BridgeBio Partners with Leading Cardiovascular Data Science Lab (CarDS Lab) to Improve Transthyretin Amyloid Cardiomyopathy (ATTR-CM) Diagnosis in Diverse Patient Populations with Multimodal Artificial Intelligence
BridgeBio Pharma (Nasdaq: BBIO) has initiated a scientific collaboration with the CarDS Lab at Yale School of Medicine to address the underdiagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM). The TRACE-AI Network Study will deploy a scalable screening toolkit across diverse health system electronic health records (EHRs) to identify individuals with ATTR-CM earlier and quantify the potential prevalence of undiagnosed cases.
The CarDS Lab has developed novel deep learning tools applied to real-world data sets, including AI-electrocardiography, AI-point-of-care ultrasound, and AI-echocardiography, which may identify potentially missed ATTR-CM cases with high accuracy. This initiative aims to improve diagnosis across the U.S. and potentially enhance outcomes for patients in need.
BridgeBio Pharma (Nasdaq: BBIO) ha avviato una collaborazione scientifica con il CarDS Lab della Yale School of Medicine per affrontare il problema della sotto-diagnosi della cardiomiopatia amiloide da transtiretina (ATTR-CM). Lo studio della TRACE-AI Network utilizzerà un toolkit di screening scalabile attraverso diversi sistemi sanitari e i registri elettronici della salute (EHR) per identificare le persone con ATTR-CM in modo più precoce e quantificare la potenziale prevalenza dei casi non diagnosticati.
Il CarDS Lab ha sviluppato nuovi strumenti di deep learning applicati a set di dati reali, inclusi AI-elettrocardiografia, AI-ultrasonografia al punto di cura e AI-ecocardiografia, che potrebbero identificare casi di ATTR-CM potenzialmente trascurati con alta precisione. Questa iniziativa mira a migliorare la diagnosi negli Stati Uniti e potenzialmente a migliorare i risultati per i pazienti in necessità.
BridgeBio Pharma (Nasdaq: BBIO) ha iniciado una colaboración científica con el CarDS Lab de la Escuela de Medicina de Yale para abordar el problema de la subdiagnosis de la cardiomiopatía amiloide por transtiretina (ATTR-CM). El estudio de la TRACE-AI Network desplegará un conjunto de herramientas de detección escalable a través de diversos registros electrónicos de salud (EHR) para identificar a las personas con ATTR-CM más temprano y cuantificar la prevalencia potencial de casos no diagnosticados.
El CarDS Lab ha desarrollado nuevas herramientas de aprendizaje profundo aplicadas a conjuntos de datos del mundo real, incluyendo AI-electrocardiografía, AI-ultrasonido en el punto de atención y AI-ecocardiografía, que pueden identificar casos de ATTR-CM que podrían haber pasado desapercibidos con alta precisión. Esta iniciativa tiene como objetivo mejorar el diagnóstico en EE.UU. y potencialmente mejorar los resultados para los pacientes que lo necesitan.
브릿지바이오 제약(나스닥: BBIO)은 예일 의과대학 CarDS Lab과 협력하여 트랜스티레틴 아밀로이드 심장병(ATTR-CM)의 저진단 문제를 해결하기 위한 과학적 협업을 시작했습니다. TRACE-AI 네트워크 연구는 다양한 의료 시스템의 전자 건강 기록(EHR)을 통해 스케일러블 스크리닝 툴킷을 배포하여 ATTR-CM 환자를 더 일찍 식별하고 진단되지 않은 사례의 잠재적 유병률을 정량화할 것입니다.
CarDS Lab은 AI-심전도, AI-진료소 초음파, AI-심초음파 등 실제 데이터 세트에 적용된 새로운 딥러닝 도구를 개발하여 놓칠 가능성이 있는 ATTR-CM 사례를 높은 정확도로 식별할 수 있습니다. 이 이니셔티브는 미국 전역에서 진단을 개선하고, 필요로 하는 환자들의 결과를 향상시키는 것을 목표로 하고 있습니다.
BridgeBio Pharma (Nasdaq: BBIO) a entamé une collaboration scientifique avec le CarDS Lab de la Yale School of Medicine pour s'attaquer au problème de la sous-diagnostique de la cardiomyopathie amyloïde à transthyrétine (ATTR-CM). L'étude du TRACE-AI Network déploiera un outil de dépistage évolutif à travers divers dossiers de santé électroniques (DSE) des systèmes de santé afin d'identifier plus tôt les individus atteints d'ATTR-CM et de quantifier la prévalence potentielle des cas non diagnostiqués.
Le CarDS Lab a développé des outils d'apprentissage profond novateurs appliqués à des ensembles de données réelles, y compris AI-électrocardiographie, AI-ultrasonographie à point de soin et AI-échocardiographie, qui peuvent identifier des cas d'ATTR-CM potentiellement manqués avec une grande précision. Cette initiative vise à améliorer le diagnostic aux États-Unis et à potentiellement améliorer les résultats pour les patients dans le besoin.
BridgeBio Pharma (Nasdaq: BBIO) hat eine wissenschaftliche Zusammenarbeit mit dem CarDS Lab an der Yale School of Medicine begonnen, um das Problem der Unterdiagnose der transthyretin-Amyloid-Kardiomypathie (ATTR-CM) anzugehen. Die TRACE-AI Netzwerkstudie wird ein skalierbares Screening-Toolkit in verschiedenen elektronischen Gesundheitsakten (EHRs) der Gesundheitssysteme einsetzen, um Personen mit ATTR-CM früher zu identifizieren und die potenzielle Prävalenz unentdeckter Fälle zu quantifizieren.
Das CarDS Lab hat neuartige Deep-Learning-Tools entwickelt, die auf realen Datensätzen basieren, darunter AI-EKG, AI-Punkt-der-Versorgung-Ultraschall und AI-Echokardiographie. Diese könnten potenziell übersehene ATTR-CM-Fälle mit hoher Genauigkeit identifizieren. Diese Initiative zielt darauf ab, die Diagnostik in den USA zu verbessern und potenziell die Ergebnisse für bedürftige Patienten zu verbessern.
- Collaboration with leading cardiovascular data science lab to improve ATTR-CM diagnosis
- Development of AI tools for early detection and risk stratification of ATTR-CM
- Potential to address underdiagnosis of ATTR-CM in diverse populations
- Presentation of original research at the European Society of Cardiology's Congress 2024
- None.
Insights
This collaboration between BridgeBio and the CarDS Lab marks a significant step in addressing the underdiagnosis of ATTR-CM. The TRACE-AI Network Study's innovative approach combines AI tools with real-world data to improve early detection and risk stratification. Key points:
- Utilizes advanced AI techniques (AI-ECG, AI-POCUS, AI-Echo) for more accurate identification of potential ATTR-CM cases
- Aims to quantify undiagnosed ATTR-CM prevalence and identify presymptomatic phenotypes
- Focuses on diverse populations, potentially addressing healthcare disparities
This initiative could significantly impact patient outcomes by enabling earlier interventions and more targeted diagnostic processes. However, the real-world efficacy and scalability of these AI tools across diverse healthcare settings remain to be seen.
The TRACE-AI Network Study represents a paradigm shift in ATTR-CM screening. By leveraging AI and large-scale health system EHRs, it addresses critical gaps in current diagnostic approaches:
- Potential for earlier detection, important for ATTR-CM management
- Improved accessibility through point-of-care applications
- Focus on diverse populations, potentially reducing diagnostic disparities
The presentation of original research at ESC Congress 2024 further validates the scientific rigor behind these tools. However, it's important to note that while promising, AI-based screening tools require thorough clinical validation and careful implementation to avoid overdiagnosis or misdiagnosis. The true impact on patient outcomes and healthcare costs will need to be carefully evaluated in real-world settings.
This collaboration positions BridgeBio at the forefront of AI-driven diagnostics in cardiology, potentially expanding its market reach:
- Addresses a significant unmet need in ATTR-CM diagnosis, estimated to be underdiagnosed in many patients
- Potential to increase demand for ATTR-CM treatments by identifying more patients earlier
- Enhances BridgeBio's reputation in both AI and cardiology fields
The initiative could lead to increased market share and revenue growth if successful. However, investors should consider:
- Regulatory hurdles for AI-based diagnostic tools
- Potential pushback from traditional diagnostic method providers
- Long-term ROI dependent on successful implementation and adoption by healthcare systems
Overall, this represents a strategic move with significant upside potential, balanced by execution risks and regulatory uncertainties.
- 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
PALO ALTO, Calif., Aug. 26, 2024 (GLOBE NEWSWIRE) -- BridgeBio Pharma, Inc. (Nasdaq: BBIO) (“BridgeBio” or the “Company”), a commercial-stage biopharmaceutical company focused on genetic diseases, today announced the initiation of a scientific collaboration with the CarDS Lab, led by cardiologist-data scientist, Rohan Khera, M.D., M.S. at Yale School of Medicine, to help address the underdiagnosis of ATTR-CM.
The TRACE-AI Network Study will be deployed as a novel paradigm of large-scale federated screening for ATTR-CM that harnesses a central repository of validated AI tools across multiple participating sites to evaluate the scale of ATTR-CM underdiagnosis across the U.S. The participating sites in the network will aim to evaluate the scale of underdiagnosis among key socioeconomic and demographic subpopulations, estimate the prevalence of presymptomatic phenotypes of people with ATTR-CM, and assess the association between high-risk ATTR-CM on opportunistic testing and adverse clinical outcomes across the Network.
"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,” said Jennifer Hodge, Ph.D., Vice President of Evidence Generation at BridgeBio.
The CarDS Lab has developed a series of novel deep learning tools applied to real-world data sets, including AI-electrocardiography (AI-ECG), AI-point-of-care ultrasound (AI-POCUS), and AI-echocardiography (AI-Echo), which may be capable of identifying those with potentially missed ATTR-CM, such as those with heart failure, with high accuracy, sensitivity and specificity. This strategy not only offers a novel and accessible method for early disease detection but also serves as a valuable tool for risk stratification, ultimately enhancing the effectiveness of the current diagnostic processes in healthcare systems.
"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," said Dr. Khera. "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.”
“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,” said Ahmad Masri, M.D., M.S., Cardiomyopathy Section Head and Director of the Cardiac Amyloidosis Program at Oregon Health & Science University and Steering Committee Member of the TRACE-AI Network.
Additionally, 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 (ESC) Congress 2024. Details are as follows:
Oral Presentations
Title: Artificial intelligence applied to electrocardiographic images for scalable screening of cardiac amyloidosis
Presenter: Veer Sangha, Yale School of Medicine, U.S.
Presentation date & time: Friday, August 30th 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, U.S.
Presentation date & time: Sunday, September 1 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, U.S.
Moderated poster date & time: Saturday, August 31st 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, U.S.
Moderated poster date & time: Monday, September 2nd at 12:00 p.m. BST
About BridgeBio Pharma, Inc.
BridgeBio Pharma, Inc. (BridgeBio) is a commercial-stage biopharmaceutical company founded to discover, create, test and deliver transformative medicines to treat patients who suffer from genetic diseases. BridgeBio’s pipeline of development programs ranges from early science to advanced clinical trials. BridgeBio was founded in 2015 and its team of experienced drug discoverers, developers and innovators are committed to applying advances in genetic medicine to help patients as quickly as possible. For more information visit bridgebio.com and follow us on LinkedIn, Twitter and Facebook.
BridgeBio Forward-Looking Statements
This press release contains forward-looking statements. Statements in this press release may include statements that are not historical facts and are considered forward-looking within the meaning of Section 27A of the Securities Act of 1933, as amended (the Securities Act), and Section 21E of the Securities Exchange Act of 1934, as amended (the Exchange Act), which are usually identified by the use of words such as “anticipates,” “believes,” “continues,” “estimates,” “expects,” “hopes,” “intends,” “may,” “plans,” “projects,” “remains,” “seeks,” “should,” “will,” and variations of such words or similar expressions. We intend these forward-looking statements to be covered by the safe harbor provisions for forward-looking statements contained in Section 27A of the Securities Act and Section 21E of the Exchange Act. These forward-looking statements, including statements relating to the plans, intentions, expectations and strategies of applying artificial intelligence to improve the diagnosis and prediction of ATTR-CM are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations and strategies as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations or strategies will be attained or achieved. Furthermore, actual results may differ materially from those described in the forward-looking statements and will be affected by a number of risks, uncertainties and assumptions, including, but not limited to, , the design and success of the artificial intelligence tools used in the TRACE-AI Network Study in early disease detection, or the sufficiency of data sets to which such tools are applied, the continuing success of our collaborations with the CarDS Lab, and adverse effects on healthcare systems and disruption of the global economy, as well as those risks set forth in the Risk Factors section of our most recent Annual Report on Form 10-K and our other filings with the U.S. Securities and Exchange Commission. Moreover, we operate in a very competitive and rapidly changing environment in which new risks emerge from time to time. These forward-looking statements are based upon the current expectations and beliefs of our management as of the date of this press release, and are subject to certain risks and uncertainties that could cause actual results to differ materially from those described in the forward-looking statements. Except as required by applicable law, we assume no obligation to update publicly any forward-looking statements, whether as a result of new information, future events or otherwise.
BridgeBio Contact:
Vikram Bali
contact@bridgebio.com
(650)-789-8220
FAQ
What is the TRACE-AI Network Study initiated by BridgeBio (BBIO)?
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