Evaluating left atrial strain and left ventricular diastolic strain rate as markers for diastolic dysfunction in patients with mitral annular calcification.

BACKGROUND: Mitral annular calcification (MAC) poses many challenges to the evaluation of diastolic function using standard echocardiography. Left atrial (LA) strain and left ventricular early diastolic strain rate (DSr) measured by speckle-tracking echocardiography (STE) are emerging techniques in the noninvasive evaluation of diastolic function. We aim to evaluate the utility of LA strain and early DSr in predicting elevated left ventricular filling pressures (LVFP) in patients with MAC and compare their effectiveness to ratio of mitral inflow velocity in early and late diastole (E/A). METHODS: We included adult patients with MAC who presented between January 1 and December 31, 2014 and received a transthoracic echocardiogram (TTE) and cardiac catheterization with measurement of LVFP within a 24-h period. We used Spearman's rank correlation coefficient to assess associations of LA reservoir strain and average early DSr with LVFP. Receiver operating characteristic (ROC) curves were computed to assess the effectiveness of LA strain and DSr in discriminating elevated LVFP as a dichotomized variable and to compare their effectiveness with E/A ratio categorized according to grade of diastolic dysfunction. RESULTS: Fifty-five patients were included. LA reservoir strain demonstrated poor correlation with LVFP (Spearman's rho = 0.03, p = 0.81) and poor discriminatory ability for detecting elevated LVFP (AUC = 0.54, 95% CI 0.38-0.69). Categorical E/A ratio alone also demonstrated poor discriminatory ability (AUC = 0.53, 95% CI 0.39-0.67), and addition of LA reservoir strain did not significantly improve effectiveness (AUC = 0.58, 95% CI 0.42-0.74, p = 0.56). Average early DSr also demonstrated poor correlation with LVFP (Spearman's rho = -0.19, p = 0.16) and poor discriminatory ability for detecting elevated LVFP (AUC = 0.59, 95% CI 0.44-0.75). Addition of average early DSr to categorical E/A ratio failed to improve effectiveness (AUC = 0.62, 95% CI 0.46-0.77 vs. AUC = 0.54, 95% CI 0.39-0.69, p = 0.38). CONCLUSIONS: In our sample, LA reservoir strain and DSr do not accurately predict diastolic filling pressure. Further research is required before LA strain and early DSr can be routinely used in clinical practice to assess filling pressure in patients with MAC.

Left atrial strain, embolic stroke of undetermined source, and atrial fibrillation detection.

BACKGROUND: Atrial cardiopathy is a proposed mechanism of embolic stroke of undetermined source (ESUS). Left atrial (LA) strain may identify early atrial cardiopathy prior to structural changes. We aim to study the associations between LA strain, ESUS, and atrial fibrillation (AF) detection in ESUS. METHODS: The study population included patients with ESUS and noncardioembolic (NCE) stroke presenting to the Rhode Island Hospital Stroke Center between January 2016 and June 2017 who underwent transthoracic echocardiography. Speckle tracking echocardiography (STE) was used to measure the three phases of LA strain (reservoir, conduit, and contractile). Binary logistic regression analysis was performed to determine the associations between LA strain and stroke subtype (ESUS vs. NCE) as well as follow-up detection of AF in ESUS patients. RESULTS: We identified 656 patients, 307 with ESUS and 349 with NCE. In binary logistic regression, the lowest tertiles of LA reservoir (adjusted OR 1.944, 95% CI 1.266-2.986, p = .002), contractile (aOR 1.568, 95% CI 1.035-2.374, p = .034), and conduit strain (aOR 2.288, 95% CI 1.448-3.613, p = .001) were more likely to be significantly associated with ESUS compared to NCE stroke. Among all ESUS patients, the lowest tertiles of LA reservoir strain (OR 2.534, 95% CI 1.029-6.236, p = .043), contractile strain (OR 2.828, 95% CI 1.158-6.903, p = .022), and conduit strain (OR 2.614, 95% CI 1.003-6.815, p = .049) were significantly associated with subsequent detection of AF. CONCLUSION: Reduced LA strain is associated with ESUS occurrence and AF detection in ESUS patients. Therefore, quantification of LA strain in ESUS patients may improve risk stratification and guide secondary prevention strategies.

Insight into different phenotypic presentations of heart failure with preserved ejection fraction.

Heart failure (HF) with preserved ejection fraction (HFpEF) accounts for half of all HF diagnoses, and its prevalence is increasing at an alarming rate. Lately, it has been recognized as a clinical syndrome due to diverse underlying etiology and pathophysiological mechanisms. The classic echocardiographic features of HFpEF have been well described as preserved ejection fraction (≥50%), left ventricular hypertrophy, and left atrial enlargement. However, echocardiography can play a key role in identifying the principal underlying mechanism responsible for HFpEF in the individual patient. The recognition of different phenotypic presentations of HFpEF (infiltrative, metabolic, genetic, and inflammatory) can assist the clinician in tailoring the appropriate management, and offer prognostic information. The goal of this review is to highlight several key phenotypes of HFpEF and illustrate the classic clinical scenario and echocardiographic features of each phenotype with real patient cases.

Particulate matter 2.5, metropolitan status, and heart failure outcomes in US counties: A nationwide ecologic analysis.

The relationship between particulate matter with a diameter of 2.5 micrometers or less (PM2.5) and heart failure (HF) hospitalizations and mortality in the US is unclear. Prior studies are limited to studying the effects of daily PM2.5 exposure on HF hospitalizations in specific geographic regions. Because PM2.5 can vary by geography, this study examines the effects of annual ambient PM2.5 exposure on HF hospitalizations and mortality at a county-level across the US. A cross-sectional analysis of county-level ambient PM2.5 concentration, HF hospitalizations, and HF mortality across 3135 US counties nationwide was performed, adjusting for county-level demographics, socioeconomic factors, comorbidities, and healthcare-associated behaviors. There was a moderate correlation between county PM2.5 and HF hospitalization among Medicare beneficiaries (r = 0.41) and a weak correlation between county PM2.5 and HF mortality (r = 0.08) (p-values < 0.01). After adjustment for various county level covariates, every 1 ug/m3 increase in annual PM2.5 concentration was associated with an increase of 0.51 HF Hospitalizations/1,000 Medicare Beneficiaries and 0.74 HF deaths/100,000 residents (p-values < 0.05). In addition, the relationship between PM2.5 and HF hospitalizations was similar when factoring in metropolitan status of the counties. In conclusion, increased ambient PM2.5 concentration level was associated with increased incidence of HF hospitalizations and mortality at the county level across the US. This calls for future studies exploring policies that reduce ambient particulate matter pollution and their downstream effects on potentially improving HF outcomes.

Regional Variation in the Association of Poverty and Heart Failure Mortality in the 3135 Counties of the United States.

Background There is significant geographical variation in heart failure (HF) mortality across the United States. County socioeconomic factors that influence these outcomes are unknown. We studied the association between county socioeconomic factors and HF mortality and compared it with coronary heart disease (CHD) mortality. Methods and Results This is a cross-sectional analysis of socioeconomic factors and mortality in HF and CHD across 3135 US counties from 2010 to 2015. County-level poverty, education, income, unemployment, health insurance status, and cause-specific mortality rates were collected from the Centers for Disease Control and Prevention and US Census Bureau databases. Poverty had the strongest correlation with both HF and CHD mortality, disproportionately higher for HF (r=0.48) than CHD (r=0.24). HF mortality increased by 5.2 deaths/100 000 for each percentage increase in county poverty prevalence in a frequency-weighted, demographic-adjusted, multivariate regression model. The greatest attenuation in the poverty regression coefficient (66.4%) was seen after adjustment for prevalence of diabetes mellitus and obesity. Subgroup analysis by census region showed that this relationship was the strongest in the South and weakest in the Northeast (6.1 versus 1.4 deaths/100 000 per 1% increase in county poverty in a demographics-adjusted model). Conclusions County poverty is the strongest socioeconomic factor associated with HF and CHD mortality, an association that is stronger with HF than with CHD and varied by census region. Over half of the association was explained by differences in the prevalence of diabetes mellitus and obesity across the counties. Health policies targeting improvement in these risk factors may address and possibly minimize health disparities caused by socioeconomic factors.