Heart Failure, Heart Failure, Diastolic, Heart Failure, Systolic, Heart Failure With Mid-Range Ejection Fraction (HFmrEF), Heart Failure With Preserved Ejection Fraction (HFPEF), Heart Failure With Reduced Ejection Fraction (HFrEF)
Conditions
Keywords
Heart failure prognosis, Biomarkers, NT-proBNP, Medical imaging, Echocardiography, Computed tomography, Epicardial adipose tissue, Global longitudinal strain, Risk prediction model, Retrospective cohort study, HFpEF, HFrEF, Phenotype, All-cause mortality
Brief summary
This study aims to develop a better model to predict one-year risk of death in patients with heart failure. We will test whether combining information from routine blood tests (like NT-proBNP) and heart scans (measuring features like epicardial fat density) improves risk prediction compared to using either type of data alone. This is a retrospective study using existing medical records of patients treated for chronic heart failure at Xinjiang Medical University First Affiliated Hospital between 2012 and 2024. No new patient contact or interventions are involved. The goal is to enable more accurate, personalized risk assessment across different types of heart failure (HFrEF, HFmrEF, HFpEF).
Detailed description
Background and Rationale: Accurate prognosis in heart failure (HF) remains challenging due to phenotypic heterogeneity across the spectrum of left ventricular ejection fraction (LVEF). While biomarkers like N-terminal pro-B-type natriuretic peptide (NT-proBNP) and imaging parameters like LVEF are standard prognostic tools, each has limitations. Emerging imaging parameters, such as epicardial adipose tissue (EAT) density (reflecting fat inflammation/fibrosis) and left ventricular global longitudinal strain (LVGLS), offer potential incremental prognostic value but are not yet integrated into routine clinical models. This study aims to systematically evaluate whether a multi-parameter model combining established blood biomarkers and advanced imaging metrics improves the prognostic stratification of patients with HFrEF, HFmrEF, and HFpEF compared to traditional approaches. Detailed Methodology: This is a single-center, retrospective cohort study. The study population consists of consecutive adult patients (≥18 years) with a confirmed diagnosis of chronic HF who had both qualifying blood biomarker assessment (NT-proBNP and/or high-sensitivity cardiac troponin) and cardiac imaging (transthoracic echocardiography and/or cardiac computed tomography) performed within a ±3-month window around an index encounter between January 1, 2012, and December 31, 2024, at Xinjiang Medical University First Affiliated Hospital. Key data to be extracted from electronic health records include: 1) Clinical variables: demographics, comorbidities (e.g., ischemic etiology, diabetes, hypertension), medications, and NYHA class; 2) Blood biomarkers: NT-proBNP, hs-cTnT/I, hs-CRP, and renal function (eGFR); 3) Imaging parameters: LVEF, LVGLS, left atrial volume index (LAVI), E/e' ratio, and EAT volume/density (from CT, if available). The primary endpoint is all-cause mortality at one year from the index date. Follow-up data will be obtained from hospital records. Statistical Analysis Plan: The incremental prognostic value will be assessed by constructing and comparing nested Cox proportional hazards models: Model 1 (Base Clinical): Includes age, sex, BMI, ischemic etiology, diabetes, and hypertension. Model 2 (Biomarker-Enhanced): Model 1 + NT-proBNP + eGFR. Model 3 (Imaging-Enhanced): Model 2 + key imaging parameters (e.g., EAT density or LVGLS). Model performance will be compared using Harrell's C-statistic, the Akaike Information Criterion (AIC), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). Pre-specified subgroup analyses will be conducted for HFrEF, HFmrEF, and HFpEF phenotypes. Multiple imputation will be used for variables with low rates of missing data (\<10%).
Interventions
None listed
Sponsors
Xinjiang Medical University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Age ≥ 18 years. 2. Confirmed diagnosis of chronic heart failure. 3. Treated at the study center between January 1, 2012, and December 31, 2024. 4. Availability of both qualifying blood biomarker test results (NT-proBNP and/or high-sensitivity cardiac troponin) and cardiac imaging (echocardiography and/or cardiac CT) performed within a ±3-month window around the index encounter.
Exclusion criteria
1. Heart failure primarily due to severe primary valvular disease, acute myocardial infarction, myocarditis, or pulmonary embolism. 2. End-stage renal disease requiring dialysis. 3. Clinical records or follow-up data are severely incomplete, precluding outcome assessment.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| All-cause mortality | 1 year | Occurrence of death from any cause within one year (365 days) from the index date. The index date is defined as the date of the first qualifying encounter that meets all inclusion criteria. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Phenotype-specific prognostic performance | 1 year | Difference in the predictive performance (measured by Harrell's C-statistic) of the combined biomarker-imaging model across heart failure phenotypes (HFrEF, HFmrEF, HFpEF). |
| Independent prognostic value of EAT density in HFpEF | 1 year | Hazard ratio of epicardial adipose tissue (EAT) density for all-cause mortality in HFpEF patients, after adjustment for body mass index (BMI) and high-sensitivity C-reactive protein (hs-CRP). |
| Occurrence of HFimpEF | Through study completion,up to 13 years. | The proportion of patients with baseline HFrEF or HFmrEF who achieve HFimpEF, defined as a follow-up LVEF increase by ≥10 percentage points to a value of \>40%, assessed by follow-up echocardiography. |
| Association between baseline NT-proBNP level and HFimpEF | Through study completion, up to 13 years. | The association quantified by the Odds Ratio (OR) per unit increase in log-transformed baseline NT-proBNP level with the occurrence of HFimpEF, derived from a multivariable logistic regression model. |
| Association between baseline EAT density and HFimpEF | Through study completion, up to 13 years. | The association quantified by the Odds Ratio (OR) per unit increase in baseline epicardial adipose tissue (EAT) density (in Hounsfield Units) with the occurrence of HFimpEF, derived from a multivariable logistic regression model. |
Contacts
STUDY_CHAIRXiang Xie, PhD
First Affiliated Hospital of Xinjiang Medical University
Outcome results
None listed