of 5.6 years, 374 events occurred after CMR (162 HHF events and 279 deaths, 67 with both). ECV yielded best separation of Kaplan-Meier curves and highest log-ranks statistics. In univariable and multivariable models, ECV associated most strongly with outcomes, demonstrating the highest χ 2 values. Native T1 or post contrast T1 did not associate with outcomes in the multivariable model. ECV provided added prognostic value to models with native T1, e.g., in multivariable models IDI=0.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.