Clinical Characteristics and Predictors of Adverse Outcomes in HFpEF

Heart Failure With Preserved Ejection Fraction Clinical Trial

Official title:

Exploring Clinical Characteristics and Predictors of Adverse Outcomes in Patients Diagnosed With Heart Failure With Preserve Ejection Fraction (HFpEF)

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06228807
• Other study ID #: HFpEF-CCPO
• Status: Completed
• Start date: October 5, 2015
• Est. completion date: July 20, 2023

Study information

• Verified date: February 2024
• Source: Shanghai 10th People's Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Heart failure with preserved ejection fraction (HFpEF) is a prevalent form of heart failure characterized by impaired left ventricle pressures and diastolic dysfunction. Despite its increasing prevalence, effective treatment options for HFpEF remain limited, probably due to its heterogenous underlying pathological etiology involving chronic systemic inflammation and metabolic dysregulation. Identifying new predictors of poor prognosis is crucial for risk stratification and tailored management.

Description:

The prognosis for HFpEF is concerning, marked by significant mortality and frequent hospital readmissions. The exact mechanisms underlying HFpEF remain unresolved. The clinical syndrome arises from intricate interactions among various risk factors, leading to organ dysfunction and clinical symptoms. Common co-morbidities, including atrial fibrillation, diabetes, chronic kidney disease, and obesity, may influence HFpEF pathophysiology. Recent discussions suggest an inflammatory-metabolic phenotype in HFpEF, characterized by heightened inflammatory biomarkers, insulin resistance, hyperglycemia, hyperlipidemia, microvascular endothelial and vascular dysfunction, atherosclerosis, consequently leading to significant myocardial damage. However, there is still a lack of clinical risk predictors associated with metabolic phenotype within HFpEF.

The aim of this study is to investigate the impact of parameters reflecting the metabolic phenotype in HFpEF and establish their correlation with clinical outcomes. The objective is to identify novel predictors for adverse prognosis in HFpEF, potentially serving as targets for drug therapy. This research aims to pave the way for targeted drug interventions in cardiometabolic diseases, offering new avenues for therapeutic approaches.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 443
• Est. completion date: July 20, 2023
• Est. primary completion date: July 20, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients diagnosed with HFpEF; Diagnostic criteria for HFpEF

• Presence of heart failure signs/symptoms symptoms

• Preserved left ventricular ejection fraction (EF) =50%

• BNP=35 pg/mL and/or NTproBNP=125 pg/mL;

• At least one additional criteria: presence of cardiac functional and structural abnormalities or diastolic dysfunction.

Exclusion Criteria:

• Age >18 years old;

• Patients with serious hepatic and renal failure,

• Non-cardiac causes of dyspnea, such as, asthma

• Primary pulmonary hypertension.

• Severe valvular heart disease.

• Pericardial disease.

• Coronary artery disease (stenosis>50).

• History of myocardial infarction.

Related Conditions & MeSH terms

• Heart Failure
• Heart Failure With Preserved Ejection Fraction

Intervention

Combination Product:
• The study involved a comparison of clinical characteristics, medical history, laboratory biomarkers, echocardiographic measurements, angiographic findings, and clinical outcomes between two groups.
Differences in clinical characteristics, history, laboratory biomarkers, echocardiographic measurements, angiographic findings, and clinical outcomes were assessed between the two groups to identify potential risk factors. Subsequently, the incidence of clinical outcomes and mortality was compared. Univariate logistic regression and multivariate logistic regression analyses, adjusted for significant risk factors, were conducted to identify independent predictive factors. Finally, a Receiver Operating Characteristic (ROC) curve was generated, and the area under the curve was evaluated to assess the predictive accuracy of the regression model.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Ya-Wei Xu

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Composite Events	The composite events include a composite of all-cause mortality (death due to any cause), cardiovascular mortality (death due to cardiac cause), and heart failure rehospitalization.	From the time of hospital discharge until the event of specific outcome, death, or loss to follow up, maximum 10 years.