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.

No abstract

BackgroundMyocardial fibrosis quantified by myocardial extracellular volume fraction (ECV) and left ventricular mass (LVM) index (LVMI) measured by cardiovascular magnetic resonance might represent independent and opposing contributors to ECG voltage measures of left ventricular hypertrophy (LVH). Diffuse myocardial fibrosis can occur in LVH and interfere with ECG voltage measures. This phenomenon could explain the decreased sensitivity of LVH detectable by ECG, a fundamental diagnostic tool in cardiology.Methods and ResultsWe identified 77 patients (median age, 53 [interquartile range, 26–60] years; 49% female) referred for contrast‐enhanced cardiovascular magnetic resonance with ECV measures and 12‐lead ECG. Exclusion criteria included clinical confounders that might influence ECG measures of LVH. We evaluated ECG voltage‐based LVH measures, including Sokolow‐Lyon index, Cornell voltage, 12‐lead voltage, and the vectorcardiogram spatial QRS voltage, with respect to LVMI and ECV. ECV and LVMI were not correlated (R 2=0.02; P=0.25). For all voltage‐related parameters, higher LVMI resulted in greater voltage (r=0.33–0.49; P<0.05 for all), whereas increased ECV resulted in lower voltage (r=−0.32 to −0.57; P<0.05 for all). When accounting for body fat, LV end‐diastolic volume, and mass‐to‐volume ratio, both LVMI (β=0.58, P=0.03) and ECV (β=−0.46, P<0.001) were independent predictors of QRS voltage (multivariate adjusted R 2=0.39; P<0.001).ConclusionsMyocardial mass and diffuse myocardial fibrosis have independent and opposing effects upon ECG voltage measures of LVH. Diffuse myocardial fibrosis quantified by ECV can obscure the ECG manifestations of increased LVM. This provides mechanistic insight, which can explain the limited sensitivity of the ECG for detecting increased LVM.

No abstract

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.

IntroductionExcretion of cardiovascular magnetic resonance (CMR) extracellular gadolinium-based contrast agents (GBCA) into pleural and pericardial effusions, sometimes referred to as vicarious excretion, has been described as a rare occurrence using T1-weighted imaging. However, the T1 mapping characteristics as well as presence, magnitude and dynamics of contrast excretion into these effusions is not known.AimsTo investigate and compare the differences in T1 mapping characteristics and extracellular GBCA excretion dynamics in pleural and pericardial effusions.MethodsClinically referred patients with a pericardial and/or pleural effusion underwent CMR T1 mapping at 1.5 T before, and at 3 (early) and at 27 (late) minutes after administration of an extracellular GBCA (0.2 mmol/kg, gadoteric acid). Analyzed effusion characteristics were native T1, ΔR1 early and late after contrast injection, and the effusion-volume-independent early-to-late contrast concentration ratio ΔR1early/ΔR1late, where ΔR1 = 1/T1post-contrast - 1/T1native.ResultsNative T1 was lower in pericardial effusions (n = 69) than in pleural effusions (n = 54) (median [interquartile range], 2912 [2567–3152] vs 3148 [2692–3494] ms, p = 0.005). Pericardial and pleural effusions did not differ with regards to ΔR1early (0.05 [0.03–0.10] vs 0.07 [0.03–0.12] s− 1, p = 0.38). Compared to pleural effusions, pericardial effusions had a higher ΔR1late (0.8 [0.6–1.2] vs 0.4 [0.2–0.6] s− 1, p < 0.001) and ΔR1early/ΔR1late (0.19 [0.08–0.30] vs 0.12 [0.04–0.19], p < 0.001).ConclusionsT1 mapping shows that extracellular GBCA is excreted into pericardial and pleural effusions. Consequently, the previously used term vicarious excretion is misleading. Compared to pleural effusions, pericardial effusions had both a lower native T1, consistent with lesser relative fluid content in relation to other components such as proteins, and more prominent early excretion dynamics, which could be related to inflammation. The clinical diagnostic utility of T1 mapping to determine quantitative contrast dynamics in pericardial and pleural effusions merits further investigation.

No abstract