Heart failure (HF) is a growing public health problem, expected to increase in prevalence and incidence due to population aging. This challenge is compounded by the healthcare overload following the COVID-19 pandemic, particularly in primary care (PC). Early diagnosis of HF is critical for improving outcomes, reducing complications, and optimizing resource use. However, there is no robust scientific evidence supporting the effectiveness of early screening for HF in PC settings.
This study aims to evaluate whether an early cardiology assessment model for patients with suspected HF and elevated NT-proBNP levels (\>300 pg/mL) improves clinical outcomes compared to the standard referral pathway. The hypothesis is that early intervention will reduce emergency visits, hospitalizations, and mortality related to HF.
This is a prospective, single-center, open-label, phase II randomized controlled trial with parallel group allocation (1:1). Patients presenting to PC with HF symptoms and no prior HF diagnosis, who have NT-proBNP levels \>300 pg/mL, will be invited to participate. After informed consent, participants will be randomized to one of two groups:
* Intervention group: Early cardiology assessment within 7 days. * Control group: Standard referral by PC physician per usual care.
Randomization will be computer-generated and managed independently to ensure allocation concealment. Patients will be followed for 12 months from the date of NT-proBNP testing. Outcomes will be collected through both cardiology and PC visits.
Our primary outcome measure will be the clinical benefit, defined as a hierarchical composite endpoint of:
1. Cardiovascular mortality 2. All-cause mortality 3. Number of hospitalizations due to HF 4. Number of urgent care visits due to HF 5. Number of GDMT (Guideline-Directed Medical Therapy) drugs initiated 6. Number of GDMT drugs with dose escalation 7. Proportional change in log (NT-proBNP) at 12 months
The primary analysis will use a win ratio methodology to maximize statistical efficiency and clinical interpretability.
Secondary outcomes include:
* Each component of the primary endpoint * Stratified analysis by confirmed or excluded HF diagnosis * Stratified analysis by HF phenotype (HFrEF vs HFpEF) * Stratified analysis by sex
A sample size of 304 patients (152 per group) has been calculated to detect a win ratio of 1.7 with 80% power, based on expected clinical benefit and statistical assumptions from prior literature. The study is expected to complete recruitment within 12 months, with a total study duration of 24 months including follow-up and data analysis.
Age ≥ 18 years.
Signs and symptoms related to heart failure (HF).
Patients assessed in primary care.
No prior diagnosis of HF.
Patients diagnosed with a chronic disease with persistently elevated NT-proBNP levels, assessed by Cardiology, in whom heart failure (HF) has been ruled out in the previous year (mainly by echocardiography).
Chronic kidney disease on hemodialysis.
Patients with a life expectancy of less than 1 year due to severe comorbidities such as advanced-stage cancer.
Patients enrolled in other clinical trials.
Parallel
Early referral
Diagnostic testThe intervention involves early referral, allowing patients to be assessed by a cardiologist prior to the standard referral process. During this visit, the cardiologist will perform a comprehensive medical history and a detailed physical examination to assess signs of central and peripheral congestion. A standardized echocardiogram will also be conducted, including a series of objective measurements.
Clinical benefit based on a hierarchical composite outcome (win ratio approach)
Our primary outcome measure will be the clinical benefit, defined as a hierarchical composite endpoint of: 1. Cardiovascular mortality. 2. All-cause mortality. 3. Number of hospitalizations for heart failure (HF). 4. Number of urgent visits due to worsening HF symptoms requiring intravenous therapy, emergency department care or specialist evaluation. 5. Number of guideline-directed medical therapy (GDMT) drugs for HF initiated, if indicated (beta-blockers, RAAS inhibitors, SGLT2 inhibitors, MRAs). 6. Number of GDMT drugs with document dose up-titration (in those with indication). 7. Proportional 1-year change in plasma log(NT-proBNP) levels, as a biomarker of cardiac stress and congestion. The hierarchical structure of the primary endpoint and its components, along with the proposed statistical methodology, have been selected to ensure the feasibility of the study, efficient use of resources, and adequate statistical power.
Stratified analysis of the primary endpoint in patients with elevated NT-proBNP, with a confirmed or ruled-out diagnosis of heart failure (HF).
This outcome assesses the primary hierarchical composite clinical benefit endpoint (as defined in Outcome 1), stratifying patients with elevated NT-proBNP levels according to whether they ultimately receive a confirmed diagnosis of heart failure (HF) or not, based on clinical, imaging, and biomarker data. The analysis will evaluate whether the presence or absence of a confirmed HF diagnosis affects the patient's clinical benefit profile, as measured by the win ratio method across the predefined components: mortality, HF events, GDMT optimization, and biomarker change.
Stratified analysis of the hierarchical clinical benefit composite endpoint by heart failure phenotype (HFpEF vs. HFrEF)
This outcome will analyze the primary hierarchical composite clinical benefit endpoint (as defined in Outcome 1), stratified by HF phenotype: HFpEF (heart failure with preserved ejection fraction), and HFrEF (heart failure with reduced ejection fraction), as defined by left ventricular ejection fraction (LVEF) thresholds per guideline criteria. The goal is to assess whether the type of HF modifies the relative clinical benefit observed, using the same win ratio methodology.
Stratified analysis of the hierarchical clinical benefit composite endpoint by sex (male vs. female)
This outcome will evaluate the primary hierarchical composite clinical benefit endpoint (as defined in Outcome 1) stratified by biological sex (male vs female). The aim is to explore potential sex-related differences in the clinical response and benefit profile across the components of the composite endpoint, using the same hierarchical win ratio analysis.
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