BridgeBio Innovates ATTR-CM Diagnosis with AI Partnership
BridgeBio Partners with Leading Cardiovascular Data Science Lab for ATTR-CM
The TRACE-AI Network Study will deploy a scalable screening toolkit for ATTR-CM across large, diverse health system electronic health records (EHRs) aiming to identify individuals who have ATTR-CM earlier in their disease course and quantify the potential prevalence of undiagnosed ATTR-CM.
BridgeBio Pharma, Inc. (Nasdaq: BBIO) has announced a significant scientific collaboration with the CarDS Lab, led by cardiologist-data scientist, Rohan Khera, M.D., M.S., at Yale School of Medicine. This initiative seeks to address the underdiagnosis of ATTR-CM, a condition that is challenging to detect due to its complex presentation.
The TRACE-AI Network Study aims to introduce a novel paradigm of large-scale federated screening for ATTR-CM. This approach utilizes a central repository of validated AI tools across multiple participating sites to evaluate the scale of ATTR-CM underdiagnosis in the U.S. The study intends to assess the prevalence of presymptomatic phenotypes of people with ATTR-CM by evaluating key socioeconomic and demographic subpopulations, along with the relationship between high-risk ATTR-CM detected through opportunistic testing and adverse clinical outcomes.
Jennifer Hodge, Ph.D., Vice President of Evidence Generation at BridgeBio, expressed, "At BridgeBio, we have long invested in computational approaches to aid drug discovery; similarly, by using AI with real-world data streams, we have a unique opportunity to improve the detection and optimize the utilization of advanced diagnostic testing. In this national initiative, we will deploy scalable and accessible strategies that improve the diagnosis and prediction of ATTR-CM in diverse populations, which has long been an unmet need in this category."
The CarDS Lab has created a suite of innovative deep learning tools that apply to real-world datasets. This includes AI-electrocardiography (AI-ECG), AI-point-of-care ultrasound (AI-POCUS), and AI-echocardiography (AI-Echo). These technologies are specifically designed to identify individuals with potentially missed ATTR-CM, such as those presenting with heart failure, with increased accuracy, sensitivity, and specificity. The developed strategy not only provides a novel method for early disease detection but also serves as a valuable tool for risk stratification, potentially improving the current diagnostic processes in healthcare systems.
Dr. Khera remarked, "For the first time, the TRACE-AI Network Study represents a convergence of cutting-edge technology and clinical expertise aimed at addressing the challenges currently seen in ATTR-CM diagnosis across large, diverse U.S. health systems. One key aspect of our technology is its applicability to data routinely available at the point of care, which will enable us to facilitate widespread deployment, providing access to traditionally disadvantaged groups."
Ahmad Masri, M.D., M.S., Cardiomyopathy Section Head and Director of the Cardiac Amyloidosis Program, emphasized the importance of early detection, noting, "Early detection is critical for someone with ATTR-CM, and evidence suggests that ATTR-CM is markedly underdiagnosed due to its complex presentation. This initiative is incredibly important toward improving diagnosis across the U.S. and potentially improving outcomes for patients in need."
Moreover, the CarDS Lab will present original research funded by BridgeBio that supports the tools utilized in the TRACE-AI Network Study at the European Society of Cardiology’s Congress 2024. Some 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 dedicated to discovering, creating, testing, and delivering transformative medicines for patients suffering from genetic diseases. The company’s pipeline ranges from early science to advanced clinical trials, showcasing their commitment to applying advances in genetic medicine swiftly to help patients in need.
If you would like additional information, please contact:
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 deploy a scalable screening toolkit for detecting ATTR-CM across diverse health system electronic health records.
Who is collaborating with BridgeBio on this initiative?
BridgeBio is collaborating with the CarDS Lab, led by Rohan Khera, M.D., M.S., from Yale School of Medicine.
How does AI play a role in the study?
AI is utilized to enhance early detection and diagnosis of ATTR-CM through validated tools applied to real-world datasets.
Why is early detection of ATTR-CM important?
Early detection is crucial for managing ATTR-CM, which is often underdiagnosed due to its complex symptoms, affecting patient outcomes.
What will be presented at the European Society of Cardiology’s Congress 2024?
Original research funded by BridgeBio related to the TRACE-AI Network Study will be presented, highlighting AI applications in assessing cardiac amyloidosis.
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