View clinical trials related to Heart Failure.
Filter by:Every day, patients present to emergency department due to acute heart failure. There are many causes for decompensation. One possible cause is a lack of adherence to heart failure medication (prognosis-improving medications and diuretics). The aim of this study is to directly measure adherence in patients with acute heart failure (gold standard of adherence measurement using liquid chromatography coupled to high-resolution mass spectrometry= LC-HRMS/MS) at the emergency department. Questionnaires are used to investigate possible factors influencing adherence.
This study will test an automated, electronic health record (EHR-)embedded alert to improve prescribing of guideline-directed medical therapy for patients with heart failure and reduced ejection fraction (HFrEF). The investigators have previously tested and implemented this alert at NYU Langone Health (NYULH), and will now test and implement this alert across three other health systems.
The objective of this clinical trial is to investigate the effect of weight reduction through a diet management application and an intelligent weight scale on a composite cardiovascular endpoint in obese patients with heart failure. The main questions are: Does the use of a diet management APP and intelligent weight scale reduce 1-year all-cause mortality, heart failure hospitalization, and first heart failure hospital stay? Does the use of a diet management APP and intelligent weight scale improve the outcomes of assessment of heart failure frailty and quality of life for heart failure? Researchers will compare using the fully functional diet management app and intelligent weight scale to using the limitedly functional app and intelligent weight scale to see if the app works to improve heart failure conditions. Participants will: Use the diet management app at every meal and the intelligent weight scale every day for 12 months, and visit the clinic at 12 months for checkups.
To evaluate the effect of blood glucose level at admission and glucose variability during ICU admission and their effect on in-hospital morbidity and mortality in patients admitted with acute decompensated heart failure
The pharmacokinetics (PK) and pharmacodynamics (PD) of bisoprolol and sodium-glucose co-transporter-2 inhibitors (SGLT2i, dapagliflozin and empagliflozin) in patients with acutely decompensated heart failure (ADHF), compared to the recompensated state, is unknown. If not in cardiogenic shock (no need of vasopressor (catechoalmines) therapy or other inotropic support), established oral betablocker therapy should de continued. Whether this holds true for SGLT2i in ADHF is less clear but current evidence suggest safety and potentially beneficial effects in doing so. To the best of our knowledge, no data regarding PK/PD are available for the most widely used beta blocker bisoprolol and the newly approved/in Germany available SGLT2i Dapagliflozin and Empagliflozin. This study shall provide first evidence on the PK/PD-profile of p.o. bisoprolol and SGLT2i (dapaglifozin or empagliflozin) regarding acute (hemodynamic) effects and safety as well as to provide data on dose recommendations eventually in patients with ADHF.
This study will recruit 100 patients from a post-discharge medicine clinic to test if the addition of a pharmacist to manage heart failure medications can increase appropriate use of these medications. Participants will be randomly assigned to usual care alone or with the addition of a pharmacist to help manage medications. They will be followed for 3 months by telephone/electronically-administered questionnaires, and 12 months using administrative health records. Outcome data will include information from patients on quality of life, treatment burden, medication adherence, as well as information from their medical record on heart failure events.
Development of pacing induced cardiomyopathy (PICM) is correlated to a high morbidity as signified by an increase in heart failure admissions and mortality. At present a lack of data leads to a failure to identify patients who are at risk of PICM and would benefit from pre-selection to physiological pacing. In the light of the foregoing, there is an urgent need for novel non-invasive detection techniques which would aid risk stratification, offer a better understanding of the prevalence and incidence of PICM in individuals with pacing devices and the contribution of additional risk factors.
Around 26 million people suffer from heart failure (HF) globally, and the prevalence is increasing with an increasing longevity, prevalence of risk factors, and improved survival in patients with cardiovascular diseases In Egypt, HF is the primary cause of hospitalization among patients aged > 65 years . Hospitalization for HF is associated with a high mortality and rate of re-hospitalization . Around 75% patients with HF have ≥ 1 comorbidity, and these comorbidities make overall clinical outcomes worse . In a recent meta-analysis, patients with diabetes mellitus (DM) were suggested to have a two-fold increase in the risk of HF . DM is present in ~ 35% patients hospitalized with acute HF . Multiple factors such as ischemia, hypertension, and extracellular fluid volume expansion are involved in the pathogenesis of HF in DM.
The study seeks to explore the implementation characteristics (acceptability, appropriateness, feasibility, adoption, fidelity, penetration, implementation cost and sustainability) of systematic echocardiography in nursing homes and its impact on rates of heart failure flare-up and unscheduled hospitalization at 12 months among included nursing homes.
The Korotkoff Sounds（KS）, which have been in use for over a century, are widely regarded as the gold standard for measuring blood pressure. Furthermore, their potential extends beyond diagnosis and treatment of cardiovascular disease; however, research on the KS remains limited. Given the increasing incidence of heart failure (HF), there is a pressing need for a rapid and convenient prehospital screening method. In this study, we propose employing deep learning (DL) techniques to explore the feasibility of utilizing KS methodology in predicting functional changes in cardiac ejection fraction (LVEF) as an indicator of cardiac dysfunction.