Letter regarding the article 'Modification of the association between age and mortality in heart failure by frailty status'.

Introduction: Heart failure (HF) mortality among young adults has been increasing since 2012. It is unknown if insurance status contributes to these trends. Hypothesis: Uninsured status contributes to rising HF mortality rates in young adults across US states over the last decade. Methods: State-level age-adjusted mortality rates for HF and all cardiovascular disease (CVD) in adults aged 25-64 from 2010-2020 were obtained from the National Center for Health Statistics. All deaths across fifty states and the District of Columbia (DC) were included, averaging 3.3 million individuals per year. Annual estimates of the percentage of uninsured adults ages 25-64 (% uninsured) from the US Census were merged with mortality data by state and year. We examined cross-sectional associations of % uninsured with HF and CVD mortality in each year. We further examined the longitudinal association of % uninsured and mortality outcomes across years using linear regression models with generalized estimating equations to account for the longitudinal design. Results: Across n=51 US states and DC, the age-adjusted HF mortality rate increased from a mean of 6.48 in 2010 to 7.54 in 2020 (p<0.001), while the age-adjusted all-cause CVD mortality rate increased from 78.71 in 2010 to 88.91 in 2020 (p<0.001). Over the same period, the % uninsured decreased from 19% in 2010 to 11% in 2020 (p<0.001). In 3 of 10 years, there was a significant association between % uninsured and HF mortality (β ranging from 15.94 to 25.01 deaths per 100,000 in 2010, 2015, and 2020; p<0.01,) and CVD mortality (β ranging from 166.48 to 258.48 deaths per 100,000 in 2010, 2015, and 2020; p<0.01). The longitudinal association between % uninsured and HF mortality was not significant (β=1.81 deaths per 100,000; p=0.44). Conclusion: Uninsured status is associated with HF and overall CVD mortality across US states in certain years, however, there is no association between uninsured status and HF and CVD mortality rates from 2010-2020.

Incidence and mortality of heart failure: A community-based study

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.

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.

Background: Regional poverty has been associated with worse outcomes in stroke, myocardial infarction and overall cardiovascular mortality. There is paucity of data regarding regional socioeconomic (SE) factors and outcomes in heart failure (HF). We studied the association between SE factors and HF mortality and compared it with CHD mortality at a regional level. Methods: This is a cross sectional analysis of all US counties (n=3141) from 2010-2015. County level data for SE factors, risk factor prevalence and demographics were collected from CDC and Census Bureau. Counties (n=6) with insufficient mortality data were excluded. In 2014, poverty threshold was $11,670 for 1-person household. Mortality data was derived from death certificates, published by National Center for Health Statistics. Random sampling (n=50) was used to compare the strength of correlation for the strongest SE factor (poverty, employment, education and uninsured %) with HF and CHD mortality. Population weighted multivariate linear regression analyses were used to relate the strongest SE factor and HF mortality, adjusted for risk factor prevalence and demographics. Results: We studied 3,135 counties with median(IQR) of poverty (15.8% (12.4% - 20.1%)), male (49.5% (48.9% -50.4%)), white (84.6% (63.4% -93.1%), ≥ 65 yrs. (17.2% (14.7%-19.9%)), number of HF hospitalizations/1000 Medicare beneficiaries (13.9 (9.2-17.6)) and HF deaths/100,000 (189.5 (164.7-219.1)). Of all SE factors, poverty% has the strongest association with HF mortality, disproportionately higher for HF than CHD (p=0.000). Our final model explains 61.7% of variation in regional HF mortality. Poverty remains an independent risk factor despite adjusting for demographics, other SE factors and risk factor prevalence. Conclusion: County poverty disproportionately effects HF mortality as compared to CHD mortality, independent of demographics and risk factor prevalence. Future studies examining additional mechanism of this association are needed to reform health policy.

An epidemic increase in heart failure (HF) mortality, hospitalization, and prevalence rates has been observed among older persons associated with increased an incidence and improved survival in recent years, in spite of a decrease in coronary artery and cerebrovascular disease mortality. Importantly, increases in HF mortality and morbidity rates were confi ned to the population over 65 years of age in the Framingham study. In contrast to middle-aged patients with HF, factors other than left ventricular (LV) systolic dysfunction contribute to HF in older patients. Epidemiological studies have established that 40 to 80 percent of older patients with heart failure, despite preserved ejection fraction without valve disease, are attributed to LV diastolic dysfunction. Keyword: geriatrics, elderly person, diastolic heart function, heart failure Introduction increase in heart failure (HF) mortality, hospitalization, and prevalence rates has been observed among older persons associated with an increased incidence and improved survival in recent years, in spite of a decrease in coronary artery and cerebrovascular disease mortality. 1 Importantly, increases in HF mortality and morbidity rates were confi ned to the population over 65 years of age in the Framingham study. 2 In contrast to middle-aged patients with HF, factors other than left ventricular (LV) systolic dysfunction contribute to HF in older patients. Epidemiological studies have established that 40 to 80 percent of older patients with heart failure, despite preserved ejection fraction without valve disease, are attributed to LV diastolic dysfunction. That is defi ned as diastolic heart failure (DHF). 3 Studies examining prevalence of diastolic heart failure in hospitalized patients or in patients undergo- ing outpatient diagnostic screening and prospective community based studies have shown that in patients greater than 70 years old, the prevalence of diastolic heart failure approaches 50%. 4-6 Although HF patients with preserved systolic function have a slightly better prognosis than HF patients with abnor- mal systolic function, there is a fourfold higher mortality risk compared with subjects free of HF. 7

Heart failure (HF) disproportionately affects older patients, yet evidence guiding management in the oldest and frailest patients remains limited. We evaluated the influence of age and frailty on mortality and use of guideline-directed medical therapy (GDMT). Using nationwide Danish registry data, we identified all patients with new-onset HF (2013-2022), stratified by age (<65, 65-79, ≥80 years) and frailty status according to the Hospital Frailty Risk Score (low, intermediate, high). We assessed all-cause and cardiovascular mortality two years after HF diagnosis and evaluated GDMT use across age and frailty groups using logistic regression. Among 79,193 patients with HF, 24% were aged <65 years, 41% 65-79, and 35% ≥80. Frailty significantly modified the association between age and mortality (interaction p<0.001). Patients with high frailty reached comparable two-year mortality risks at younger ages than patients with lower frailty. The following had similar two-year mortality: low-frailty patients aged 80 years (22.1%, 95% CI, 21.4-22.8), intermediate-frailty patients aged 70 years (22.8%, 21.9-23.8), and high-frailty patients aged 47 (22.8%, 18.0-28.4). Similar results were seen for cardiovascular mortality. GDMT use was lower in high frailty than low frailty patients across all age groups. Moreover, within each frailty group, patients aged ≥80 years had lower odds of receiving GDMT compared to those aged <65 (reference). Frailty alters mortality risk in HF beyond chronological age, resulting in prognostic heterogeneity regardless of age. Our findings support the need for both frailty-informed and age-based management strategies in HF across all ages.

BackgroundAir pollution has been linked to the development and exacerbation of various health problems, including cardiovascular diseases such as heart failure. This project sought to spatially map the morbidity and mortality caused by heart failure within the county of Warwickshire, UK, to characterise and quantify any influence of air pollution on these risks. MethodsData on air pollution, hospital admission for heart failure, and mortality within 105 Warwickshire county wards were collected from 2005 to 2013. Air pollution data included mono-nitrogen oxide (NOx), sulphur dioxide (SO2), particulate matter (PM), and benzene, which could then be united into a combined index. We used Bayesian geo-additive mixed models to map the spatial distribution of air pollution and heart failure data at the county level, accounting for county risk factors. FindingsDuring 2005–13 in Warwickshire, heart failure led to 5045 hospital admissions and 479 deaths. In multivariate analyses, presence of NOx, benzene, and index of multiple deprivation (IMD) score were consistently associated with risk of heart failure morbidity (posterior mean PM 3·35, 95% credible region 1·89–4·99 vs 31·9, 8·36–55·85 vs 0·02, 0·01–0·03). PM was negatively associated with the risk of heart failure morbidity (−12·93, −20·41 to −6·54) but no association with SO2 was seen. Risk of heart failure mortality was higher in wards with a high NOx (4·30, 1·68–7·37) and in wards with more inhabitants older than 50 years (1·60, 0·47–2·92). PM was negatively associated with heart failure mortality (−14·69, −23·46 to −6·50). SO2, benzene, and IMD score were not associated with heart failure mortality. There was a striking variation in heart failure morbidity and mortality risk across wards, the highest risk being in the regions around Nuneaton and Bedworth (appendix). InterpretationThis study showed distinct spatial patterns in heart failure morbidity and mortality in Warwickshire, suggesting a potential role of air pollution beyond individual-level risk factors. Environmental factors should therefore be taken into account when considering the wider determinants of public health and the effect that changes in air pollution might have on the health of a population. FundingThis paper presents independent research supported by the National Institute for Health Research (NIHR) Collaborations for Leadership in Applied Health Research and Care West Midlands.

Heart failure (HF) is a growing public health problem in the United States. Nearly 5 million Americans suffer from HF, and an estimated 550 000 new cases of HF are diagnosed each year.1 HF is the No. 1 discharge diagnosis in patients ≥65 years of age and results in a substantial burden on healthcare expenditures. It is estimated that in 2001, more than $24 billion was spent as direct cost for the care of patients with HF.1 Furthermore, HF is associated with a significant increase in morbidity and mortality. See p 1764 Although considerable progress has been made in our approach to the pharmacological management of patients with HF, most patients remain at increased risk of cardiac death. To further improve outcomes in patients with HF, newer therapeutic modalities, including devices such as biventricular pacemaker, automatic internal cardioverter-defibrillators (AICDs), and left ventricular assist devices, have been increasingly utilized. Several recent randomized controlled trials have shown that such devices can indeed further improve the outcome in patients with HF.2–4 However, these devices are expensive, and their widespread or injudicious application in unselected patients with HF is likely to have a substantial impact on healthcare expenditures. On the other hand, appropriate use of device therapy in properly selected patients (who are at high risk of mortality) is essential to improve clinical outcome. Thus, there is a need to develop a strategy to accurately identify those patients with HF who are at increased risk of mortality. The paper by Vrtovec and associates5 in the present issue of Circulation provides such a strategy by showing that routinely available diagnostic tests, such as measurement of QT interval on 12-lead ECG and measurement of B-type natriuretic peptide (BNP), can indeed identify the HF patients who are at increased risk of overall mortality, …

To determinate the prognostic value of etiology in patients with chronic systolic heart failure (CSHF). Data of in-hospital patients with CSHF were investigated between 2000 and 2010 from 12 hospitals in Hubei province. All patients were followed up through telephone calls. Univariate and multivariate Cox proportional hazards analyses were then used to explore the differences in the all-cause mortality, heart failure (HF) mortality and sudden cardiac death (SCD) among patients caused by different etiologies. Kaplan-Meier curve were then constructed and Univariate and multivariate Cox regression analyses were used to select demographic and clinical variables in predicting the all-cause mortality, HF mortality and SCD in CSHF patients. Multivariate logistic models and ROC curve were developed with or without the confirmed etiology to assess the incremental additive information related to different etiologies. (1) Over the median 3 (2 - 4) years follow-up program, 6453 (38.69%) patients died, including 5505 (33.00%) due to HF prognosis and 717 (4.30%) died of SCD. All-cause mortality rates accounted for 34.50%, 54.30%, 41.48% and 15.76%, with HF mortality rates as 30.11%, 44.95%, 36.25% and 13.10%. SCDs accounted 8.46%, 8.45%, 9.84% and 1.05% in patients with CHD, DCM, HHD and RHD, respectively. (2) Compared with RHD patients, the adjusted HRs for all-cause mortality were 1.554 (1.240 to 1.947; P < 0.001), 1.405 (1.119 to 1.764; P = 0.003) and 1.315 (1.147 to 1.467; P = 0.005) while the adjusted HRs and 95%CIs for HF mortality were 1.458 (1.213 - 1.751; P < 0.001), 1.763 (1.448 - 2.147; P < 0.001) and 1.281 (1.067 - 1.537; P = 0.008), in patients with CHD, DCM and HHD, respectively. There were no significant differences in CHD (HR 3.345; 95%CI, 1.291 to 8.666; P = 0.013) or HHD (HR 2.062; 95%CI, 0.794 to 5.352; P = 0.137), while only DCM (HR 4.764; 95%CI, 1.799 to 12.618; P = 0.002) remained significant in SCD despite of the multivariate adjustment. (3) Etiology increased the sensitivity and specificity of predicting models for all-cause mortality (AUC 0.839, 95%CI, 0.832 to 0.845 vs. 0.776, 95%CI, 0.768 to 0.784) and HF mortality (AUC 0.814, 95%CI, 0.806 to 0.822 vs. 0.796, 95%CI, 0.788 to 0.804) but not with SCD (AUC 0.777, 95%CI, 0.749 to 0.809 vs. 0.747, 95%CI, 0.727 to 0.766). CSHF due to CHD, DCM and HHD carried a worse prognosis than that of RHD. Different etiologies provided significant incremental prognostic information beyond readily available clinical variables for all-cause mortality and HF mortality.

ObjectivesTo map using geospatial modelling techniques the morbidity and mortality caused by heart failure within Warwickshire to characterise and quantify any influence of air pollution on these risks.DesignCross-sectional.SettingWarwickshire, UK.ParticipantsData from...

Prognostic utility of pulse pressure in patients with heart failure with preserved ejection fraction: The RICA Registry

Right ventricular (RV) function may be impaired in pulmonary hypertension (PH), congenital heart disease (CHD), and coronary artery disease and in patients with left-sided heart failure (HF) or valvular heart disease. In recent years, many studies have demonstrated the prognostic value of RV function in cardiovascular disease. In the past, however, the importance of RV function has been underestimated. This perception originated from studies on open-pericardium dog models and from the observation that patients may survive without a functional subpulmonary RV (Fontan procedure). In the 1940s, studies using open-pericardium dog models showed that cauterization of the RV lateral wall did not result in a decrease in cardiac output or an increase in systemic venous pressure.1–3 As was later demonstrated, the open-pericardium model did not take into account the complex nature of ventricular interaction. In 1982, Goldstein and colleagues2 showed that RV myocardial infarction (RVMI) in a closed-chest dog model led to significant hemodynamic compromise. These findings were further supported by clinical studies demonstrating an increased risk of death, arrhythmia, and shock in patients with RVMI.4 The study of the RV is a relatively young field. In 2006, the National Heart, Lung, and Blood Institute identified RV physiology as a priority in cardiovascular research.5 The goal of this review is to present a clinical perspective on RV physiology and pathobiology. In the first article of the series, the anatomy, physiology, embryology, and assessment of the RV were discussed. In this second part, we discuss the pathophysiology, clinical importance, and management of RV failure. RV failure is a complex clinical syndrome that can result from any structural or functional cardiovascular disorder that impairs the ability of the RV to fill or to eject blood. The cardinal clinical manifestations of RV failure are (1) fluid retention, which may lead …

The prognostic impact of uric acid in acute heart failure according to coexistence of diabetes mellitus

BackgroundThere 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 ResultsThis 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).ConclusionsCounty 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.