Rob Dillard

DocwireNews

The Family Heart Foundation has announced the successful completion of the Flag, Identify, Network and Deliver™ “FIND Lp(a)&#8221; , a machine learning model which can identify people who are likely to have elevated Lp(a).
Current understanding pertaining to the prevalence and awareness of the role of Lp(a) in cardiovascular disease is lacking. The FIND Lp(a) machine learning model is designed to support at-risk individuals by engaging all health care stakeholders and provides optimal support to flagged individuals,  by developing and promoting best practices to support adoption of broad screening of the condition.
This machine learning model was developed using the Family Heart DatabaseTM of medical claims , and has demonstrated 60% precision. This model, as researchers noted, provides decision-support to clinicians by identifying a target group for this initial screening initiative. The data enables health care providers to focus their efforts and maximize the use of limited resources. Moreover, the model benefits health systems  by connecting their patients with education and individualized support.
&#8220;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,&#8221; said Ijeoma Isiadinso MD, MPH, Emory Center for Health Disease Prevention via a <a href="https://www.eurekalert.org/news-releases/1058561" target="_blank" rel="noopener">press release. &#8220;</a>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.&#8221;
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Researchers have developed a machine learning model which can identify people who are likely to have elevated Lp(a).

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