Heart failure (HF) is a large public health problem affecting more than 6 million US adults with significant associated morbidity and mortality.(1) Recent data from the Framingham Heart Study demonstrated an 8-fold increased risk of death following transition from at-risk to development of clinically overt HF in spite of significant advances in secondary prevention of HF.(2) In the context of the aging population and increasing prevalence of risk factors for HF (e.g. obesity, diabetes), , the burden of HF is projected to increase to more than 8 million individuals with total healthcare costs projected to exceed $70 billion by 2030.(3) HF occurs in most adults due to cumulative exposure to preventable or modifiable risk factors that arise from adverse behavioral/lifestyle patterns. However, there remains a critical unmet need for primordial and primary prevention strategies to reverse adverse trends observed for HF. While the current approach utilizing risk-based decision-making in the primary prevention of ASCVD is widely accepted, no such paradigm currently exists for HF. Practice guidelines for HF from the American College of Cardiology (ACC)/American Heart Association (AHA), and Heart Failure Society of America place continued emphasis on primary prevention of HF, but identification of prevention and/or intervention strategies has been limited by a dearth of well-validated risk prediction models.(4, 5) In addition, guidelines for diabetes have begun to differentiate treatment approaches for those at greater risk for HF, but guidance is lacking on how to quantify HF risk.(6) In order to address this important knowledge gap, we recently developed and extensively validated the Pooled Cohort Equations to Prevent HF (PCP-HF), sex- and race-specific ten-year HF risk equations, from individual-level data from 7 population-based. Further, extension of this model to incorporate lifetime risk of HF and integration of assessment of health behaviors (diet, physical activity) are needed to refine risk estimates and personalize individual-level approaches to prevention over the life course. The advent of large, multifaceted datasets (i.e. ‘big data’), such as electronic health records (EHRs) offer the opportunity to complement observational datasets with “real-world” patient data and lay the groundwork for large-scale implementation of risk prediction.(7) The Northwestern Medicine Enterprise Data Warehouse, which includes data on 7.1 million patients from hundreds of care locations, provides a unique platform to optimize HF risk prediction using inpatient and outpatient encounters with available data on risk factors, biomarkers, and cardiovascular imaging. Implementation of a HF-specific risk score in the primary care setting offers the opportunity for an actionable screening tool to modify the natural history of HF risk. We have created a team with broad and deep expertise in epidemiology, preventive cardiology, and heart failure to efficiently address the following three SPECIFIC AIMS: Specific Aim 1. Develop comprehensive personalized estimates for Lifetime Risk of HF integrating cardiovascular risk factors and quantitative assessment of health behaviors (diet, physical activity) leveraging the Lifetime Risk Pooling Project (N=23,542). Specific Aim 2. Validate and recalibrate HF risk equations, incorporating rich phenotyping data available (vital signs, laboratory data, cardiovascular imaging) from an integrated EHR (N=36,965) to optimize “real-world” HF risk prediction and discriminate among HF subtypes (preserved, mid-range, and redu
|Effective start/end date||7/1/19 → 6/30/22|
- American Heart Association (19TPA34890060)
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.