BridgeBio Teams Up with Top Cardiovascular Data Science Lab for ATTR-CM
The TRACE-AI Network Study is set to implement a scalable screening toolkit for ATTR-CM across a wide range of electronic health records (EHRs) from various health systems. The goal is to identify individuals with ATTR-CM earlier in their disease progression and to better understand the potential prevalence of undiagnosed cases.
BridgeBio Pharma, Inc. (Nasdaq: BBIO) has announced an important scientific partnership with the CarDS Lab, which is led by cardiologist and data scientist Rohan Khera, M.D., M.S., at Yale School of Medicine. This collaboration aims to tackle the issue of underdiagnosis in ATTR-CM, a condition that is often difficult to detect due to its complex symptoms.
The TRACE-AI Network Study seeks to establish a new approach for large-scale federated screening of ATTR-CM. By utilizing a central repository of validated AI tools across multiple participating sites, the study will evaluate the extent of underdiagnosis of ATTR-CM in the United States. It will also investigate the prevalence of presymptomatic phenotypes in individuals with ATTR-CM, focusing on key socioeconomic and demographic subpopulations, as well as the connection between high-risk ATTR-CM identified through opportunistic testing and negative clinical outcomes.
Jennifer Hodge, Ph.D., Vice President of Evidence Generation at BridgeBio, stated, "At BridgeBio, we have long been committed to using computational methods to support drug discovery. By employing AI with real-world data streams, we have a unique opportunity to enhance detection and optimize the use of advanced diagnostic testing. This national initiative will implement scalable and accessible strategies to improve the diagnosis and prediction of ATTR-CM in diverse populations, addressing a long-standing unmet need in this area."
The CarDS Lab has developed a range of innovative deep learning tools that can be applied to real-world datasets. This includes technologies such as AI-electrocardiography (AI-ECG), AI-point-of-care ultrasound (AI-POCUS), and AI-echocardiography (AI-Echo). These tools are specifically designed to accurately identify individuals who may have missed a diagnosis of ATTR-CM, particularly those presenting with heart failure, enhancing accuracy, sensitivity, and specificity. The strategy not only offers a new method for early disease detection but also serves as a valuable tool for risk stratification, potentially improving current diagnostic processes within healthcare systems.
Dr. Khera noted, "The TRACE-AI Network Study represents a groundbreaking intersection of advanced technology and clinical expertise aimed at overcoming the challenges faced in diagnosing ATTR-CM across large and diverse health systems in the U.S. A significant advantage of our technology is its ability to utilize data that is routinely available at the point of care, enabling us to facilitate widespread deployment and provide access to traditionally underserved groups."
Ahmad Masri, M.D., M.S., Head of the Cardiomyopathy Section and Director of the Cardiac Amyloidosis Program, highlighted the critical nature of early detection, saying, "Identifying ATTR-CM early is essential, as evidence indicates that it is significantly underdiagnosed due to its complex presentation. This initiative is vital for enhancing diagnosis across the U.S. and could lead to better outcomes for patients who need it."
Additionally, the CarDS Lab will showcase original research funded by BridgeBio that supports the tools used in the TRACE-AI Network Study at the European Society of Cardiology’s Congress 2024. Key highlights include:
Oral Presentations
Title: Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis
Presenter: Veer Sangha, Yale School of Medicine
Presentation date & time: Friday, at 8:15 a.m. BST
Title: Artificial intelligence-guided screening of under-recognized cardiomyopathies adapted for point-of-care echocardiography
Presenter: Evangelos K. Oikonomou, M.D., DPhil, Yale School of Medicine
Presentation date & time: Sunday, at 8:15 a.m. BST
Moderated Posters
Title: Characterizing the progression of sub-clinical cardiac amyloidosis through artificial intelligence applied to electrocardiographic images and echocardiograms
Presenter: Evangelos K. Oikonomou, M.D., DPhil, Yale School of Medicine
Moderated poster date & time: Saturday, at 3:00 p.m. BST
Title: Detection of ATTR cardiac amyloidosis using a novel artificial intelligence algorithm for wearable-adapted noisy single-lead electrocardiograms
Presenter: Veer Sangha, Yale School of Medicine
Moderated poster date & time: Monday, at 12:00 p.m. BST
About BridgeBio Pharma, Inc.
BridgeBio Pharma, Inc. (BridgeBio) is a commercial-stage biopharmaceutical company focused on discovering, creating, testing, and delivering transformative medicines for patients with genetic diseases. Their pipeline spans from early scientific research to advanced clinical trials, demonstrating their commitment to rapidly applying advancements in genetic medicine to assist patients in need.
If you would like more information, please reach out to:
BridgeBio Contact:
Vikram Bali
contact@bridgebio.com
(650)-789-8220
Frequently Asked Questions
What is the TRACE-AI Network Study?
The TRACE-AI Network Study aims to implement a scalable screening toolkit to detect ATTR-CM across a variety of health system electronic health records.
Who is collaborating with BridgeBio on this initiative?
BridgeBio is partnering with the CarDS Lab, which is led by Rohan Khera, M.D., M.S., from Yale School of Medicine.
How does AI contribute to the study?
AI is used to enhance the early detection and diagnosis of ATTR-CM through validated tools applied to real-world datasets.
Why is early detection of ATTR-CM crucial?
Early detection is vital for managing ATTR-CM, which is frequently underdiagnosed due to its complex symptoms, impacting patient outcomes.
What will be showcased at the European Society of Cardiology’s Congress 2024?
Original research funded by BridgeBio related to the TRACE-AI Network Study will be presented, emphasizing AI applications in evaluating cardiac amyloidosis.
