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Oikonomou EK, Holste G, Yuan N, Coppi A, McNamara RL, Haynes NA, Vora AN, Velazquez EJ, Li F, Menon V, Kapadia SR, Gill TM, Nadkarni GN, Krumholz HM, Wang Z, Ouyang D, Khera R. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiol 2024:2817468. [PMID: 38581644 PMCID: PMC10999005 DOI: 10.1001/jamacardio.2024.0595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 02/27/2024] [Indexed: 04/08/2024]
Abstract
Importance Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization. Objective To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024. Exposure DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors. Conclusions and Relevance In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Gregory Holste
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin
| | - Neal Yuan
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Norrisa A. Haynes
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Amit N. Vora
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Eric J. Velazquez
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut
| | - Venu Menon
- Heart and Vascular Institute, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Samir R. Kapadia
- Heart and Vascular Institute, Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Thomas M. Gill
- Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin
| | - David Ouyang
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut
- Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, Connecticut
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
- Associate Editor, JAMA
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Oikonomou EK, Holste G, Coppi A, McNamara RL, Nadkarni GN, Baloescu C, Krumholz HM, Wang Z, Khera R. Artificial intelligence-guided detection of under-recognized cardiomyopathies on point-of-care cardiac ultrasound. medRxiv 2024:2024.03.10.24304044. [PMID: 38559021 PMCID: PMC10980112 DOI: 10.1101/2024.03.10.24304044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Point-of-care ultrasonography (POCUS) enables access to cardiac imaging directly at the bedside but is limited by brief acquisition, variation in acquisition quality, and lack of advanced protocols. Objective To develop and validate deep learning models for detecting underdiagnosed cardiomyopathies on cardiac POCUS, leveraging a novel acquisition quality-adapted modeling strategy. Methods To develop the models, we identified transthoracic echocardiograms (TTEs) of patients across five hospitals in a large U.S. health system with transthyretin amyloid cardiomyopathy (ATTR-CM, confirmed by Tc99m-pyrophosphate imaging), hypertrophic cardiomyopathy (HCM, confirmed by cardiac magnetic resonance), and controls enriched for the presence of severe AS. In a sample of 290,245 TTE videos, we used novel augmentation approaches and a customized loss function to weigh image and view quality to train a multi-label, view agnostic video-based convolutional neural network (CNN) to discriminate the presence of ATTR-CM, HCM, and/or AS. Models were tested across 3,758 real-world POCUS videos from 1,879 studies in 1,330 independent emergency department (ED) patients from 2011 through 2023. Results Our multi-label, view-agnostic classifier demonstrated state-of-the-art performance in discriminating ATTR-CM (AUROC 0.98 [95%CI: 0.96-0.99]) and HCM (AUROC 0.95 [95% CI: 0.94-0.96]) on standard TTE studies. Automated metrics of anatomical view correctness confirmed significantly lower quality in POCUS vs TTE videos (median view classifier confidence of 0.63 [IQR: 0.44-0.88] vs 0.93 [IQR: 0.69-1.00], p<0.001). When deployed to POCUS videos, our algorithm effectively discriminated ATTR-CM and HCM with AUROC of up to 0.94 (parasternal long-axis (PLAX)), and 0.85 (apical 4 chamber), corresponding to positive diagnostic odds ratios of 46.7 and 25.5, respectively. In total, 18/35 (51.4%) of ATTR-CM and 32/57 (41.1%) of HCM patients in the POCUS cohort had an AI-positive screen in the year before their eventual confirmatory imaging. Conclusions We define and validate an AI framework that enables scalable, opportunistic screening of under-diagnosed cardiomyopathies using POCUS.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Gregory Holste
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Cristiana Baloescu
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
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Ouyang D, Carter RE, Pellikka PA. Machine Learning in Imaging: What is JASE Looking For? J Am Soc Echocardiogr 2024; 37:273-275. [PMID: 38432849 DOI: 10.1016/j.echo.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Affiliation(s)
- David Ouyang
- Department of Cardiology, Cedars-Sinai Medical Center
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Oikonomou EK, Holste G, Yuan N, Coppi A, McNamara RL, Haynes N, Vora AN, Velazquez EJ, Li F, Menon V, Kapadia SR, Gill TM, Nadkarni GN, Krumholz HM, Wang Z, Ouyang D, Khera R. A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression. medRxiv 2024:2023.09.28.23296234. [PMID: 37808685 PMCID: PMC10557799 DOI: 10.1101/2023.09.28.23296234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Importance Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. Objective A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression. Design Setting and Participants We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024. Exposure DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR). Results A total of 12,599 participants were included in the echocardiographic study (YNHHS: n=8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: n=3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-Vmax. This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction. Conclusions and Relevance In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.
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Affiliation(s)
- Evangelos K. Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Gregory Holste
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Neal Yuan
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - Andreas Coppi
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Robert L. McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Norrisa Haynes
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Amit N. Vora
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Eric J. Velazquez
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA
| | - Venu Menon
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Samir R. Kapadia
- Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Thomas M Gill
- Section of Geriatrics, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Harlan M. Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
| | - Zhangyang Wang
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT
- Associate Editor, JAMA
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Delgado V, Ajmone Marsan N, Bonow RO, Hahn RT, Norris RA, Zühlke L, Borger MA. Degenerative mitral regurgitation. Nat Rev Dis Primers 2023; 9:70. [PMID: 38062018 DOI: 10.1038/s41572-023-00478-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/31/2023] [Indexed: 12/18/2023]
Abstract
Degenerative mitral regurgitation is a major threat to public health and affects at least 24 million people worldwide, with an estimated 0.88 million disability-adjusted life years and 34,000 deaths in 2019. Improving access to diagnostic testing and to timely curative therapies such as surgical mitral valve repair will improve the outcomes of many individuals. Imaging such as echocardiography and cardiac magnetic resonance allow accurate diagnosis and have provided new insights for a better definition of the most appropriate timing for intervention. Advances in surgical techniques allow minimally invasive treatment with durable results that last for ≥20 years. Transcatheter therapies can provide good results in select patients who are considered high risk for surgery and have a suitable anatomy; the durability of such repairs is up to 5 years. Translational science has provided new knowledge on the pathophysiology of degenerative mitral regurgitation and may pave the road to the development of medical therapies that could be used to halt the progression of the disease.
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Affiliation(s)
| | - Nina Ajmone Marsan
- Department of Cardiology, Leiden University Medical Center, Leiden, Netherlands
| | - Robert O Bonow
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Rebecca T Hahn
- Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY, USA
| | - Russell A Norris
- Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC, USA
| | - Liesl Zühlke
- South African Medical Research Council, Cape Town, South Africa
- Division of Paediatric Cardiology, Department of Paediatrics, Institute of Child Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Michael A Borger
- University Department of Cardiac Surgery, Leipzig Heart Center, Leipzig, Germany
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Affiliation(s)
- Ramsey M Wehbe
- Division of Cardiology, Department of Medicine and Biomedical Informatics Center (BMIC), Medical University of South Carolina, Charleston, South Carolina.
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