scholarly journals Epidemiologic Determinants of Dynamics in Heart Failure Prevalence and Mortality in Older U.S. Adults

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 164-165
Author(s):  
Bin Yu ◽  
Igor Akushevich ◽  
Arseniy Yashkin ◽  
Julia Kravchenko
Keyword(s):  
Heart Failure ◽  
Hospital Readmission ◽  
Recent Decade ◽  
Study Data ◽  
Medicare Beneficiaries ◽  
Relative Contribution ◽  
Current Trends ◽  
The Us ◽  
If Current ◽  
Epidemiologic Determinants

Abstract Recent declines in heart failure (HF) prevalence and increases in mortality among older adults in the US suggest the need for research to investigate the relative contribution of the epidemiological determinants of these two processes to their historical and current trends. Study data were derived from a 5% sample of Medicare beneficiaries, 1991-2017. Partitioning analysis was used to decompose age-adjusted prevalence and incidence-based mortality (IBM) into their constituent components. HF prevalence trend decomposition demonstrated three phases: (a) Decelerated Increasing Prevalence (1994-2006) mainly driven by decreasing incidence, overpowering increasing survival, (b) Accelerated Declining Prevalence (2007-2014) and (c) Decelerated Declining Prevalence (2015-2017), mainly driven by declining incidence, overpowering declining survival. For HF IBM four phases were identified: (a) Decelerated Increasing Mortality (1994-2001) with declining incidence and increasing survival driving deceleration, (b) Accelerated Declining Mortality (2002-2012), (c) Decelerated Declining Mortality (2013-2016), mainly driven by declining incidence, overpowering declining survival, and (d) Accelerated Increasing Mortality (2017) mainly driven by declining survival, overpowering declining incidence. Study findings suggest that the recent decade-long decline in HF prevalence and 15-year decline in HF mortality mainly reflected decreasing incidence, while the most recent increase in mortality was due to declining survival, which may be associated with the Hospital Readmission Reduction Program. If current trends of incidence and survival persist, HF prevalence and mortality are forecasted to grow, suggesting that actions to reduce HF risk factors and improve treatment and management of HF after diagnosis are warranted.

In-Vitro Modeling of Heart Failure in the Presence of a Prosthetic Heart Valve Using Particle Image Velocimetry

2011 ◽  
Author(s):  
Min Xiao ◽  
Annie Bailey ◽  
Olga Pierrakos
Keyword(s):  
Heart Failure ◽  
Cardiovascular Disease ◽  
American Heart ◽  
Prosthetic Heart Valve ◽  
Indirect Costs ◽  
Heart Association ◽  
Current Trends ◽  
Image Velocimetry ◽  
The Us

It is well-known that cardiovascular disease, affecting millions of people, is the number one killer in the US and worldwide. Current trends indicate that cardiovascular disease (CVD) will claim approximately 20 million victims in 2020 as the leading cause of death worldwide and will be responsible for over a billion deaths between 2000 and 2050 [1]. According to the American Heart Association, one in three American adults have one or more types of heart disease. Economically, the total and indirect costs due to cardiovascular diseases in 2009 were estimated at $475.3 billion. The spectrum of cardiac disease encompasses a broad range of disorders, varying from myocardial ischemia, valvular disease, diastolic dysfunction, congestive heart failure (which is projected to affect 20 million people by 2020), etc. Most of these disorders initiate and are associated to the left side of the heart, which is the workhorse and also the focus of our research herein.

Abstract TP6: Current Trends in Thrombolytic Use for Acute Ischemic Stroke

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Marquita Decker-Palmer ◽  
Ning Wu ◽  
Michele Biscossi ◽  
Sean I Savitz
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
The United States ◽  
Medicare Beneficiaries ◽  
Adherence To Guidelines ◽  
Current Trends ◽  
The Us ◽  
Hierarchical Categorization ◽  
Insured Patients ◽  
Rural Patients

Introduction: Alteplase is approved in the United States for acute ischemic stroke (AIS). Guidelines recommend IV weight-based alteplase dosing for AIS treatment with or without endovascular therapy (EVT). With increasing use of EVT, current thrombolytic use and dosing practices in AIS are poorly understood. This study assesses current and historical trends in thrombolytic use. Methods: All patients who received alteplase from 2007 to 2017 in the US Premier Hospital database were included. Hierarchical categorization identified indications by the presence of primary or secondary diagnoses including AIS > pulmonary embolism (PE) > myocardial infarction (MI) > or other. Patients undergoing EVT were subcategorized. Dosing was estimated by vial size. Demographics were analyzed descriptively. Results: Of 78,216 patients included, 33,530 (43%) had AIS, 7442 (9%) PE, 1696 (2%) MI, and 35,548 (45%) off-label indications. Patients with AIS had mean age of 68, and 2409 (7%) received alteplase + EVT. Of those with alteplase + EVT, 1428 (59%) were solely Medicare beneficiaries and 600 (25%) had solely commercial insurance vs 19,572 (63%) Medicare and 6585 (21%) commercially insured patients receiving alteplase alone. Only 37 patients (2%) with AIS receiving alteplase + EVT had care at rural hospitals, whereas 2946 rural patients with AIS (9%) received alteplase alone. Before 2011, EVT was associated with use of 50-mg vials of alteplase to treat AIS (Fig). After 2011, more patients with AIS receiving EVT had 100-mg vials of alteplase, consistent with dosing closer to the 90-mg maximum. Conclusions: AIS is the most common indication for current alteplase use. Since 2011, weight-based dosing has been widely adopted for treatment with and without EVT, which represents adherence to guidelines. Differences in payer mix and rurality among patients receiving alteplase + EVT may represent opportunities to improve access to care.

Discharge Hospice Referral Is Associated with Lower 30-Day All-Cause Hospital Readmission in Medicare Beneficiaries with Heart Failure

2013 ◽  
Vol 19 (8) ◽  
pp. S33
Author(s):  
Chakradhari Inampudi ◽  
Sridivya Parvataneni ◽  
Kanan Patel ◽  
Gregg C. Fonarow ◽  
Inmaculada B. Aban ◽  
...  
Keyword(s):  
Heart Failure ◽  
Hospital Readmission ◽  
Medicare Beneficiaries ◽  
Hospice Referral

Obesity: Stigma, Trends, and Interventions

2018 ◽  
Author(s):  
Keyword(s):  
Obesity Stigma ◽  
Current Trends ◽  
The Us ◽  
If Current

Obesity is one of the defining health challenges of our generation. Studies project that, if current trends continue, more than 50% of the US population will have obesity within the next 20 years. https://shop.aap.org/pediatric-collections-obesity-stigma-trends-and-interventions-paperback/

Acute heart failure: predicting early in-hospital outcomes

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
S Paula ◽  
I Almeida ◽  
H Santos ◽  
H Miranda ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Acute Heart Failure ◽  
Roc Curve ◽  
Real Life ◽  
Study Data ◽  
Curve Analysis ◽  
Older Age ◽  
Invasive Ventilation ◽  
Roc Curve Analysis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Patients (P) with acute heart failure (AHF) are a heterogeneous population. Risk stratification at admission may help predict in-hospital complications and needs. The Get With The Guidelines Heart Failure score (GWTG-HF) predicts in-hospital mortality (M) of P admitted with AHF. ACTION ICU score is validated to estimate the risk of complications requiring ICU care in non-ST elevation acute coronary syndromes. Objective To validate ACTION-ICU score in AHF and to compare ACTION-ICU to GWTG-HF as predictors of in-hospital M (IHM), early M [1-month mortality (1mM)] and 1-month readmission (1mRA), using real-life data. Methods Based on a single-center retrospective study, data collected from P admitted in the Cardiology department with AHF between 2010 and 2017. P without data on previous cardiovascular history or uncompleted clinical data were excluded. Statistical analysis used chi-square, non-parametric tests, logistic regression analysis and ROC curve analysis. Results Among the 300 P admitted with AHF included, mean age was 67.4 ± 12.6 years old and 72.7% were male. Systolic blood pressure (SBP) was 131.2 ± 37.0mmHg, glomerular filtration rate (GFR) was 57.1 ± 23.5ml/min. 35.3% were admitted in Killip-Kimball class (KKC) 4. ACTION-ICU score was 10.4 ± 2.3 and GWTG-HF was 41.7 ± 9.6. Inotropes’ usage was necessary in 32.7% of the P, 11.3% of the P needed non-invasive ventilation (NIV), 8% needed invasive ventilation (IV). IHM rate was 5% and 1mM was 8%. 6.3% of the P were readmitted 1 month after discharge. Older age (p < 0.001), lower SBP (p = 0,035) and need of inotropes (p < 0.001) were predictors of IHM in our population. As expected, patients presenting in KKC 4 had higher IHM (OR 8.13, p < 0.001). Older age (OR 1.06, p = 0.002, CI 1.02-1.10), lower SBP (OR 1.01, p = 0.05, CI 1.00-1.02) and lower left ventricle ejection fraction (LVEF) (OR 1.06, p < 0.001, CI 1.03-1.09) were predictors of need of NIV. None of the variables were predictive of IV. LVEF (OR 0.924, p < 0.001, CI 0.899-0.949), lower SBP (OR 0.80, p < 0.001, CI 0.971-0.988), higher urea (OR 1.01, p < 0.001, CI 1.005-1.018) and lower sodium (OR 0.92, p = 0.002, CI 0.873-0.971) were predictors of inotropes’ usage. Logistic regression showed that GWTG-HF predicted IHM (OR 1.12, p < 0.001, CI 1.05-1.19), 1mM (OR 1.10, p = 1.10, CI 1.04-1.16) and inotropes’s usage (OR 1.06, p < 0.001, CI 1.03-1.10), however it was not predictive of 1mRA, need of IV or NIV. Similarly, ACTION-ICU predicted IHM (OR 1.51, p = 0.02, CI 1.158-1.977), 1mM (OR 1.45, p = 0.002, CI 1.15-1.81) and inotropes’ usage (OR 1.22, p = 0.002, CI 1.08-1.39), but not 1mRA, the need of IV or NIV. ROC curve analysis revealed that GWTG-HF score performed better than ACTION-ICU regarding IHM (AUC 0.774, CI 0.46-0-90 vs AUC 0.731, CI 0.59-0.88) and 1mM (AUC 0.727, CI 0.60-0.85 vs AUC 0.707, CI 0.58-0.84). Conclusion In our population, both scores were able to predict IHM, 1mM and inotropes’s usage.

Heart failure in Acute Coronary Syndrome: predictors and prognosis

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Santos ◽  
H Santos ◽  
I Almeida ◽  
H Miranda ◽  
C Sa ◽  
...  
Keyword(s):  
Heart Failure ◽  
Artery Bypass ◽  
Study Data ◽  
Multivessel Disease ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Coronary Syndrome ◽  
Funding Sources ◽  
Previous Stroke ◽  
St Elevation

Abstract Funding Acknowledgements Type of funding sources: None. OnBehalf on behalf of the Investigators of " Portuguese Registry of ACS " Introduction Heart failure (HF) is a frequent complication of acute coronary syndromes (ACS). Therefore, it is important to access its impact on prognosis and identify patients (pts) with higher risk of HF. Objective To evaluate predictors and prognosis of HF in the setting of ACS. Methods Based on a multicenter retrospective study, data collected from admissions between 1/10/2010 and 4/09/2019. Pts without data on cardiovascular history or uncompleted clinical data were excluded. Pts were divided in 2 groups (G): GA – pts without HF; GB - pts with HF during hospitalization. Results HF occurred in 4003 (15.6%) out of 25718 pts with ACS. GB was older (74 ± 12 vs 65 ± 13, p < 0.001), had more females (36.3% vs 26.2%, p < 0.001), had higher rates of arterial hypertension (78.4% vs 69.3%, p < 0.001), dyslipidaemia (64.4% vs 61.1%. p < 0.001), previous ACS (25.6% vs 19.7%, p < 0.001,), previous HF (16.4% vs 4.1%, p < 0.001), previous stroke (11.9% vs 6.4%, p < 0.001), chronic kidney disease (CKD) (17.1% vs 5.5%, p < 0.001), chronic obstructive pulmonary disease (COPD) (7.8% vs 3.8%, p < 0.001) and longer times from first symptoms to admission (268min vs 238min, p < 0.001). GA had higher rate of smokers (28.4% vs 16.2%, p < 0.001) and higher rate of non-ST-elevation myocardial infarction (MI) (46.5% vs 43.0%, p < 0.001). GB had higher rates of ST-elevation MI (STEMI) (49.2% vs 41.1%, p < 0.001), namely anterior STEMI (58.1% vs 44.9%, p < 0.001). GB had lower blood pressure (130 ± 32 vs 140 ± 28, p < 0.001), higher heart rate (86 ± 23 vs 76 ± 18, p < 0.001), Killip-Kimball class (KKC) ≥2 (63.2% vs 6.7%, p < 0.001), atrial fibrillation (AF) (15.4% vs 5.7%, p < 0.001), left bundle branch block (7.5% vs 3.1%, p < 0.001) and were previously treated with diuretics (39.1% vs 22.1%, p < 0.001), amiodarone (2.2% vs 1.4%, p < 0.001) and digoxin (2.8% vs 0.7%, p < 0.001). GB had higher rates of multivessel disease (66.0% vs 49.5%, p < 0.001) and planned coronary artery bypass grafting (7.3% vs 6.0%, p < 0.001), reduced left ventricle function (72.3% vs 33.4%, p < 0.001) and needed more frequently mechanical ventilation (8.2% vs 0.9%, p < 0.001), non-invasive ventilation (8.7% vs 0.5%, p < 0.001) and provisory pacemaker (4.5% vs 1.0%, p < 0.001). Logistic regression confirmed females (p < 0.001, OR 1.42, CI 1.29-1.58), diabetes (p < 0.001, OR 1.43, CI 1.30-1.58), previous ACS (p < 0.001, OR 1.27, CI 1.10-1.47), previous stroke (p < 0.001, OR 1.35, CI 1.16-1.57), CKD (p < 0.001, OR 1.76, CI 1.50-2.05), COPD (p < 0.001, OR 2.15, CI 1.82-2.54), previous usage of amiodarone (p = 0.041, OR 1.35, CI 1.01-1.81) and digoxin (p < 0.001, OR 2.30, CI 1.70-3.16), and multivessel disease (p < 0.001, OR 1.64, CI 1.67-2.32) were predictors of HF in the setting of ACS. Event-free survival was higher in GA than GB (79.5% vs 58.1%, OR 2.3, p < 0.001, CI 2.09-2.56). Conclusion As expected, HF in the setting of ACS is associated with poorer prognosis. Several features may help predict the HF occurrence during hospitalizations, allowing an earlier treatment.

scholarly journals THU0442 GOUT AND HEART FAILURE IN THE US: A NATIONAL PERSPECTIVE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 458.2-458
Author(s):  
G. Singh ◽  
M. Sehgal ◽  
A. Mithal
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Confidence Intervals ◽  
Burden Of Illness ◽  
Average Length ◽  
Private Insurance ◽  
Significant Risk ◽  
Case Fatality ◽  
The United States ◽  
The Us

Background:Heart failure (HF) is the eighth leading cause of death in the US, with a 38% increase in the number of deaths due to HF from 2011 to 2017 (1). Gout and hyperuricemia have previously been recognized as significant risk factors for heart failure (2), but there is little nationwide data on the clinical and economic consequences of these comorbidities.Objectives:To study heart failure hospitalizations in patients with gout in the United States (US) and estimate their clinical and economic impact.Methods:The Nationwide Inpatient Sample (NIS) is a stratified random sample of all US community hospitals. It is the only US national hospital database with information on all patients, regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. We examined all inpatient hospitalizations in the NIS in 2017, the most recent year of available data, with a primary or secondary diagnosis of gout and heart failure. Over 69,800 ICD 10 diagnoses were collapsed into a smaller number of clinically meaningful categories, consistent with the CDC Clinical Classification Software.Results:There were 35.8 million all-cause hospitalizations in patients in the US in 2017. Of these, 351,735 hospitalizations occurred for acute and/or chronic heart failure in patients with gout. These patients had a mean age of 73.3 years (95% confidence intervals 73.1 – 73.5 years) and were more likely to be male (63.4%). The average length of hospitalization was 6.1 days (95% confidence intervals 6.0 to 6.2 days) with a case fatality rate of 3.5% (95% confidence intervals 3.4% – 3.7%). The average cost of each hospitalization was $63,992 (95% confidence intervals $61,908 - $66,075), with a total annual national cost estimate of $22.8 billion (95% confidence intervals $21.7 billion - $24.0 billion).Conclusion:While gout and hyperuricemia have long been recognized as potential risk factors for heart failure, the aging of the US population is projected to significantly increase the burden of illness and costs of care of these comorbidities (1). This calls for an increased awareness and management of serious co-morbid conditions in patients with gout.References:[1]Sidney, S., Go, A. S., Jaffe, M. G., Solomon, M. D., Ambrosy, A. P., & Rana, J. S. (2019). Association Between Aging of the US Population and Heart Disease Mortality From 2011 to 2017. JAMA Cardiology. doi:10.1001/jamacardio.2019.4187[2]Krishnan E. Gout and the risk for incident heart failure and systolic dysfunction. BMJ Open 2012;2:e000282.doi:10.1136/bmjopen-2011-000282Disclosure of Interests: :Gurkirpal Singh Grant/research support from: Horizon Therapeutics, Maanek Sehgal: None declared, Alka Mithal: None declared

Sign in / Sign up

Export Citation Format

Share Document