the Affiliated Hospital of Xuzhou Medical University
Xuzhou, Jiangsu, 221000, China
NCT Number: NCT06916611
Using cardiac magnetic resonance imaging technology, the ejection fraction of heart failure (HFpEF) and different subtypes of cardiac magnetic resonance characteristics, and combined with the clinical characteristics and prognosis of the patient, explore the value of cardiac magnetic resonance in disease diagnosis, classification, treatment and prognosis, and provide new ideas for clinical practice.
This study is active but is not currently recruiting participants.
Notify Me50 year and older
All sexes
Observational
Xuzhou, Jiangsu, 221000, China
To identify distinct cardiac magnetic resonance (CMR) features associated with HFpEF and its comorbidity subtypes.
To evaluate prognostic differences (mortality, heart failure readmission, healthcare costs) across HFpEF subtypes.
Baseline Data:
Clinical Parameters:
Demographics (age, gender, BMI). Comorbidities (hypertension, diabetes, renal function). Biochemical markers (BNP/NT-proBNP, HbA1c, lipid profile, renal function). Medications (ACE inhibitors, beta-blockers, diuretics).
Imaging Data:
Echocardiography: Left ventricular ejection fraction (LVEF ≥50%). CMR: Ventricular volumes, mass, strain analysis, T1 mapping, and late gadolinium enhancement (LGE).
Follow-Up Protocol:
Frequency: Every 6 months post-discharge.
Endpoints:
Primary: Cardiovascular mortality, HF-related readmission. Secondary: Changes in CMR parameters, medication adjustments, healthcare utilization.
Comorbidity Subgroups:
HFpEF + Hypertension. HFpEF + Type 2 Diabetes. HFpEF + Renal Insufficiency. HFpEF + Obesity. Control Group: HFpEF patients without the above comorbidities.
Imaging Sequences:
Cine imaging for ventricular function. T1 mapping for myocardial fibrosis assessment. LGE for scar detection. Post-Processing: Analysis of myocardial strain, extracellular volume (ECV), and perfusion reserve.
Methods:
Regression analysis for associations between CMR features and clinical outcomes.
Survival analysis (Kaplan-Meier, Cox proportional hazards models). Subgroup comparisons using ANOVA or non-parametric tests. Software: SPSS 20.0 (significance threshold: p <0.05).
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
This study included patients who were diagnosed with HFpEF and had completed CMR.
Time frame: follow - up every 6 months; up to 24 - months
Time frame: follow - up every 6 months; up to 24 - months
Time frame: Baseline
Measure body temperature every morning on an empty stomach using an electronic thermometer, with the unit being degrees Celsius (℃)
Time frame: Baseline
Draw blood on an empty stomach in the morning and detect fasting blood glucose using a biochemical analyzer, with the unit being mmol/L; measure 2 - hour post - prandial blood glucose using a portable blood glucose meter, with the unit being mmol/L.
Time frame: Baseline
Detect BNP levels using an electrochemiluminescence immunoassay analyzer, with the unit being picograms per milliliter (pg/mL).
Time frame: Baseline
Detect glycated hemoglobin using high - performance liquid chromatography, and the result is expressed as a percentage (%).
Time frame: Baseline
Measure blood pressure once in the morning and once in the evening every day using a blood pressure monitor, with the unit being millimeters of mercury (mmHg)
Time frame: Baseline
Continuously monitor the heart rate using an electrocardiogram monitor, with the unit being beats per minute (bpm).
The Affiliated Hospital of Xuzhou Medical University
Other
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