Turning heart failure care from reactive rescue to proactive prevention—Heart failure prevention at scale

Heart failure is a condition in which the heart cannot pump enough blood to supply the body with the oxygen and nutrients it needs. It has emerged as a public health priority. It currently affects about 6.7 million adults in the United States and is projected to affect more than 11 million adults by 2050, with an estimated annual cost of $142 billion.

Heart failure can develop when the heart muscle is weak, when it becomes stiff and does not relax properly, or when the heart valves do not work as they should. Common symptoms include shortness of breath, difficulty lying flat because of breathing problems, swelling of the legs or abdomen, and a reduced ability to exercise or perform daily activities.

Heart failure is a chronic, progressive condition, meaning it develops over time and can worsen if not treated. To better reflect this progression, the American Heart Association introduced a staging system for heart failure:

●      Stage A: Individuals have risk factors for heart failure (such as high blood pressure, diabetes, or prior exposure to heart-damaging treatments) but no structural heart disease.

●      Stage B: Structural changes in the heart—such as weakened heart muscle, thickened walls, or abnormal heart valves—are present, but there are no symptoms.

●      Stage C: Structural heart disease is present with symptoms of heart failure.

●      Stage D: Advanced or end-stage heart failure, characterized by severe symptoms despite treatment and sometimes requiring advanced therapies such as heart transplantation or mechanical heart pumps.

This staging framework underscores the importance of early identification and prevention, particularly in the pre-symptomatic phases of disease. Risk-based prediction equations have long served as the foundation for preventive strategies in atherosclerotic cardiovascular disease, including heart attacks and stroke and the implementation of algorithm-driven prevention has led to substantial improvements in cardiovascular outcomes. Increasingly, however, evidence supports the role of targeted, population-based, early identification approaches for heart failure. Large-scale cohort studies have informed the development of risk-based equations for predicting incident heart failure, prompting the American Heart Association to issue guidance on population-based assessment for early-stage disease (Stage B heart failure) in 2025[1]^.These strategies aim to identify high-risk individuals earlier and enable timely intervention to mitigate progression to symptomatic heart failure.

Yet, implementation of population-based risk stratification faces substantial challenges. Selecting appropriate target populations, operationalizing the sequential steps after identification, and ensuring adequate provider education all represent key barriers, among other practical considerations. One approach that has been operationalized is a virtual-first, decentralized echocardiogram program in which individuals with Stage A heart failure are first identified using claims-based risk assessment, followed by biomarker assessment and, when indicated, an echocardiogram (ultrasound of heart) to detect subclinical structural or functional cardiac abnormalities. This approach bypasses the need for individual clinicians to manually review cases and coordinate sequential screening tests—processes that are often difficult to operationalize and contribute to clinical workload.

Furthermore, once high-risk individuals are identified, a multifactorial approach is required to translate early risk detection into durable improvements in outcomes. This includes promotion of cardiovascular health through lifestyle modification—such as healthy diet and physical activity—alongside treatment of common cardiometabolic risk factors, including hypertension, diabetes, obesity, sleep apnea, and chronic kidney disease, among other contributors to heart failure progression. Within a virtual-first, decentralized echocardiogram program, early interventions can be delivered through tailored, risk factor - focused clinical recommendations, enabling timely, individualized care aimed at improving cardiometabolic health and preventing progression to symptomatic heart failure. Emerging evidence suggests that well-operationalized virtual-first cardiology programs can meaningfully improve cardiometabolic risk profiles, including reductions in systolic blood pressure of approximately 11 mmHg and weight loss of 4.5 pounds in a previously published prospective study[2]. Effective intensive control of key risk factors, such as hypertension, has been shown in large randomized controlled trials to substantially reduce the risk of heart failure decompensation[3].

As the United States faces a growing burden of heart failure, effective proactive strategies paired with evidence-based interventions are critical for early disease identification and for shifting care from reactive management to impactful prevention. In addition to clinical impact, scalable prevention models must demonstrate economic value by reducing downstream utilization and avoidable costs. Achieving meaningful, cost-effective heart failure prevention will require coordinated, multi-stakeholder collaboration and innovative care frameworks that extend beyond traditional healthcare delivery models to support earlier, more efficient intervention.