Today, the Family Heart Foundation®, a leading research and advocacy organization, announced the successful completion of the Flag, Identify, Network and Deliver™ “FIND Lp(a)” machine learning model – a first-of-its-kind offering being implemented in care delivery systems across the United States. This program utilizes a machine learning model to identify people who are likely to have elevated Lp(a) which is present in approximately 20% of people globally. Lipoprotein(a), also known as Lp(a), is an inherited condition that increases cardiovascular disease risk. It causes inflammation, clotting, and clogging in the vascular system throughout the life of an affected person, leading to premature cardiovascular disease and death. The Family Heart Foundation has partnered with a group of large health systems spanning across the United States to implement the FIND Lp(a) machine learning model, an initiative sponsored by Novartis. This model is used to pinpoint individuals whose early or aggressive cardiovascular disease may be accelerated by a genetic condition, elevated Lp(a).
Current understanding of the prevalence and awareness of the role of Lp(a) in cardiovascular disease is low, even in the medical community. The FIND Lp(a) machine learning model is designed to support at-risk individuals and their healthcare teams. This program engages all healthcare stakeholders and provides high touch support to flagged individuals, working to develop and promote best practices to support adoption of broad screening of a condition that is present in 1 in 5 people. It will accelerate the adoption of Lp(a) screening and appropriate management in patients with cardiovascular disease, including African American and Southeast Asian populations, who have been shown to have higher levels of Lp(a). FIND Lp(a) aims to dramatically increase Lp(a) screening and is designed to serve as a bridge to widespread screening of Lp(a). By combining machine learning methodology and implementation science improvement strategies with individualized patient support, the program will identify individuals with heart disease who most likely have this inherited condition to ensure they are well informed, are screened, and receive optimal care.
The machine learning model has already been created and initial testing using the Family Heart Database™ of medical claims and lab data has demonstrated 60% precision. This model provides decision-support to clinicians by identifying a target group for this initial screening initiative. This allows the clinicians and health care systems to focus their efforts and maximize the use of limited resources. Additionally, health systems are able to connect their patients with education and individualized support through the Family Heart Foundation (https://familyheart.org/care-navigation-center). This will provide a critical opportunity for individuals to better understand their risk for heart disease and stroke and become fully empowered to achieve their health goals.
"As a preventive cardiologist, I know how critical it is that we identify individuals with high Lp(a) early. It is equally important that patients with high Lp(a) are aware of the risk factors for cardiovascular disease and receive aggressive treatment for these risk factors, including controlling blood pressure, diabetes and cholesterol. The FIND Lp(a) partnership with the Family Heart Foundation will dramatically increase the number of individuals being screened for high Lp(a) and empower our patients with quality education and resources to manage their diagnosis. Time is of the essence. We are working together to identify and help individuals with high Lp(a) now."
- Ijeoma Isiadinso MD, MPH, Emory Center for Health Disease Prevention
We need to do more to leverage science and technology in our fight against heart disease. The Family Heart Foundation is committed to empowering individuals and their medical teams to understand if a lipoprotein(a) disorder is contributing to early or aggressive cardiovascular disease.”
Katherine Wilemon CEO and Founder, Family Heart Foundation
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