Ignite Creation Date:
2026-03-26 @ 3:20 PM
Ignite Modification Date:
2026-03-30 @ 12:19 AM
Study NCT ID:
NCT07332520
Status:
COMPLETED
Last Update Posted:
2026-01-27
First Post:
2025-12-28
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predicting Heart Failure Outcomes With Biomarkers and Imaging
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D006333', 'term': 'Heart Failure'}, {'id': 'D054143', 'term': 'Heart Failure, Systolic'}, {'id': 'D054144', 'term': 'Heart Failure, Diastolic'}], 'ancestors': [{'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 4000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2012-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-01', 'completionDateStruct': {'date': '2025-01-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2026-01-26', 'studyFirstSubmitDate': '2025-12-28', 'studyFirstSubmitQcDate': '2025-12-30', 'lastUpdatePostDateStruct': {'date': '2026-01-27', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-01-12', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'All-cause mortality', 'timeFrame': '1 year', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Phenotype-specific prognostic performance', 'timeFrame': '1 year', 'description': "Difference in the predictive performance (measured by Harrell's C-statistic) of the combined biomarker-imaging model across heart failure phenotypes (HFrEF, HFmrEF, HFpEF)."}, {'measure': 'Independent prognostic value of EAT density in HFpEF', 'timeFrame': '1 year', 'description': '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).'}, {'measure': 'Occurrence of HFimpEF', 'timeFrame': 'Through study completion,up to 13 years.', 'description': '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.'}, {'measure': 'Association between baseline NT-proBNP level and HFimpEF', 'timeFrame': 'Through study completion, up to 13 years.', 'description': '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.'}, {'measure': 'Association between baseline EAT density and HFimpEF', 'timeFrame': 'Through study completion, up to 13 years.', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'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'], 'conditions': ['Heart Failure', 'Heart Failure, Systolic', 'Heart Failure, Diastolic', 'Heart Failure With Reduced Ejection Fraction (HFrEF)', 'Heart Failure With Preserved Ejection Fraction (HFPEF)', 'Heart Failure With Mid-Range Ejection Fraction (HFmrEF)']}, 'descriptionModule': {'briefSummary': '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.\n\nThis 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.\n\nThe goal is to enable more accurate, personalized risk assessment across different types of heart failure (HFrEF, HFmrEF, HFpEF).', 'detailedDescription': "Background and Rationale:\n\nAccurate 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.\n\nDetailed Methodology:\n\nThis 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.\n\nKey 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).\n\nThe primary endpoint is all-cause mortality at one year from the index date. Follow-up data will be obtained from hospital records.\n\nStatistical Analysis Plan:\n\nThe incremental prognostic value will be assessed by constructing and comparing nested Cox proportional hazards models:\n\nModel 1 (Base Clinical): Includes age, sex, BMI, ischemic etiology, diabetes, and hypertension.\n\nModel 2 (Biomarker-Enhanced): Model 1 + NT-proBNP + eGFR. Model 3 (Imaging-Enhanced): Model 2 + key imaging parameters (e.g., EAT density or LVGLS).\n\nModel 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%)."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Adults diagnosed with chronic heart failure at the study center between 2012 and 2024.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age ≥ 18 years.\n2. Confirmed diagnosis of chronic heart failure.\n3. Treated at the study center between January 1, 2012, and December 31, 2024.\n4. 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.\n\nExclusion Criteria:\n\n1. Heart failure primarily due to severe primary valvular disease, acute myocardial infarction, myocarditis, or pulmonary embolism.\n2. End-stage renal disease requiring dialysis.\n3. Clinical records or follow-up data are severely incomplete, precluding outcome assessment.'}, 'identificationModule': {'nctId': 'NCT07332520', 'acronym': 'BIOPHF', 'briefTitle': 'Predicting Heart Failure Outcomes With Biomarkers and Imaging', 'organization': {'class': 'OTHER', 'fullName': 'Xinjiang Medical University'}, 'officialTitle': 'Blood Biomarkers Combined With Imaging Parameters for Prognostic Assessment in Patients With Different Types of Heart Failure: A Retrospective Single-Center Cohort Study', 'orgStudyIdInfo': {'id': 'HF_Prognosis_2025'}, 'secondaryIdInfos': [{'id': 'K202511-03', 'type': 'OTHER', 'domain': 'Xinjiang Medical University'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Heart Failure Cohort', 'description': 'Adult patients (≥18 years) with a confirmed diagnosis of chronic heart failure who had both qualifying blood biomarker assessment and cardiac imaging performed within a specified window at the study center between 2012 and 2024.\n\nThis is an observational cohort. No specific intervention is administered or withheld as part of the research protocol.'}]}, 'contactsLocationsModule': {'overallOfficials': [{'name': 'Xiang Xie, PhD', 'role': 'STUDY_CHAIR', 'affiliation': 'First Affiliated Hospital of Xinjiang Medical University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Xinjiang Medical University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Xiang Xie', 'investigatorAffiliation': 'Xinjiang Medical University'}}}}