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Yuan N, Stein NR, Duffy G, Sandhu RK, Chugh SS, Chen PS, Rosenberg C, Albert CM, Cheng S, Siegel RJ, Ouyang D. Deep learning evaluation of echocardiograms to identify occult atrial fibrillation. NPJ Digit Med 2024; 7:96. [PMID: 38615104 PMCID: PMC11016113 DOI: 10.1038/s41746-024-01090-z] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 03/29/2024] [Indexed: 04/15/2024] Open
Abstract
Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.
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Affiliation(s)
- Neal Yuan
- School of Medicine, University of California, San Francisco, CA; Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA.
| | - Nathan R Stein
- Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | - Grant Duffy
- Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | | | - Sumeet S Chugh
- Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | | | | | | | - Susan Cheng
- Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | | | - David Ouyang
- Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
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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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3
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Jain S, Elias P, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein A, Avram R, Tison G, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence in Cardiovascular Care - Part 2: Applications: JACC Review Topic of the Week. J Am Coll Cardiol 2024:S0735-1097(24)06744-5. [PMID: 38593945 DOI: 10.1016/j.jacc.2024.03.401] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent Artificial Intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. Over 600 Food and Drug Administration (FDA)-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Sneha Jain
- Division of Cardiology, Stanford University School of Medicine; Palo Alto, CA
| | - Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center; New York, NY; Department of Biomedical Informatics Columbia University Irving Medical Center; New York, NY
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center; New York, NY
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center; Chicago, IL
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine; New Haven, CN
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine; Palo Alto, CA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center; Los Angeles, CA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine; Palo Alto, CA
| | - Andrew Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center; New York, NY
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, CA
| | - Geoff Tison
- Division of Cardiology, University of California San Francisco, San Francisco, CA
| | | | | | - Emma Pierson
- Department of Computer Science, Cornell Tech; New York, NY
| | - Ashley Beecy
- NewYork-Presbyterian Health System; New York, NY; Division of Cardiology, Weill Cornell Medical College; New York, NY
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center; New York, NY; NewYork-Presbyterian Health System; New York, NY
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center; New York, NY; NewYork-Presbyterian Health System; New York, NY
| | | | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine; St. Louis, MO.
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Elias P, Jain S, Poterucha T, Randazzo M, Lopez Jimenez F, Khera R, Perez M, Ouyang D, Pirruccello J, Salerno M, Einstein A, Avram R, Tison G, Nadkarni G, Natarajan V, Pierson E, Beecy A, Kumaraiah D, Haggerty C, Avari Silva JN, Maddox TM. Artificial Intelligence for Cardiovascular Care - Part 1: Advances: JACC Review Topic of the Week. J Am Coll Cardiol 2024:S0735-1097(24)06742-1. [PMID: 38593946 DOI: 10.1016/j.jacc.2024.03.400] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 03/14/2024] [Indexed: 04/11/2024]
Abstract
Recent AI advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using novel methods. Applications span wearables, electrocardiograms, echocardiography, angiography, genetics, and more. AI models detect diseases from electrocardiograms at accuracy not previously achieved by technology or human experts, including reduced ejection fraction, valvular heart disease, and other cardiomyopathies. However, AI's unique characteristics necessitates rigorous validation by addressing training methods, real-world efficacy, equity concerns, and long-term reliability. Despite an exponentially growing number of studies in cardiovascular AI, trials showing improvement in outcomes remain lacking. A number are currently underway. Embracing this rapidly evolving technology while setting a high evaluation benchmark will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.
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Affiliation(s)
- Pierre Elias
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center; New York, NY; Department of Biomedical Informatics Columbia University Irving Medical Center; New York, NY
| | - Sneha Jain
- Division of Cardiology, Stanford University School of Medicine; Palo Alto, CA
| | - Timothy Poterucha
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center; New York, NY
| | - Michael Randazzo
- Division of Cardiology, University of Chicago Medical Center; Chicago, IL
| | | | - Rohan Khera
- Division of Cardiology, Yale School of Medicine; New Haven, CN
| | - Marco Perez
- Division of Cardiology, Stanford University School of Medicine; Palo Alto, CA
| | - David Ouyang
- Division of Cardiology, Cedars-Sinai Medical Center; Los Angeles, CA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA
| | - Michael Salerno
- Division of Cardiology, Stanford University School of Medicine; Palo Alto, CA
| | - Andrew Einstein
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center; New York, NY
| | - Robert Avram
- Division of Cardiology, Montreal Heart Institute, Montreal, CA
| | - Geoff Tison
- Division of Cardiology, University of California San Francisco, San Francisco, CA
| | | | | | - Emma Pierson
- Department of Computer Science, Cornell Tech; New York, NY
| | - Ashley Beecy
- NewYork-Presbyterian Health System; New York, NY; Division of Cardiology, Weill Cornell Medical College; New York, NY
| | - Deepa Kumaraiah
- Seymour, Paul and Gloria Milstein Division of Cardiology, Columbia University Irving Medical Center; New York, NY; NewYork-Presbyterian Health System; New York, NY
| | - Chris Haggerty
- Department of Biomedical Informatics Columbia University Irving Medical Center; New York, NY; NewYork-Presbyterian Health System; New York, NY
| | | | - Thomas M Maddox
- Division of Cardiology, Washington University School of Medicine; St. Louis, MO.
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5
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Bhave S, Rodriguez V, Poterucha T, Mutasa S, Aberle D, Capaccione KM, Chen Y, Dsouza B, Dumeer S, Goldstein J, Hodes A, Leb J, Lungren M, Miller M, Monoky D, Navot B, Wattamwar K, Wattamwar A, Clerkin K, Ouyang D, Ashley E, Topkara VK, Maurer M, Einstein AJ, Uriel N, Homma S, Schwartz A, Jaramillo D, Perotte AJ, Elias P. Deep learning to detect left ventricular structural abnormalities in chest X-rays. Eur Heart J 2024:ehad782. [PMID: 38503537 DOI: 10.1093/eurheartj/ehad782] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/24/2023] [Accepted: 11/14/2023] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND AND AIMS Early identification of cardiac structural abnormalities indicative of heart failure is crucial to improving patient outcomes. Chest X-rays (CXRs) are routinely conducted on a broad population of patients, presenting an opportunity to build scalable screening tools for structural abnormalities indicative of Stage B or worse heart failure with deep learning methods. In this study, a model was developed to identify severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) using CXRs. METHODS A total of 71 589 unique CXRs from 24 689 different patients completed within 1 year of echocardiograms were identified. Labels for SLVH, DLV, and a composite label indicating the presence of either were extracted from echocardiograms. A deep learning model was developed and evaluated using area under the receiver operating characteristic curve (AUROC). Performance was additionally validated on 8003 CXRs from an external site and compared against visual assessment by 15 board-certified radiologists. RESULTS The model yielded an AUROC of 0.79 (0.76-0.81) for SLVH, 0.80 (0.77-0.84) for DLV, and 0.80 (0.78-0.83) for the composite label, with similar performance on an external data set. The model outperformed all 15 individual radiologists for predicting the composite label and achieved a sensitivity of 71% vs. 66% against the consensus vote across all radiologists at a fixed specificity of 73%. CONCLUSIONS Deep learning analysis of CXRs can accurately detect the presence of certain structural abnormalities and may be useful in early identification of patients with LV hypertrophy and dilation. As a resource to promote further innovation, 71 589 CXRs with adjoining echocardiographic labels have been made publicly available.
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Affiliation(s)
- Shreyas Bhave
- Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA
| | - Victor Rodriguez
- Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA
| | - Timothy Poterucha
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Simukayi Mutasa
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Dwight Aberle
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Kathleen M Capaccione
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Yibo Chen
- Inova Fairfax Hospital Imaging Center, Inova Fairfax Medical Campus, Falls Church, VA, USA
| | - Belinda Dsouza
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Shifali Dumeer
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Jonathan Goldstein
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Aaron Hodes
- Hackensack Radiology Group, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - Jay Leb
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Matthew Lungren
- Department of Radiology, University of California, SanFrancisco, CA, USA
| | - Mitchell Miller
- Hackensack Radiology Group, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - David Monoky
- Hackensack Radiology Group, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - Benjamin Navot
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Kapil Wattamwar
- Division of Vascular and Interventional Radiology, Department of Radiology, Montefiore Medical Center, Bronx, NY, USA
| | - Anoop Wattamwar
- Hackensack Radiology Group, Hackensack Meridian School of Medicine, Nutley, NJ, USA
| | - Kevin Clerkin
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - David Ouyang
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Euan Ashley
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Veli K Topkara
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Mathew Maurer
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Andrew J Einstein
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Nir Uriel
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Shunichi Homma
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Allan Schwartz
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
| | - Diego Jaramillo
- Department of Radiology, Columbia University Irving Medical Center, NewYork, NY, USA
| | - Adler J Perotte
- Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA
| | - Pierre Elias
- Division of Cardiology and Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 West 168th Street, PH20, NewYork, NY 10032, USA
- Seymour, Paul, and Gloria Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, 630 West 168th Street, NewYork, NY 10032, USA
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6
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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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7
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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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8
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Ji H, Gulati M, Huang TY, Kwan AC, Ouyang D, Ebinger JE, Casaletto K, Moreau KL, Skali H, Cheng S. Sex Differences in Association of Physical Activity With All-Cause and Cardiovascular Mortality. J Am Coll Cardiol 2024; 83:783-793. [PMID: 38383092 PMCID: PMC10984219 DOI: 10.1016/j.jacc.2023.12.019] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 02/23/2024]
Abstract
BACKGROUND Although physical activity is widely recommended for reducing cardiovascular and all-cause mortality risks, female individuals consistently lag behind male individuals in exercise engagement. OBJECTIVES The goal of this study was to evaluate whether physical activity derived health benefits may differ by sex. METHODS In a prospective study of 412,413 U.S. adults (55% female, age 44 ± 17 years) who provided survey data on leisure-time physical activity, we examined sex-specific multivariable-adjusted associations of physical activity measures (frequency, duration, intensity, type) with all-cause and cardiovascular mortality from 1997 through 2019. RESULTS During 4,911,178 person-years of follow-up, there were 39,935 all-cause deaths including 11,670 cardiovascular deaths. Regular leisure-time physical activity compared with inactivity was associated with 24% (HR: 0.76; 95% CI: 0.73-0.80) and 15% (HR: 0.85; 95% CI: 0.82-0.89) lower risk of all-cause mortality in women and men, respectively (Wald F = 12.0, sex interaction P < 0.001). Men reached their maximal survival benefit of HR 0.81 from 300 min/wk of moderate-to-vigorous physical activity, whereas women achieved similar benefit at 140 min/wk and then continued to reach a maximum survival benefit of HR 0.76 also at ∼300 min/wk. Sex-specific findings were similar for cardiovascular death (Wald F = 20.1, sex interaction P < 0.001) and consistent across all measures of aerobic activity as well as muscle strengthening activity (Wald F = 6.7, sex interaction P = 0.009). CONCLUSIONS Women compared with men derived greater gains in all-cause and cardiovascular mortality risk reduction from equivalent doses of leisure-time physical activity. These findings could enhance efforts to close the "gender gap" by motivating especially women to engage in any regular leisure-time physical activity.
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Affiliation(s)
- Hongwei Ji
- Tsinghua Medicine, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
| | - Martha Gulati
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Tzu Yu Huang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Joseph E Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Kaitlin Casaletto
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, California, USA
| | - Kerrie L Moreau
- Division of Geriatrics, University of Colorado School of Medicine, Aurora, Colorado, USA; Eastern Colorado Geriatric Research Education and Clinical Center, Aurora, Colorado, USA
| | - Hicham Skali
- Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
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9
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Holmstrom L, Chugh H, Nakamura K, Bhanji Z, Seifer M, Uy-Evanado A, Reinier K, Ouyang D, Chugh SS. An ECG-based artificial intelligence model for assessment of sudden cardiac death risk. Commun Med (Lond) 2024; 4:17. [PMID: 38413711 PMCID: PMC10899257 DOI: 10.1038/s43856-024-00451-9] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 02/02/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Conventional ECG-based algorithms could contribute to sudden cardiac death (SCD) risk stratification but demonstrate moderate predictive capabilities. Deep learning (DL) models use the entire digital signal and could potentially improve predictive power. We aimed to train and validate a 12 lead ECG-based DL algorithm for SCD risk assessment. METHODS Out-of-hospital SCD cases were prospectively ascertained in the Portland, Oregon, metro area. A total of 1,827 pre- cardiac arrest 12 lead ECGs from 1,796 SCD cases were retrospectively collected and analyzed to develop an ECG-based DL model. External validation was performed in 714 ECGs from 714 SCD cases from Ventura County, CA. Two separate control group samples were obtained from 1342 ECGs taken from 1325 individuals of which at least 50% had established coronary artery disease. The DL model was compared with a previously validated conventional 6 variable ECG risk model. RESULTS The DL model achieves an AUROC of 0.889 (95% CI 0.861-0.917) for the detection of SCD cases vs. controls in the internal held-out test dataset, and is successfully validated in external SCD cases with an AUROC of 0.820 (0.794-0.847). The DL model performs significantly better than the conventional ECG model that achieves an AUROC of 0.712 (0.668-0.756) in the internal and 0.743 (0.711-0.775) in the external cohort. CONCLUSIONS An ECG-based DL model distinguishes SCD cases from controls with improved accuracy and performs better than a conventional ECG risk model. Further detailed investigation is warranted to evaluate how the DL model could contribute to improved SCD risk stratification.
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Affiliation(s)
- Lauri Holmstrom
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Harpriya Chugh
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kotoka Nakamura
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ziana Bhanji
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Madison Seifer
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Audrey Uy-Evanado
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kyndaron Reinier
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sumeet S Chugh
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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10
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Vrudhula A, Kwan AC, Ouyang D, Cheng S. Machine Learning and Bias in Medical Imaging: Opportunities and Challenges. Circ Cardiovasc Imaging 2024; 17:e015495. [PMID: 38377237 PMCID: PMC10883605 DOI: 10.1161/circimaging.123.015495] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.
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Affiliation(s)
- Amey Vrudhula
- Icahn School of Medicine at Mount Sinai, New York
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
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11
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He B, Dash D, Duanmu Y, Tan TX, Ouyang D, Zou J. AI-ENABLED ASSESSMENT OF CARDIAC FUNCTION AND VIDEO QUALITY IN EMERGENCY DEPARTMENT POINT-OF-CARE ECHOCARDIOGRAMS. J Emerg Med 2024; 66:184-191. [PMID: 38369413 DOI: 10.1016/j.jemermed.2023.02.005] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 01/11/2023] [Accepted: 02/17/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND The adoption of point-of-care ultrasound (POCUS) has greatly improved the ability to rapidly evaluate unstable emergency department (ED) patients at the bedside. One major use of POCUS is to obtain echocardiograms to assess cardiac function. OBJECTIVES We developed EchoNet-POCUS, a novel deep learning system, to aid emergency physicians (EPs) in interpreting POCUS echocardiograms and to reduce operator-to-operator variability. METHODS We collected a new dataset of POCUS echocardiogram videos obtained in the ED by EPs and annotated the cardiac function and quality of each video. Using this dataset, we train EchoNet-POCUS to evaluate both cardiac function and video quality in POCUS echocardiograms. RESULTS EchoNet-POCUS achieves an area under the receiver operating characteristic curve (AUROC) of 0.92 (0.89-0.94) for predicting whether cardiac function is abnormal and an AUROC of 0.81 (0.78-0.85) for predicting video quality. CONCLUSIONS EchoNet-POCUS can be applied to bedside echocardiogram videos in real time using commodity hardware, as we demonstrate in a prospective pilot study.
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Affiliation(s)
- Bryan He
- Department of Computer Science, Stanford University, Stanford, California
| | - Dev Dash
- Department of Emergency Medicine, Stanford University, Stanford, California
| | - Youyou Duanmu
- Department of Emergency Medicine, Stanford University, Stanford, California
| | - Ting Xu Tan
- Department of Emergency Medicine, Stanford University, Stanford, California
| | - David Ouyang
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, California
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12
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Ebinger JE, Driver MP, Huang TY, Magraner J, Botting PG, Wang M, Chen PS, Bello NA, Ouyang D, Theurer J, Cheng S, Tan ZS. Blood pressure variability supersedes heart rate variability as a real-world measure of dementia risk. Sci Rep 2024; 14:1838. [PMID: 38246978 PMCID: PMC10800333 DOI: 10.1038/s41598-024-52406-8] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/18/2024] [Indexed: 01/23/2024] Open
Abstract
Blood pressure variability (BPV) and heart rate variability (HRV) have been associated with Alzheimer's Disease and Related Dementias (ADRD) in rigorously controlled studies. However, the extent to which BPV and HRV may offer predictive information in real-world, routine clinical care is unclear. In a retrospective cohort study of 48,204 adults (age 54.9 ± 17.5 years, 60% female) receiving continuous care at a single center, we derived BPV and HRV from routinely collected clinical data. We use multivariable Cox models to evaluate the association of BPV and HRV, separately and in combination, with incident ADRD. Over a median 3 [2.4, 3.0] years, there were 443 cases of new-onset ADRD. We found that clinically derived measures of BPV, but not HRV, were consistently associated with incident ADRD. In combined analyses, only patients in both the highest quartile of BPV and lowest quartile of HRV had increased ADRD risk (HR 2.34, 95% CI 1.44-3.81). These results indicate that clinically derived BPV, rather than HRV, offers a consistent and readily available metric for ADRD risk assessment in a real-world patient care setting. Thus, implementation of BPV as a widely accessible tool could allow clinical providers to efficiently identify patients most likely to benefit from comprehensive ADRD screening.
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Affiliation(s)
- Joseph E Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Matthew P Driver
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Tzu Yu Huang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jose Magraner
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Patrick G Botting
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Minhao Wang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Peng-Sheng Chen
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Natalie A Bello
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - John Theurer
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Zaldy S Tan
- Departments of Neurology and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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13
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Pillai B, Salerno M, Schnittger I, Cheng S, Ouyang D. Precision of Echocardiographic Measurements. J Am Soc Echocardiogr 2024:S0894-7317(24)00001-4. [PMID: 38199333 DOI: 10.1016/j.echo.2024.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 12/31/2023] [Accepted: 01/02/2024] [Indexed: 01/12/2024]
Affiliation(s)
- Balakrishnan Pillai
- Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Michael Salerno
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Ingela Schnittger
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
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14
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Steffner KR, Christensen M, Gill G, Bowdish M, Rhee J, Kumaresan A, He B, Zou J, Ouyang D. Deep learning for transesophageal echocardiography view classification. Sci Rep 2024; 14:11. [PMID: 38167849 PMCID: PMC10761863 DOI: 10.1038/s41598-023-50735-8] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/24/2023] [Indexed: 01/05/2024] Open
Abstract
Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and intraprocedural TEE data is the complexity and unstructured nature of these images. In the present study, we developed a deep learning-based, multi-category TEE view classification model that can be used to add structure to intraoperative and intraprocedural TEE imaging data. More specifically, we trained a convolutional neural network (CNN) to predict standardized TEE views using labeled intraoperative and intraprocedural TEE videos from Cedars-Sinai Medical Center (CSMC). We externally validated our model on intraoperative TEE videos from Stanford University Medical Center (SUMC). Accuracy of our model was high across all labeled views. The highest performance was achieved for the Trans-Gastric Left Ventricular Short Axis View (area under the receiver operating curve [AUC] = 0.971 at CSMC, 0.957 at SUMC), the Mid-Esophageal Long Axis View (AUC = 0.954 at CSMC, 0.905 at SUMC), the Mid-Esophageal Aortic Valve Short Axis View (AUC = 0.946 at CSMC, 0.898 at SUMC), and the Mid-Esophageal 4-Chamber View (AUC = 0.939 at CSMC, 0.902 at SUMC). Ultimately, we demonstrate that our deep learning model can accurately classify standardized TEE views, which will facilitate further downstream deep learning analyses for intraoperative and intraprocedural TEE imaging.
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Affiliation(s)
- Kirsten R Steffner
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA.
| | - Matthew Christensen
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - George Gill
- Department of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Michael Bowdish
- Department of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Justin Rhee
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Abirami Kumaresan
- Department of Cardiac Surgery, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
- Department of Anesthesiology, Cedars-Sinai Medical Center, Los Angeles, USA
| | - Bryan He
- Department of Computer Science, Stanford University, Stanford, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, USA
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15
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Ouyang D, Theurer J, Stein NR, Hughes JW, Elias P, He B, Yuan N, Duffy G, Sandhu RK, Ebinger J, Botting P, Jujjavarapu M, Claggett B, Tooley JE, Poterucha T, Chen JH, Nurok M, Perez M, Perotte A, Zou JY, Cook NR, Chugh SS, Cheng S, Albert CM. Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study. Lancet Digit Health 2024; 6:e70-e78. [PMID: 38065778 DOI: 10.1016/s2589-7500(23)00220-0] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/01/2023] [Accepted: 10/18/2023] [Indexed: 12/22/2023]
Abstract
BACKGROUND Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING National Heart, Lung, and Blood Institute.
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Affiliation(s)
- David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - John Theurer
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Nathan R Stein
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - J Weston Hughes
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - Pierre Elias
- Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Bryan He
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - Neal Yuan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Grant Duffy
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Roopinder K Sandhu
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joseph Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Patrick Botting
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Melvin Jujjavarapu
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Brian Claggett
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James E Tooley
- Division of Cardiology, Stanford University, Palo Alto, CA, USA
| | - Tim Poterucha
- Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jonathan H Chen
- Division of Bioinformatics Research, Stanford University, Palo Alto, CA, USA
| | - Michael Nurok
- Division of Anesthesia, Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Marco Perez
- Division of Cardiology, Stanford University, Palo Alto, CA, USA
| | - Adler Perotte
- Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - James Y Zou
- Department of Computer Science, Stanford University, Palo Alto, CA, USA; Department of Medicine, and Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Nancy R Cook
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Sumeet S Chugh
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Christine M Albert
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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16
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Duffy G, Christensen K, Ouyang D. Leveraging 3D Echocardiograms to Evaluate AI Model Performance in Predicting Cardiac Function on Out-of-Distribution Data. Pac Symp Biocomput 2024; 29:39-52. [PMID: 38160268] [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] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Advancements in medical imaging and artificial intelligence (AI) have revolutionized the field of cardiac diagnostics, providing accurate and efficient tools for assessing cardiac function. AI diagnostics claims to improve upon the human-to-human variation that is known to be significant. However, when put in practice, for cardiac ultrasound, AI models are being run on images acquired by human sonographers whose quality and consistency may vary. With more variation than other medical imaging modalities, variation in image acquisition may lead to out-of-distribution (OOD) data and unpredictable performance of the AI tools. Recent advances in ultrasound technology has allowed the acquisition of both 3D as well as 2D data, however 3D has more limited temporal and spatial resolution and is still not routinely acquired. Because the training datasets used when developing AI algorithms are mostly developed using 2D images, it is difficult to determine the impact of human variation on the performance of AI tools in the real world. The objective of this project is to leverage 3D echos to simulate realistic human variation of image acquisition and better understand the OOD performance of a previously validated AI model. In doing so, we develop tools for interpreting 3D echo data and quantifiably recreating common variation in image acquisition between sonographers. We also developed a technique for finding good standard 2D views in 3D echo volumes. We found the performance of the AI model we evaluated to be as expected when the view is good, but variations in acquisition position degraded AI model performance. Performance on far from ideal views was poor, but still better than random, suggesting that there is some information being used that permeates the whole volume, not just a quality view. Additionally, we found that variations in foreshortening didn't result in the same errors that a human would make.
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Affiliation(s)
- Grant Duffy
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd A3600, Los Angeles, CA 90048, United States
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Fouladvand S, Pierson E, Jankovic I, Ouyang D, Chen JH, Daneshjou R. Session Introduction: Artificial Intelligence in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface. Pac Symp Biocomput 2024; 29:1-7. [PMID: 38160265] [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] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Artificial Intelligence (AI) models are substantially enhancing the capability to analyze complex and multi-dimensional datasets. Generative AI and deep learning models have demonstrated significant advancements in extracting knowledge from unstructured text, imaging as well as structured and tabular data. This recent breakthrough in AI has inspired research in medicine, leading to the development of numerous tools for creating clinical decision support systems, monitoring tools, image interpretation, and triaging capabilities. Nevertheless, comprehensive research is imperative to evaluate the potential impact and implications of AI systems in healthcare. At the 2024 Pacific Symposium on Biocomputing (PSB) session entitled "Artificial Intelligence in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface", we spotlight research that develops and applies AI algorithms to solve real-world problems in healthcare.
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Affiliation(s)
- Sajjad Fouladvand
- Real-World Evidence and Advanced Analytics, Johnson and Johnson, Brisbane, CA, USA,
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18
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Vukadinovic M, Renjith G, Yuan V, Kwan A, Cheng SC, Li D, Clarke SL, Ouyang D. Impact of Measurement Noise on Genetic Association Studies of Cardiac Function. Pac Symp Biocomput 2024; 29:134-147. [PMID: 38160275] [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] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Recent research has effectively used quantitative traits from imaging to boost the capabilities of genome-wide association studies (GWAS), providing further understanding of disease biology and various traits. However, it's important to note that phenotyping inherently carries measurement error and noise that could influence subsequent genetic analyses. The study focused on left ventricular ejection fraction (LVEF), a vital yet potentially inaccurate quantitative measurement, to investigate how imprecision in phenotype measurement affects genetic studies. Several methods of acquiring LVEF, along with simulating measurement noise, were assessed for their effects on ensuing genetic analyses. The results showed that by introducing just 7.9% of measurement noise, all genetic associations in an LVEF GWAS with almost forty thousand individuals could be eliminated. Moreover, a 1% increase in mean absolute error (MAE) in LVEF had an effect equivalent to a 10% reduction in the sample size of the cohort on the power of GWAS. Therefore, enhancing the accuracy of phenotyping is crucial to maximize the effectiveness of genome-wide association studies.
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Affiliation(s)
- Milos Vukadinovic
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States2Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States4Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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19
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Yuan N, Duffy G, Dhruva SS, Oesterle A, Pellegrini CN, Theurer J, Vali M, Heidenreich PA, Keyhani S, Ouyang D. Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation. JAMA Cardiol 2023; 8:1131-1139. [PMID: 37851434 PMCID: PMC10585587 DOI: 10.1001/jamacardio.2023.3701] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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] [Received: 05/22/2023] [Accepted: 07/31/2023] [Indexed: 10/19/2023]
Abstract
Importance Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases. Objective To determine whether deep learning models applied to outpatient ECGs in sinus rhythm can predict AF in a large and diverse patient population. Design, Setting, and Participants This prognostic study was performed on ECGs acquired from January 1, 1987, to December 31, 2022, at 6 US Veterans Affairs (VA) hospital networks and 1 large non-VA academic medical center. Participants included all outpatients with 12-lead ECGs in sinus rhythm. Main Outcomes and Measures A convolutional neural network using 12-lead ECGs from 2 US VA hospital networks was trained to predict the presence of AF within 31 days of sinus rhythm ECGs. The model was tested on ECGs held out from training at the 2 VA networks as well as 4 additional VA networks and 1 large non-VA academic medical center. Results A total of 907 858 ECGs from patients across 6 VA sites were included in the analysis. These patients had a mean (SD) age of 62.4 (13.5) years, 6.4% were female, and 93.6% were male, with a mean (SD) CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) score of 1.9 (1.6). A total of 0.2% were American Indian or Alaska Native, 2.7% were Asian, 10.7% were Black, 4.6% were Latinx, 0.7% were Native Hawaiian or Other Pacific Islander, 62.4% were White, 0.4% were of other race or ethnicity (which is not broken down into subcategories in the VA data set), and 18.4% were of unknown race or ethnicity. At the non-VA academic medical center (72 483 ECGs), the mean (SD) age was 59.5 (15.4) years and 52.5% were female, with a mean (SD) CHA2DS2-VASc score of 1.6 (1.4). A total of 0.1% were American Indian or Alaska Native, 7.9% were Asian, 9.4% were Black, 2.9% were Latinx, 0.03% were Native Hawaiian or Other Pacific Islander, 74.8% were White, 0.1% were of other race or ethnicity, and 4.7% were of unknown race or ethnicity. A deep learning model predicted the presence of AF within 31 days of a sinus rhythm ECG on held-out test ECGs at VA sites with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.85-0.86), accuracy of 0.78 (95% CI, 0.77-0.78), and F1 score of 0.30 (95% CI, 0.30-0.31). At the non-VA site, AUROC was 0.93 (95% CI, 0.93-0.94); accuracy, 0.87 (95% CI, 0.86-0.88); and F1 score, 0.46 (95% CI, 0.44-0.48). The model was well calibrated, with a Brier score of 0.02 across all sites. Among individuals deemed high risk by deep learning, the number needed to screen to detect a positive case of AF was 2.47 individuals for a testing sensitivity of 25% and 11.48 for 75%. Model performance was similar in patients who were Black, female, or younger than 65 years or who had CHA2DS2-VASc scores of 2 or greater. Conclusions and Relevance Deep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.
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Affiliation(s)
- Neal Yuan
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Grant Duffy
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sanket S. Dhruva
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Adam Oesterle
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Cara N. Pellegrini
- Department of Medicine, University of California, San Francisco
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - John Theurer
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Marzieh Vali
- Department of Medicine, University of California, San Francisco
- Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - Paul A. Heidenreich
- Division of Cardiology, Palo Alto Veterans Affairs Medical Center, Palo Alto, California
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Palo Alto, California
| | - Salomeh Keyhani
- Department of Medicine, University of California, San Francisco
- Division of General Internal Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, California
| | - David Ouyang
- Division of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
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20
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Vukadinovic M, Kwan AC, Li D, Ouyang D. GANcMRI: Cardiac magnetic resonance video generation and physiologic guidance using latent space prompting. Proc Mach Learn Res 2023; 225:594-606. [PMID: 38213931 PMCID: PMC10783442] [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] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Generative artificial intelligence can be applied to medical imaging on tasks such as privacy-preserving image generation and superresolution and denoising of existing images. Few prior approaches have used cardiac magnetic resonance imaging (cMRI) as a modality given the complexity of videos (the addition of the temporal dimension) as well as the limited scale of publicly available datasets. We introduce GANcMRI, a generative adversarial network that can synthesize cMRI videos with physiological guidance based on latent space prompting. GANcMRI uses a StyleGAN framework to learn the latent space from individual video frames and leverages the timedependent trajectory between end-systolic and end-diastolic frames in the latent space to predict progression and generate motion over time. We proposed various methods for modeling latent time-dependent trajectories and found that our Frame-to-frame approach generates the best motion and video quality. GANcMRI generated high-quality cMRI image frames that are indistinguishable by cardiologists, however, artifacts in video generation allow cardiologists to still recognize the difference between real and generated videos. The generated cMRI videos can be prompted to apply physiologybased adjustments which produces clinically relevant phenotypes recognizable by cardiologists. GANcMRI has many potential applications such as data augmentation, education, anomaly detection, and preoperative planning.
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21
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Holste G, Oikonomou EK, Mortazavi BJ, Coppi A, Faridi KF, Miller EJ, Forrest JK, McNamara RL, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz HM, Wang Z, Khera R. Severe aortic stenosis detection by deep learning applied to echocardiography. Eur Heart J 2023; 44:4592-4604. [PMID: 37611002 PMCID: PMC11004929 DOI: 10.1093/eurheartj/ehad456] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 06/21/2023] [Accepted: 07/11/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND AND AIMS Early diagnosis of aortic stenosis (AS) is critical to prevent morbidity and mortality but requires skilled examination with Doppler imaging. This study reports the development and validation of a novel deep learning model that relies on two-dimensional (2D) parasternal long axis videos from transthoracic echocardiography without Doppler imaging to identify severe AS, suitable for point-of-care ultrasonography. METHODS AND RESULTS In a training set of 5257 studies (17 570 videos) from 2016 to 2020 [Yale-New Haven Hospital (YNHH), Connecticut], an ensemble of three-dimensional convolutional neural networks was developed to detect severe AS, leveraging self-supervised contrastive pretraining for label-efficient model development. This deep learning model was validated in a temporally distinct set of 2040 consecutive studies from 2021 from YNHH as well as two geographically distinct cohorts of 4226 and 3072 studies, from California and other hospitals in New England, respectively. The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.978 (95% CI: 0.966, 0.988) for detecting severe AS in the temporally distinct test set, maintaining its diagnostic performance in geographically distinct cohorts [0.952 AUROC (95% CI: 0.941, 0.963) in California and 0.942 AUROC (95% CI: 0.909, 0.966) in New England]. The model was interpretable with saliency maps identifying the aortic valve, mitral annulus, and left atrium as the predictive regions. Among non-severe AS cases, predicted probabilities were associated with worse quantitative metrics of AS suggesting an association with various stages of AS severity. CONCLUSION This study developed and externally validated an automated approach for severe AS detection using single-view 2D echocardiography, with potential utility for point-of-care screening.
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Affiliation(s)
- Gregory Holste
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Bobak J Mortazavi
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
| | - Andreas Coppi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
| | - Kamil F Faridi
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - John K Forrest
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Robert L McNamara
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
| | - Lucila Ohno-Machado
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, 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
| | - Aakriti Gupta
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 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
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, 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 University School of Medicine, 333 Cedar Street, New Haven, CT 06520-8056, USA
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St 5th Floor, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, 60 College St, New Haven, CT, USA
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Wehbe RM, Katsaggelos AK, Hammond KJ, Hong H, Ahmad FS, Ouyang D, Shah SJ, McCarthy PM, Thomas JD. Deep Learning for Cardiovascular Imaging: A Review. JAMA Cardiol 2023; 8:1089-1098. [PMID: 37728933 DOI: 10.1001/jamacardio.2023.3142] [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] [Indexed: 09/22/2023]
Abstract
Importance Artificial intelligence (AI), driven by advances in deep learning (DL), has the potential to reshape the field of cardiovascular imaging (CVI). While DL for CVI is still in its infancy, research is accelerating to aid in the acquisition, processing, and/or interpretation of CVI across various modalities, with several commercial products already in clinical use. It is imperative that cardiovascular imagers are familiar with DL systems, including a basic understanding of how they work, their relative strengths compared with other automated systems, and possible pitfalls in their implementation. The goal of this article is to review the methodology and application of DL to CVI in a simple, digestible fashion toward demystifying this emerging technology. Observations At its core, DL is simply the application of a series of tunable mathematical operations that translate input data into a desired output. Based on artificial neural networks that are inspired by the human nervous system, there are several types of DL architectures suited to different tasks; convolutional neural networks are particularly adept at extracting valuable information from CVI data. We survey some of the notable applications of DL to tasks across the spectrum of CVI modalities. We also discuss challenges in the development and implementation of DL systems, including avoiding overfitting, preventing systematic bias, improving explainability, and fostering a human-machine partnership. Finally, we conclude with a vision of the future of DL for CVI. Conclusions and Relevance Deep learning has the potential to meaningfully affect the field of CVI. Rather than a threat, DL could be seen as a partner to cardiovascular imagers in reducing technical burden and improving efficiency and quality of care. High-quality prospective evidence is still needed to demonstrate how the benefits of DL CVI systems may outweigh the risks.
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Affiliation(s)
- Ramsey M Wehbe
- Division of Cardiology, Department of Medicine & Biomedical Informatics Center, Medical University of South Carolina, Charleston
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Aggelos K Katsaggelos
- Department of Computer and Electrical Engineering, Northwestern University, Evanston, Illinois
| | - Kristian J Hammond
- Department of Computer Science, Northwestern University, Evanston, Illinois
| | - Ha Hong
- Medtronic, Minneapolis, Minnesota
| | - Faraz S Ahmad
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - David Ouyang
- Division of Cardiology, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Sanjiv J Shah
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - Patrick M McCarthy
- Division of Cardiac Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
| | - James D Thomas
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
- Center for Artificial Intelligence, Northwestern Medicine Bluhm Cardiovascular Institute, Chicago, Illinois
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23
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Gasho JO, Silos K, Guthier CV, Zhang SC, Burnison M, Mirhadi AJ, Jang JK, Shiao SL, Kamrava M, Steers J, McKenzie E, Tamarappoo B, Ouyang D, Kwan AC, Nikolova A, Mak RH, Atkins KM. Association of Left Anterior Descending Coronary Artery Calcium Progression and Radiation Dose with Major Adverse Cardiac Events in Breast Cancer. Int J Radiat Oncol Biol Phys 2023; 117:e175. [PMID: 37784789 DOI: 10.1016/j.ijrobp.2023.06.1020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Coronary artery calcium (CAC) is associated with increased risk of major adverse cardiac events (MACE). Accelerated CAC progression has been observed in patients with breast cancer after radiotherapy (RT) and there is a relationship between left anterior descending (LAD) coronary artery RT dose and the risk of coronary events. However, there is lack of consensus on LAD dose constraints for breast RT and limited data on the extent and impact of CAC progression. Our objective was to evaluate the association of LAD dose exposure and CAC progression with the risk of MACE in patients with breast cancer following RT. MATERIALS/METHODS Retrospective analysis of 181 patients with breast cancer treated with RT between 2008 and 2019. CAC was manually measured on RT planning and follow-up CTs (with at least one-year interval) using the Agatston method. Coronary arteries were segmented using a deep learning-based automated algorithm and dosimetric parameters collected. MACE cumulative incidence was estimated, and Fine and Gray regressions performed, accounting for non-cardiac death as a competing risk. RESULTS The median follow-up following RT was 70 months (interquartile range [IQR], 53-86). The median age was 63 years (IQR, 53-72), 43% had hypertension, 40% hyperlipidemia, 8% coronary heart disease (CHD). Most had pathologic stage I-II disease (76%). RT was targeted to breast/chest wall only in 60% and included regional nodes in 40% (internal mammary chain in 4%). The most common dose/fractionation was 48-50.4 Gy/25-28 fractions (67%) and 42.6-42.7 Gy/16 fractions (30%). At the time of RT, 68 (38%) had at least moderate CAC burden (CAC >100; statin-therapy indicated), but only 29 (43%) were on statin therapy. At a median interval of 44 months (IQR, 26-63), 55% (n = 84) had CAC progression, with a median increase of 52%/year (IQR, 18-193). The median time to MACE was 68 months (IQR, 53-85), with a 5-year cumulative incidence of 7.3% (15 MACE overall). Accounting for age and CHD, there was an increased risk of MACE with LAD CAC progression (subdistribution hazard ratio [SHR] 1.02/10 CAC points; 95% confidence interval [CI] 1.01 = 1.03; p = .007) and the volume of LAD receiving 15 Gy (LAD V15 Gy; SHR 1.03/%; 95% CI, 1.01-1.06; p = .004). There was no association between mean heart dose, chemotherapy, or Her2 therapy exposure and MACE (p>.05). CONCLUSION LAD CAC progression and LAD V15 Gy dose exposure were associated with an increased risk of MACE following RT. Accelerated CAC progression was commonly observed, however most patients were under-optimized for cardiovascular (CV) risk, with less than half of statin-eligible patients with at least moderate CAC burden on statin therapy. Together, these data support more aggressive cardiac risk mitigation approaches, including guidelines-based CV risk factor modification and optimized sparing of LAD radiation dose.
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Affiliation(s)
- J O Gasho
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - K Silos
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - C V Guthier
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA
| | - S C Zhang
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - M Burnison
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - A J Mirhadi
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - J K Jang
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - S L Shiao
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - M Kamrava
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - J Steers
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - E McKenzie
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - B Tamarappoo
- Department of Cardiology, Indiana University, Indianapolis, IN
| | - D Ouyang
- Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - A C Kwan
- Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - A Nikolova
- Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA
| | - R H Mak
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, MA
| | - K M Atkins
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
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24
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Hughes JW, Tooley J, Torres Soto J, Ostropolets A, Poterucha T, Christensen MK, Yuan N, Ehlert B, Kaur D, Kang G, Rogers A, Narayan S, Elias P, Ouyang D, Ashley E, Zou J, Perez MV. A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease. NPJ Digit Med 2023; 6:169. [PMID: 37700032 PMCID: PMC10497604 DOI: 10.1038/s41746-023-00916-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 08/30/2023] [Indexed: 09/14/2023] Open
Abstract
The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.
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Affiliation(s)
- J Weston Hughes
- Department of Computer Science, Stanford University, Palo Alto, CA, USA.
| | - James Tooley
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | - Jessica Torres Soto
- Department of Biomedical Informatics, Stanford University, Palo Alto, CA, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Tim Poterucha
- Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Matthew Kai Christensen
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Neal Yuan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ben Ehlert
- Department of Biomedical Informatics, Stanford University, Palo Alto, CA, USA
| | | | - Guson Kang
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | - Albert Rogers
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sanjiv Narayan
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | - Pierre Elias
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- Milstein Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Euan Ashley
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | - James Zou
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Marco V Perez
- Department of Medicine, Stanford University, Palo Alto, CA, USA
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25
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Sangha V, Nargesi AA, Dhingra LS, Khunte A, Mortazavi BJ, Ribeiro AH, Banina E, Adeola O, Garg N, Brandt CA, Miller EJ, Ribeiro ALJ, Velazquez EJ, Giatti L, Barreto SM, Foppa M, Yuan N, Ouyang D, Krumholz HM, Khera R. Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images. Circulation 2023; 148:765-777. [PMID: 37489538 PMCID: PMC10982757 DOI: 10.1161/circulationaha.122.062646] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 06/26/2023] [Indexed: 07/26/2023]
Abstract
BACKGROUND Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction <40%. The model was validated within clinical settings at Yale New Haven Hospital and externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA; Lake Regional Hospital in Osage Beach, MO; Memorial Hermann Southeast Hospital in Houston, TX; and Methodist Cardiology Clinic of San Antonio, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health. Gradient-weighted class activation mapping was used to localize class-discriminating signals on ECG images. RESULTS Overall, 385 601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROCs], 0.91; area under precision-recall curve [AUPRC], 0.55); and external sets of ECG images from Cedars Sinai (AUROC, 0.90 and AUPRC, 0.53), outpatient Yale New Haven Hospital clinics (AUROC, 0.94 and AUPRC, 0.77), Lake Regional Hospital (AUROC, 0.90 and AUPRC, 0.88), Memorial Hermann Southeast Hospital (AUROC, 0.91 and AUPRC 0.88), Methodist Cardiology Clinic (AUROC, 0.90 and AUPRC, 0.74), and Brazilian Longitudinal Study of Adult Health cohort (AUROC, 0.95 and AUPRC, 0.45). An ECG suggestive of LV systolic dysfunction portended >27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2 and V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years). CONCLUSIONS We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.
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Affiliation(s)
- Veer Sangha
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Arash A Nargesi
- Heart and Vascular Center, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Lovedeep S Dhingra
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Akshay Khunte
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Bobak J Mortazavi
- Department of Computer Science & Engineering, Texas A&M University, College Station, TX, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
| | - Antônio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Evgeniya Banina
- Internal Medicine Department, Lake Regional Hospital Health, Osage Beach, MO, USA
| | - Oluwaseun Adeola
- Methodist Cardiology Clinic of San Antonio, San Antonio, TX, USA
| | - Nadish Garg
- Heart and Vascular Institute, Memorial Hermann Southeast Hospital, Houston, TX, USA
| | - Cynthia A Brandt
- Department of Emergency Medicine, Yale University, New Haven, CT, USA
- VA Connecticut Healthcare System, West Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Antonio Luiz J Ribeiro
- Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Eric J Velazquez
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
| | - Luana Giatti
- Department of Preventive Medicine, School of Medicine and Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sandhi M Barreto
- Department of Preventive Medicine, School of Medicine, and Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Murilo Foppa
- Postgraduate Studies Program in Cardiology and Division of Cardiology, Hospital de Clinicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Neal Yuan
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Section of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, CA, 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
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University, New Haven, CT, USA
- Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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26
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Holmström L, Zhang FZ, Ouyang D, Dey D, Slomka PJ, Chugh SS. Artificial Intelligence in Ventricular Arrhythmias and Sudden Death. Arrhythm Electrophysiol Rev 2023; 12:e17. [PMID: 37457439 PMCID: PMC10345967 DOI: 10.15420/aer.2022.42] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/16/2023] [Indexed: 07/18/2023] Open
Abstract
Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field.
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Affiliation(s)
- Lauri Holmström
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA, US
| | - Frank Zijun Zhang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
| | - David Ouyang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
| | - Damini Dey
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
| | - Sumeet S Chugh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA, US
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27
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Nagueh SF, Klein AL, Scherrer-Crosbie M, Fine NM, Kirkpatrick JN, Forsha DE, Nicoara A, Mackensen GB, Tilkemeier PL, Doukky R, Cheema B, Adusumalli S, Hill JC, Tanguturi VK, Ouyang D, Bdoyan SB, Strom JB. A Vision for the Future of Quality in Echocardiographic Reporting: The American Society of Echocardiography ImageGuideEcho Registry, Current and Future States. J Am Soc Echocardiogr 2023:S0894-7317(23)00250-X. [PMID: 37256252 DOI: 10.1016/j.echo.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/05/2023] [Accepted: 05/01/2023] [Indexed: 06/01/2023]
Affiliation(s)
- Sherif F Nagueh
- Department of Cardiology, Houston Methodist Hospital, Weill Cornell Medical College, Houston, Texas
| | - Allan L Klein
- Center for the Diagnosis and Treatment of Pericardial Diseases, Section of Cardiovascular Imaging, Department of Cardiovascular Medicine, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, Ohio
| | - Marielle Scherrer-Crosbie
- Cardiovascular Medicine Division, Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Nowell M Fine
- Division of Cardiology, Department of Cardiac Sciences, Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - James N Kirkpatrick
- Division of Cardiology, Department of Medicine, University of Washington School of Medicine, Seattle, Washington
| | - Daniel E Forsha
- Ward Family Heart Center, Children's Mercy Kansas City, Missouri; Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Alina Nicoara
- Department of Anesthesiology, Duke University Medical Center, Durham, North Carolina
| | - G Burkhard Mackensen
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington
| | - Peter L Tilkemeier
- Department of Medicine, Prisma Health and the University of South Carolina School of Medicine Greenville, Greenville, South Carolina
| | - Rami Doukky
- Division of Cardiology, Cook County Health, Chicago, Illinois
| | - Baljash Cheema
- Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Srinath Adusumalli
- Cardiovascular Medicine Division, Department of Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania; CVS Health, Woonsocket, Rhode Island
| | - Jeffrey C Hill
- School of Medical Imaging and Therapeutics, Massachusetts College of Pharmacy and Health Sciences University, Worcester, Massachusetts
| | - Varsha K Tanguturi
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - David Ouyang
- Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Jordan B Strom
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts; Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
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28
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Holmstrom L, Christensen M, Yuan N, Weston Hughes J, Theurer J, Jujjavarapu M, Fatehi P, Kwan A, Sandhu RK, Ebinger J, Cheng S, Zou J, Chugh SS, Ouyang D. Deep learning-based electrocardiographic screening for chronic kidney disease. Commun Med (Lond) 2023; 3:73. [PMID: 37237055 DOI: 10.1038/s43856-023-00278-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 03/10/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Undiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs. METHODS We collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs between 2005 and 2018. RESULTS Using 12-lead ECG waveforms, our deep learning algorithm achieves discrimination for CKD of any stage with an AUC of 0.767 (95% CI 0.760-0.773) in a held-out test set and an AUC of 0.709 (0.708-0.710) in the external cohort. Our 12-lead ECG-based model performance is consistent across the severity of CKD, with an AUC of 0.753 (0.735-0.770) for mild CKD, AUC of 0.759 (0.750-0.767) for moderate-severe CKD, and an AUC of 0.783 (0.773-0.793) for ESRD. In patients under 60 years old, our model achieves high performance in detecting any stage CKD with both 12-lead (AUC 0.843 [0.836-0.852]) and 1-lead ECG waveform (0.824 [0.815-0.832]). CONCLUSIONS Our deep learning algorithm is able to detect CKD using ECG waveforms, with stronger performance in younger patients and more severe CKD stages. This ECG algorithm has the potential to augment screening for CKD.
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Affiliation(s)
- Lauri Holmstrom
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Research Unit of Internal Medicine, Medical Research Center Oulu, University of Oulu and Oulu University Hospital, Oulu, Finland
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Matthew Christensen
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Neal Yuan
- Department of Medicine, Division of Cardiology, San Francisco VA, UCSF, San Francisco, CA, USA
| | - J Weston Hughes
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - John Theurer
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Melvin Jujjavarapu
- Enterprise Information Service, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Pedram Fatehi
- Division of Nephrology, Department of Medicine, Stanford University, Palo Alto, CA, USA
| | - Alan Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Roopinder K Sandhu
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joseph Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - James Zou
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA
| | - Sumeet S Chugh
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
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29
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Vukadinovic M, Kwan AC, Yuan V, Salerno M, Lee DC, Albert CM, Cheng S, Li D, Ouyang D, Clarke SL. Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes. Med 2023; 4:252-262.e3. [PMID: 36996817 PMCID: PMC10106428 DOI: 10.1016/j.medj.2023.02.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/02/2023] [Accepted: 02/15/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology. METHODS We measured the left ventricle (LV) sphericity index (short axis length/long axis length) using deep learning-enabled image segmentation of cardiac magnetic resonance imaging data from the UK Biobank. Subjects with abnormal LV size or systolic function were excluded. The relationship between LV sphericity and cardiomyopathy was assessed using Cox analyses, genome-wide association studies, and two-sample Mendelian randomization. FINDINGS In a cohort of 38,897 subjects, we show that a one standard deviation increase in sphericity index is associated with a 47% increased incidence of cardiomyopathy (hazard ratio [HR]: 1.47, 95% confidence interval [CI]: 1.10-1.98, p = 0.01) and a 20% increased incidence of atrial fibrillation (HR: 1.20, 95% CI: 1.11-1.28, p < 0.001), independent of clinical factors and traditional magnetic resonance imaging (MRI) measurements. We identify four loci associated with sphericity at genome-wide significance, and Mendelian randomization supports non-ischemic cardiomyopathy as causal for LV sphericity. CONCLUSIONS Variation in LV sphericity in otherwise normal hearts predicts risk for cardiomyopathy and related outcomes and is caused by non-ischemic cardiomyopathy. FUNDING This study was supported by grants K99-HL157421 (D.O.) and KL2TR003143 (S.L.C.) from the National Institutes of Health.
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Affiliation(s)
- Milos Vukadinovic
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Victoria Yuan
- School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Salerno
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94306, USA
| | - Daniel C Lee
- Department of Medicine and Radiology, Northwestern Medicine, Chicago, IL 60611, USA
| | - Christine M Albert
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
| | - Shoa L Clarke
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94306, USA.
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30
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He B, Kwan AC, Cho JH, Yuan N, Pollick C, Shiota T, Ebinger J, Bello NA, Wei J, Josan K, Duffy G, Jujjavarapu M, Siegel R, Cheng S, Zou JY, Ouyang D. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature 2023; 616:520-524. [PMID: 37020027 PMCID: PMC10115627 DOI: 10.1038/s41586-023-05947-3] [Citation(s) in RCA: 43] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/13/2023] [Indexed: 04/07/2023]
Abstract
Artificial intelligence (AI) has been developed for echocardiography1-3, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of -10.4%, 95% confidence interval: -13.2% to -7.7%, P < 0.001 for non-inferiority, P < 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of -0.96%, 95% confidence interval: -1.34% to -0.54%, P < 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.
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Affiliation(s)
- Bryan He
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jae Hyung Cho
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Neal Yuan
- Department of Medicine, Division of Cardiology, San Francisco VA, UCSF, San Francisco, CA, USA
| | - Charles Pollick
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Takahiro Shiota
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joseph Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Natalie A Bello
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Janet Wei
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Kiranbir Josan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Grant Duffy
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Melvin Jujjavarapu
- Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Robert Siegel
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - James Y Zou
- Department of Computer Science, Stanford University, Palo Alto, CA, USA.
- Department of Biomedical Data Science, Stanford University, Palo Alto, CA, 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.
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Joshi M, Melo DP, Ouyang D, Slomka PJ, Williams MC, Dey D. Current and Future Applications of Artificial Intelligence in Cardiac CT. Curr Cardiol Rep 2023; 25:109-117. [PMID: 36708505 DOI: 10.1007/s11886-022-01837-8] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/10/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW In this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications. RECENT FINDINGS Recent studies have shown that deep learning networks can be applied for rapid automated segmentation of coronary plaque from coronary CT angiography, with AI-enabled measurement of total plaque volume predicting future heart attack. AI has also been applied to automate assessment of coronary artery calcium on cardiac and ungated chest CT and to automate the measurement of epicardial fat. Additionally, AI-based prediction models integrating clinical and imaging parameters have been shown to improve prediction of cardiac events compared to traditional risk scores. Artificial intelligence applications have been applied in all aspects of cardiovascular CT - in image acquisition, reconstruction and denoising, segmentation and quantitative analysis, diagnosis and decision assistance and to integrate prognostic risk from clinical data and images. Further incorporation of artificial intelligence in cardiovascular imaging holds important promise to enhance cardiovascular CT as a precision medicine tool.
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Affiliation(s)
- Mugdha Joshi
- Department of Medicine, Stanford Healthcare, Palo Alto, CA, USA
| | - Diana Patricia Melo
- Division of Cardiovascular Medicine, Stanford Healthcare, Palo Alto, CA, USA
| | - David Ouyang
- Cedars-Sinai Medical Center, Smidt Heart Institute, Los Angeles, CA, USA
| | - Piotr J Slomka
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michelle C Williams
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Damini Dey
- Cedars-Sinai Medical Center, Biomedical Imaging Research Institute, 116 N Robertson Boulevard, Los Angeles, CA, 90048, USA.
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Vukadinovic M, Renjith G, Yuan V, Kwan A, Cheng SC, Li D, Clarke SL, Ouyang D. Impact of Measurement Imprecision on Genetic Association Studies of Cardiac Function. medRxiv 2023:2023.02.16.23286058. [PMID: 36824841 PMCID: PMC9949184 DOI: 10.1101/2023.02.16.23286058] [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: 02/20/2023]
Abstract
Background Recent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses. Methods Left ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses. Results Even within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies. Conclusions Improving the precision of phenotyping is important for maximizing the yield of genome-wide association studies.
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Affiliation(s)
- Milos Vukadinovic
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Gauri Renjith
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Victoria Yuan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA
| | - Alan Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Susan C Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Shoa L Clarke
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
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Reddy CD, Lopez L, Ouyang D, Zou JY, He B. Video-Based Deep Learning for Automated Assessment of Left Ventricular Ejection Fraction in Pediatric Patients. J Am Soc Echocardiogr 2023; 36:482-489. [PMID: 36754100 DOI: 10.1016/j.echo.2023.01.015] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 02/10/2023]
Abstract
BACKGROUND Significant interobserver and interstudy variability occurs for left ventricular functional indices despite standardization of measurement techniques. Artificial intelligence models trained on adult echocardiograms are not likely to be applicable to a pediatric population. We present EchoNet-Peds, a video-based deep learning algorithm, which matches human expert performance of left ventricular (LV) segmentation and ejection fraction (EF). METHODS A large pediatric dataset of 4,467 echocardiograms were used to develop EchoNet-Peds. EchoNet-Peds was trained on 80% of the data for segmentation of the left ventricle and estimation of left ventricular EF. The remaining 20% was used to fine tune and validate the algorithm. RESULTS In both apical 4-chamber (A4C) and parasternal short-axis views (PSAX), EchoNet-Peds segments the left ventricle with a Dice similarity coefficient of 0.89. EchoNet-Peds estimates EF with a mean absolute error of 3.66% and can routinely identify pediatric patients with systolic dysfunction (area under the curve of 0.95). EchoNet-Peds was trained on pediatric echocardiograms and performed significantly better to estimate EF (p < 0.001) than an adult model applied to the same data. CONCLUSION Accurate, rapid automation of EF assessment and recognition of systolic dysfunction in a pediatric population are feasible using EchoNet-Peds with the potential for far-reaching clinical impact. In addition, the first large pediatric dataset of annotated echocardiograms is now publicly available for efforts to develop pediatric-specific artificial intelligence algorithms.
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Affiliation(s)
- Charitha D Reddy
- Department of Pediatrics, Division of Pediatric Cardiology, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA, USA.
| | - Leo Lopez
- Department of Pediatrics, Division of Pediatric Cardiology, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA, USA
| | - David Ouyang
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - James Y Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Bryan He
- Department of Computer Science, Stanford University, Stanford, CA, USA
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Kwan AC, Wei J, Ouyang D, Ebinger JE, Merz CNB, Berman D, Cheng S. Sex differences in contributors to coronary microvascular dysfunction. Front Cardiovasc Med 2023; 10:1085914. [PMID: 36760556 PMCID: PMC9902873 DOI: 10.3389/fcvm.2023.1085914] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/09/2023] [Indexed: 01/25/2023] Open
Abstract
Background Coronary microvascular dysfunction (CMD) has differences in prevalence and presentation between women and men; however, we have limited understanding about underlying contributors to sex differences in CMD. Myocardial perfusion reserve index (MPRI), as semi-quantitative measure of myocardial perfusion derived from cardiac magnetic resonance (CMR) imaging has been validated as a measure of CMD. We sought to understand the sex differences in the relations between the MPRI and traditional measures of cardiovascular disease by CMR. Methods A retrospective analysis of a single-center cohort of patients receiving clinical stress CMR from 2015 to 2022 was performed. Patients with calculated MPRI and no visible perfusion defects consistent with obstructive epicardial coronary disease were included. We compared associations between MPRI versus traditional cardiovascular risk factors and markers of cardiac structure/function in sex-stratified populations using univariable and multivariable regression models. Results A total of 229 patients [193 female, 36 male, median age 57 (47-67) years] were included in the analysis. In the female population, no traditional cardiovascular risk factors were associated with MPRI, whereas in the male population, diabetes (β: -0.80, p = 0.03) and hyperlipidemia (β: -0.76, p = 0.006) were both associated with reduced MPRI in multivariable models. Multivariable models revealed significant associations between reduced MPRI and increased ascending aortic diameter (β: -0.42, p = 0.005) and T1 times (β: -0.0056, p = 0.03) in the male population, and increased T1 times (β: -0.0037, p = 0.006) and LVMI (β: -0.022, p = 0.0003) in the female population. Conclusion The findings suggest different underlying pathophysiology of CMD in men versus women, with lower MPRI in male patients fitting a more "traditional" atherosclerotic profile.
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Affiliation(s)
- Alan C. Kwan
- Department of Cardiology, Smidt Heart Institute, Los Angeles, CA, United States,*Correspondence: Alan C. Kwan,
| | - Janet Wei
- Department of Cardiology, Smidt Heart Institute, Los Angeles, CA, United States,Barbara Streisand Women’s Heart Institute, Los Angeles, CA, United States
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Los Angeles, CA, United States
| | - Joseph E. Ebinger
- Department of Cardiology, Smidt Heart Institute, Los Angeles, CA, United States
| | - C. Noel Bairey Merz
- Department of Cardiology, Smidt Heart Institute, Los Angeles, CA, United States,Barbara Streisand Women’s Heart Institute, Los Angeles, CA, United States
| | - Daniel Berman
- Department of Cardiology, Smidt Heart Institute, Los Angeles, CA, United States,Department of Imaging, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Los Angeles, CA, United States
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Yuan N, Oesterle A, Botting P, Chugh S, Albert C, Ebinger J, Ouyang D. High-Throughput Assessment of Real-World Medication Effects on QT Interval Prolongation: Observational Study. JMIR Cardio 2023; 7:e41055. [PMID: 36662566 PMCID: PMC9898836 DOI: 10.2196/41055] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Drug-induced prolongation of the corrected QT interval (QTc) increases the risk for Torsades de Pointes (TdP) and sudden cardiac death. Medication effects on the QTc have been studied in controlled settings but may not be well evaluated in real-world settings where medication effects may be modulated by patient demographics and comorbidities as well as the usage of other concomitant medications. OBJECTIVE We demonstrate a new, high-throughput method leveraging electronic health records (EHRs) and the Surescripts pharmacy database to monitor real-world QTc-prolonging medication and potential interacting effects from demographics and comorbidities. METHODS We included all outpatient electrocardiograms (ECGs) from September 2008 to December 2019 at a large academic medical system, which were in sinus rhythm with a heart rate of 40-100 beats per minute, QRS duration of <120 milliseconds, and QTc of 300-700 milliseconds, determined using the Bazett formula. We used prescription information from the Surescripts pharmacy database and EHR medication lists to classify whether a patient was on a medication during an ECG. Negative control ECGs were obtained from patients not currently on the medication but who had been or would be on that medication within 1 year. We calculated the difference in mean QTc between ECGs of patients who are on and those who are off a medication and made comparisons to known medication TdP risks per the CredibleMeds.org database. Using linear regression analysis, we studied the interaction of patient-level demographics or comorbidities on medication-related QTc prolongation. RESULTS We analyzed the effects of 272 medications on 310,335 ECGs from 159,397 individuals. Medications associated with the greatest QTc prolongation were dofetilide (mean QTc difference 21.52, 95% CI 10.58-32.70 milliseconds), mexiletine (mean QTc difference 18.56, 95% CI 7.70-29.27 milliseconds), amiodarone (mean QTc difference 14.96, 95% CI 13.52-16.33 milliseconds), rifaximin (mean QTc difference 14.50, 95% CI 12.12-17.13 milliseconds), and sotalol (mean QTc difference 10.73, 95% CI 7.09-14.37 milliseconds). Several top QT prolonging medications such as rifaximin, lactulose, cinacalcet, and lenalidomide were not previously known but have plausible mechanistic explanations. Significant interactions were observed between demographics or comorbidities and QTc prolongation with many medications, such as coronary disease and amiodarone. CONCLUSIONS We demonstrate a new, high-throughput technique for monitoring real-world effects of QTc-prolonging medications from readily accessible clinical data. Using this approach, we confirmed known medications for QTc prolongation and identified potential new associations and demographic or comorbidity interactions that could supplement findings in curated databases. Our single-center results would benefit from additional verification in future multisite studies that incorporate larger numbers of patients and ECGs along with more precise medication adherence and comorbidity data.
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Affiliation(s)
- Neal Yuan
- Division of Cardiology, Department of Medicine, San Francisco Veteran Affairs Medical Center, San Francisco, CA, United States
| | - Adam Oesterle
- Division of Cardiology, Department of Medicine, San Francisco Veteran Affairs Medical Center, San Francisco, CA, United States
| | - Patrick Botting
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Sumeet Chugh
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Christine Albert
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Joseph Ebinger
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - David Ouyang
- Smidt Heart Institute, Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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Ouyang D, Hiesinger W, Langlotz C. Deep Learning Preoperative Risk Stratification. Ann Thorac Surg 2023; 115:264-265. [PMID: 35661716 DOI: 10.1016/j.athoracsur.2022.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 05/21/2022] [Indexed: 12/31/2022]
Affiliation(s)
- David Ouyang
- Division of Artificial Intelligence in Medicine, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 S San Vicente Blvd, AHSP Pavilion Ste A3100, Los Angeles, CA 90048.
| | - William Hiesinger
- Department of Cardiothoracic Surgery, Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, California
| | - Curtis Langlotz
- Department of Radiology, Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, California
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Yuan N, Kwan AC, Duffy G, Theurer J, Chen JH, Nieman K, Botting P, Dey D, Berman DS, Cheng S, Ouyang D. Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms. J Am Soc Echocardiogr 2022; 36:474-481.e3. [PMID: 36566995 PMCID: PMC10164107 DOI: 10.1016/j.echo.2022.12.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/17/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Coronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially obtainable. It remains unclear whether other widespread tests such as transthoracic echocardiograms (TTEs) can be used to predict CAC. METHODS Using a data set of 2,881 TTE videos paired with coronary calcium CTs, we trained a video-based artificial intelligence convolutional neural network to predict CAC scores from parasternal long-axis views. We evaluated the model's ability to classify patients from a held-out sample as well as an external site sample into zero CAC and high CAC (CAC ≥ 400 Agatston units) groups by receiver operating characteristic and precision-recall curves. We also investigated whether such classifications prognosticated significant differences in 1-year mortality rates by the log-rank test of Kaplan-Meier curves. RESULTS Transthoracic echocardiogram artificial intelligence models had high discriminatory abilities in predicting zero CAC (receiver operating characteristic area under the curve [AUC] = 0.81 [95% CI, 0.74-0.88], F1 score = 0.95) and high CAC (AUC = 0.74 [0.68-0.8], F1 score = 0.74). This performance was confirmed in an external test data set of 92 TTEs (AUC = 0.75 [0.65-0.85], F1 score = 0.77; and AUC = 0.85 [0.76-0.93], F1 score = 0.59, respectively). Risk stratification by TTE-predicted CAC performed similarly to CT CAC scores in prognosticating significant differences in 1-year survival in high-CAC patients (CT CAC ≥ 400 vs CT CAC < 400, P = .03; TTE-predicted CAC ≥ 400 vs TTE-predicted CAC < 400, P = .02). CONCLUSIONS A video-based deep learning model successfully used TTE videos to predict zero CAC and high CAC with high accuracy. Transthoracic echocardiography-predicted CAC prognosticated differences in 1-year survival similar to CT CAC. Deep learning of TTEs holds promise for future adjunctive coronary artery disease risk stratification to guide preventive therapies.
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Affiliation(s)
- Neal Yuan
- School of Medicine, University of California, San Francisco, California; Section of Cardiology, San Francisco Veterans Affairs Medical Center, San Francisco, California.
| | - Alan C Kwan
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Grant Duffy
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - John Theurer
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jonathan H Chen
- Department of Medicine, Stanford University, Stanford, California
| | - Koen Nieman
- Department of Medicine, Stanford University, Stanford, California; Department of Radiology, Stanford University, Stanford, California
| | - Patrick Botting
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Damini Dey
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Daniel S Berman
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - David Ouyang
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California; Department of Medicine, Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California
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Kwan AC, Ebinger JE, Wei J, Le CN, Oft JR, Zabner R, Teodorescu D, Botting PG, Navarrette J, Ouyang D, Driver M, Claggett B, Weber BN, Chen PS, Cheng S. Apparent Risks of Postural Orthostatic Tachycardia Syndrome Diagnoses After COVID-19 Vaccination and SARS-Cov-2 Infection. Nat Cardiovasc Res 2022; 1:1187-1194. [PMID: 37303827 PMCID: PMC10254901 DOI: 10.1038/s44161-022-00177-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/31/2022] [Indexed: 06/13/2023]
Abstract
Postural orthostatic tachycardia syndrome (POTS) has been previously described after SARS-CoV-2 infection; however, limited data is available on the relation of POTS with COVID-19 vaccination. Here we show in a cohort of 284,592 COVID-19 vaccinated individuals using a sequence-symmetry analysis, that the odds of POTS are higher 90 days after vaccine exposure than 90 days prior to exposure, and that the odds for POTS are higher than referent conventional primary care diagnoses, but lower than the odds of new POTS diagnosis after SARS-CoV-2 infection. Our results identify a possible association between COVID-19 vaccination and incidence of POTS. Notwithstanding the probable low incidence of POTS after COVID-19 vaccination, particularly when compared to SARS-Cov-2 post-infection odds which were five times higher, our results suggest that further studies, are needed to investigate the incidence and etiology of POTS occurring after COVID-19 vaccination.
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Affiliation(s)
- Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - Joseph E Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - Janet Wei
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - Catherine N Le
- Division of Infectious Diseases, Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA
| | - Jillian R Oft
- Division of Infectious Diseases, Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA
| | - Rachel Zabner
- Division of Infectious Diseases, Department of Medicine, Cedars Sinai Medical Center, Los Angeles, CA
| | - Debbie Teodorescu
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - Patrick G Botting
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - Jesse Navarrette
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - Matthew Driver
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - Brian Claggett
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Brittany N Weber
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA
| | - Peng-Sheng Chen
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center Los Angeles, CA
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Kwan A, Demosthenes E, Salto G, Ouyang D, Nguyen T, Nwabuo CC, Luong E, Hoang A, Osypiuk E, Stantchev P, Kim EH, Hiremath P, Li D, Vasan R, Xanthakis V, Cheng S. Cardiac microstructural alterations measured by echocardiography identify sex-specific risk for heart failure. Heart 2022; 108:1800-1806. [PMID: 35680379 PMCID: PMC9626911 DOI: 10.1136/heartjnl-2022-320876] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/16/2022] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE Established preclinical imaging assessments of heart failure (HF) risk are based on macrostructural cardiac remodelling. Given that microstructural alterations may also influence HF risk, particularly in women, we examined associations between microstructural alterations and incident HF. METHODS We studied N=2511 adult participants (mean age 65.7±8.8 years, 56% women) of the Framingham Offspring Study who were free of cardiovascular disease at baseline. We employed texture analysis of echocardiography to quantify microstructural alteration, based on the high spectrum signal intensity coefficient (HS-SIC). We examined its relations to incident HF in sex-pooled and sex-specific Cox models accounting for traditional HF risk factors and macrostructural alterations. RESULTS We observed 94 new HF events over 7.4±1.7 years. Individuals with higher HS-SIC had increased risk for incident HF (HR 1.67 per 1-SD in HS-SIC, 95% CI 1.31 to 2.13; p<0.0001). Adjusting for age and antihypertensive medication use, this association was significant in women (p=0.02) but not men (p=0.78). Adjusting for traditional risk factors (including body mass index, total/high-density lipoprotein cholesterol, blood pressure traits, diabetes and smoking) attenuated the association in women (HR 1.30, p=0.07), with mediation of HF risk by the HS-SIC seen for a majority of these risk factors. However, the HS-SIC association with HF in women remained significant after adjusting for relative wall thickness (representing macrostructure alteration) in addition to these risk factors (HR 1.47, p=0.02). CONCLUSIONS Cardiac microstructural alterations are associated with elevated risk for HF, particularly in women. Microstructural alteration may identify sex-specific pathways by which individuals progress from risk factors to clinical HF.
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Affiliation(s)
- Alan Kwan
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | | | - Gerran Salto
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Framingham Heart Study, Framingham, Massachusetts, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Trevor Nguyen
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Chike C Nwabuo
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Ronin Institute, Montclair, New Jersey, USA
| | - Eric Luong
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Amy Hoang
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ewa Osypiuk
- Framingham Heart Study, Framingham, Massachusetts, USA
| | | | - Elizabeth H Kim
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Pranoti Hiremath
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Debiao Li
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ramachandran Vasan
- Framingham Heart Study, Framingham, Massachusetts, USA
- Departments of Medicine, Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA
| | - Vanessa Xanthakis
- Framingham Heart Study, Framingham, Massachusetts, USA
- Departments of Medicine, Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Framingham Heart Study, Framingham, Massachusetts, USA
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Kwan AC, Sun N, Driver M, Botting P, Navarrette J, Ouyang D, Hussain SK, Noureddin M, Li D, Ebinger JE, Berman DS, Cheng S. Cardiovascular and hepatic disease associations by magnetic resonance imaging: A retrospective cohort study. Front Cardiovasc Med 2022; 9:1009474. [PMID: 36324754 PMCID: PMC9618632 DOI: 10.3389/fcvm.2022.1009474] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022] Open
Abstract
Background Hepatic disease is linked to cardiovascular events but the independent association between hepatic and cardiovascular disease remains unclear, given shared risk factors. Methods This was a retrospective study of consecutive patients with a clinical cardiac MRI (CMR) and a serological marker of hepatic fibrosis, the FIB-4 score, within one year of clinical imaging. We assessed the relations between FIB-4 scores grouped based on prior literature: low (< 1.3), moderate (1.3–3.25), and high (>3.25), and abnormalities detected by comprehensive CMR grouped into 4 domains: cardiac structure (end diastolic volumes, atrial dimensions, wall thickness); cardiac function (ejection fractions, wall motion abnormalities, cardiac output); vascular structure (ascending aortic and pulmonary arterial sizes); and cardiac composition (late gadolinium enhancement, T1 and T2 times). We used Poisson regression to examine the association between the conventionally defined FIB-4 category (low <1.3, moderate 1.3–3.25, and high >3.25) and any CMR abnormality while adjusting for demographics and traditional cardiovascular risk factors. Results Of the 1668 patients studied (mean age: 55.971 ± 7.28, 901 [54%] male), 85.9% had ≥1 cardiac abnormality with increasing prevalence seen within the low (82.0%) to moderate (88.8%) to high (92.3%) FIB-4 categories. Multivariable analyses demonstrated the presence of any cardiac abnormality was significantly associated with having a high-range FIB-4 (prevalence ratio 1.07, 95% CI: 1.01–1.13); notably, the presence of functional cardiac abnormalities were associated with being in the high FIB-4 range (1.41, 1.21–1.65) and any vascular abnormalities with being in the moderate FIB-4 range (1.22, 1.01–1.47). Conclusions Elevated FIB-4 was associated with cardiac functional and vascular abnormalities even after adjustment for shared risk factors in a cohort of patients with clinically referred CMR. These CMR findings indicate that cardiovascular abnormalities exist in the presence of subclinical hepatic fibrosis, irrespective of shared risk factors, underscoring the need for further studies of the heart-liver axis.
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Affiliation(s)
- Alan C. Kwan
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
- *Correspondence: Alan C. Kwan
| | - Nancy Sun
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Matthew Driver
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Patrick Botting
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Jesse Navarrette
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - David Ouyang
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Shehnaz K. Hussain
- Department of Public Health Sciences, School of Medicine and Comprehensive Cancer Center, University of California, Davis, CA, United States
| | - Mazen Noureddin
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Debiao Li
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Joseph E. Ebinger
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Daniel S. Berman
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Susan Cheng
- Departments of Cardiology, Internal Medicine, Biomedical Sciences, and Imaging, Smidt Heart Institute and Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, CA, United States
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Chen H, Ouyang D, Baykaner T, Jamal F, Cheng P, Rhee JW. Artificial intelligence applications in cardio-oncology: Leveraging high dimensional cardiovascular data. Front Cardiovasc Med 2022; 9:941148. [PMID: 35958422 PMCID: PMC9360492 DOI: 10.3389/fcvm.2022.941148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022] Open
Abstract
Growing evidence suggests a wide spectrum of potential cardiovascular complications following cancer therapies, leading to an urgent need for better risk-stratifying and disease screening in patients undergoing oncological treatment. As many cancer patients undergo frequent surveillance through imaging as well as other diagnostic testing, there is a wealth of information that can be utilized to assess one's risk for cardiovascular complications of cancer therapies. Over the past decade, there have been remarkable advances in applying artificial intelligence (AI) to analyze cardiovascular data obtained from electrocardiograms, echocardiograms, computed tomography, and cardiac magnetic resonance imaging to detect early signs or future risk of cardiovascular diseases. Studies have shown AI-guided cardiovascular image analysis can accurately, reliably and inexpensively identify and quantify cardiovascular risk, leading to better detection of at-risk or disease features, which may open preventive and therapeutic opportunities in cardio-oncology. In this perspective, we discuss the potential for the use of AI in analyzing cardiovascular data to identify cancer patients at risk for cardiovascular complications early in treatment which would allow for rapid intervention to prevent adverse cardiovascular outcomes.
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Affiliation(s)
- Haidee Chen
- City of Hope National Medical Center, Duarte, CA, United States
| | - David Ouyang
- Cedars Sinai Medical Center, Los Angeles, CA, United States
| | - Tina Baykaner
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA, United States
| | - Faizi Jamal
- City of Hope National Medical Center, Duarte, CA, United States
| | - Paul Cheng
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA, United States
- Paul Cheng
| | - June-Wha Rhee
- City of Hope National Medical Center, Duarte, CA, United States
- *Correspondence: June-Wha Rhee
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Ebinger JE, Driver M, Ouyang D, Botting P, Ji H, Rashid MA, Blyler CA, Bello NA, Rader F, Niiranen TJ, Albert CM, Cheng S. Variability independent of mean blood pressure as a real-world measure of cardiovascular risk. EClinicalMedicine 2022; 48:101442. [PMID: 35706499 PMCID: PMC9112125 DOI: 10.1016/j.eclinm.2022.101442] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND Individual-level blood pressure (BP) variability, independent of mean BP levels, has been associated with increased risk for cardiovascular events in cohort studies and clinical trials using standardized BP measurements. The extent to which BP variability relates to cardiovascular risk in the real-world clinical practice setting is unclear. We sought to determine if BP variability in clinical practice is associated with adverse cardiovascular outcomes using clinically generated data from the electronic health record (EHR). METHODS We identified 42,482 patients followed continuously at a single academic medical center in Southern California between 2013 and 2019 and calculated their systolic and diastolic BP variability independent of the mean (VIM) over the first 3 years of the study period. We then performed multivariable Cox proportional hazards regression to examine the association between VIM and both composite and individual outcomes of interest (incident myocardial infarction, heart failure, stroke, and death). FINDINGS Both systolic (HR, 95% CI 1.22, 1.17-1.28) and diastolic VIM (1.24, 1.19-1.30) were positively associated with the composite outcome, as well as all individual outcome measures. These findings were robust to stratification by age, sex and clinical comorbidities. In sensitivity analyses using a time-shifted follow-up period, VIM remained significantly associated with the composite outcome for both systolic (1.15, 1.11-1.20) and diastolic (1.18, 1.13-1.22) values. INTERPRETATION VIM derived from clinically generated data remains associated with adverse cardiovascular outcomes and represents a risk marker beyond mean BP, including in important demographic and clinical subgroups. The demonstrated prognostic ability of VIM derived from non-standardized BP readings indicates the utility of this measure for risk stratification in a real-world practice setting, although residual confounding from unmeasured variables cannot be excluded. FUNDING This study was funded in part by National Institutes of Health grants R01-HL134168, R01-HL131532, R01-HL143227, R01-HL142983, U54-AG065141; R01-HL153382, K23-HL136853, K23-HL153888, and K99-HL157421; China Scholarship Council grant 201806260086; Academy of Finland (Grant no: 321351); Emil Aaltonen Foundation; Finnish Foundation for Cardiovascular Research.
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Affiliation(s)
- Joseph E. Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Corresponding auhtor.
| | - Matthew Driver
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Patrick Botting
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Hongwei Ji
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Mohamad A. Rashid
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ciantel A. Blyler
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Natalie A. Bello
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Cardiology, Columbia University Medical Center, New York, NY, USA
| | - Florian Rader
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Teemu J. Niiranen
- University of Turku, Turku University Hospital, Turku, Finland
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, Turku, Finland
| | - Christine M. Albert
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Ouyang D, Cheng S. Extracting More From Less: A New Frontier for High-Throughput Clinical Phenotyping. Circ Cardiovasc Qual Outcomes 2022; 15:e009055. [PMID: 35477258 DOI: 10.1161/circoutcomes.122.009055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- David Ouyang
- Department of Cardiology, Smidt Heart Institute (D.O., S.C.), Cedars-Sinai Medical Center, Los Angeles, CA.,Division of Artificial Intelligence in Medicine (D.O.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute (D.O., S.C.), Cedars-Sinai Medical Center, Los Angeles, CA
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Soto JT, Weston Hughes J, Sanchez PA, Perez M, Ouyang D, Ashley EA. Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy . Eur Heart J Digit Health 2022; 3:380-389. [PMID: 36712167 PMCID: PMC9707995 DOI: 10.1093/ehjdh/ztac033] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 04/25/2022] [Indexed: 02/01/2023]
Abstract
Aims Determining the aetiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that an artificial intelligence method based joint interpretation of 12-lead electrocardiograms and echocardiogram videos could augment physician interpretation. Methods and results We chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage of electronic health record derived clinical blood pressure measurements and diagnostic consensus (often including molecular testing) among physicians in an HCM centre of excellence. Using more than 18 000 combined instances of electrocardiograms and echocardiograms from 2728 patients, we developed LVH-fusion. On held-out test data, LVH-fusion achieved an F1-score of 0.71 in predicting HCM, and 0.96 in predicting HTN. In head-to-head comparison with human readers LVH-fusion had higher sensitivity and specificity rates than its human counterparts. Finally, we use explainability techniques to investigate local and global features that positively and negatively impact LVH-fusion prediction estimates providing confirmation from unsupervised analysis the diagnostic power of lateral T-wave inversion on the ECG and proximal septal hypertrophy on the echocardiogram for HCM. Conclusion These results show that deep learning can provide effective physician augmentation in the face of a common diagnostic dilemma with far reaching implications for the prevention of sudden cardiac death.
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Affiliation(s)
| | | | - Pablo Amador Sanchez
- Department of Medicine, Division of Cardiology, Stanford University, Stanford, California, USA
| | - Marco Perez
- Department of Medicine, Division of Cardiology, Stanford University, Stanford, California, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, USA,Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, USA
| | - Euan A Ashley
- Corresponding author. Tel: 650 498-4900, Fax: 650 498-7452,
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Popescu DM, Shade JK, Lai C, Aronis KN, Ouyang D, Popescu D, Popescu DR, Popescu DC, Kadish AH, Albert CM, Popescu D, Wu KC, Trayanova NA. DH-575-04 ARRHYTHMIC SUDDEN DEATH (SCDA) SURVIVAL PREDICTION USING DEEP LEARNING (DL) ANALYSIS OF CONTRAST-ENHANCED CARDIAC MAGNETIC RESONANCE IMAGING (LGE-CMR). Heart Rhythm 2022. [DOI: 10.1016/j.hrthm.2022.03.582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Kwan AC, Salto G, Nguyen TT, Kim EH, Luong E, Hiremath P, Ouyang D, Ebinger JE, Li D, Berman DS, Kittleson MM, Kobashigawa JA, Patel JK, Cheng S. Cardiac microstructural alterations in immune-inflammatory myocardial disease: a retrospective case-control study. Cardiovasc Ultrasound 2022; 20:9. [PMID: 35369883 PMCID: PMC8978375 DOI: 10.1186/s12947-022-00279-0] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 03/28/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Immune-inflammatory myocardial disease contributes to multiple chronic cardiac processes, but access to non-invasive screening is limited. We have previously developed a method of echocardiographic texture analysis, called the high-spectrum signal intensity coefficient (HS-SIC) which assesses myocardial microstructure and previously associated with myocardial fibrosis. We aimed to determine whether this echocardiographic texture analysis of cardiac microstructure can identify inflammatory cardiac disease in the clinical setting. METHODS We conducted a retrospective case-control study of 318 patients with distinct clinical myocardial pathologies and 20 healthy controls. Populations included myocarditis, atypical chest pain/palpitations, STEMI, severe aortic stenosis, acute COVID infection, amyloidosis, and cardiac transplantation with acute rejection, without current rejection but with prior rejection, and with no history of rejection. We assessed the HS-SIC's ability to differentiate between a broader diversity of clinical groups and healthy controls. We used Kruskal-Wallis tests to compare HS-SIC values measured in each of the clinical populations with those in the healthy control group and compared HS-SIC values between the subgroups of cardiac transplantation rejection status. RESULTS For the total sample of N = 338, the mean age was 49.6 ± 20.9 years and 50% were women. The mean ± standard error of the mean of HS-SIC were: 0.668 ± 0.074 for controls, 0.552 ± 0.049 for atypical chest pain/palpitations, 0.425 ± 0.058 for myocarditis, 0.881 ± 0.129 for STEMI, 1.116 ± 0.196 for severe aortic stenosis, 0.904 ± 0.116 for acute COVID, and 0.698 ± 0.103 for amyloidosis. Among cardiac transplant recipients, HS-SIC values were 0.478 ± 0.999 for active rejection, 0.594 ± 0.091 for prior rejection, and 1.191 ± 0.442 for never rejection. We observed significant differences in HS-SIC between controls and myocarditis (P = 0.0014), active rejection (P = 0.0076), and atypical chest pain or palpitations (P = 0.0014); as well as between transplant patients with active rejection and those without current or prior rejection (P = 0.031). CONCLUSIONS An echocardiographic method can be used to characterize tissue signatures of microstructural changes across a spectrum of cardiac disease including immune-inflammatory conditions.
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Affiliation(s)
- Alan C. Kwan
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Gerran Salto
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA ,grid.62560.370000 0004 0378 8294Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, MA USA ,grid.510954.c0000 0004 0444 3861Framingham Heart Study, Framingham, MA USA
| | - Trevor-Trung Nguyen
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Elizabeth H. Kim
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Eric Luong
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Pranoti Hiremath
- grid.411935.b0000 0001 2192 2723Division of Cardiology, Johns Hopkins Hospital, Baltimore, MD USA
| | - David Ouyang
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Joseph E. Ebinger
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Debiao Li
- grid.50956.3f0000 0001 2152 9905Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Daniel S. Berman
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA ,grid.50956.3f0000 0001 2152 9905Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Michelle M. Kittleson
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Jon A. Kobashigawa
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Jignesh K. Patel
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA
| | - Susan Cheng
- grid.50956.3f0000 0001 2152 9905Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA USA ,grid.62560.370000 0004 0378 8294Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, MA USA ,grid.510954.c0000 0004 0444 3861Framingham Heart Study, Framingham, MA USA
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Duffy G, Cheng PP, Yuan N, He B, Kwan AC, Shun-Shin MJ, Alexander KM, Ebinger J, Lungren MP, Rader F, Liang DH, Schnittger I, Ashley EA, Zou JY, Patel J, Witteles R, Cheng S, Ouyang D. High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy With Cardiovascular Deep Learning. JAMA Cardiol 2022; 7:386-395. [PMID: 35195663 PMCID: PMC9008505 DOI: 10.1001/jamacardio.2021.6059] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
IMPORTANCE Early detection and characterization of increased left ventricular (LV) wall thickness can markedly impact patient care but is limited by under-recognition of hypertrophy, measurement error and variability, and difficulty differentiating causes of increased wall thickness, such as hypertrophy, cardiomyopathy, and cardiac amyloidosis. OBJECTIVE To assess the accuracy of a deep learning workflow in quantifying ventricular hypertrophy and predicting the cause of increased LV wall thickness. DESIGN, SETTINGS, AND PARTICIPANTS This cohort study included physician-curated cohorts from the Stanford Amyloid Center and Cedars-Sinai Medical Center (CSMC) Advanced Heart Disease Clinic for cardiac amyloidosis and the Stanford Center for Inherited Cardiovascular Disease and the CSMC Hypertrophic Cardiomyopathy Clinic for hypertrophic cardiomyopathy from January 1, 2008, to December 31, 2020. The deep learning algorithm was trained and tested on retrospectively obtained independent echocardiogram videos from Stanford Healthcare, CSMC, and the Unity Imaging Collaborative. MAIN OUTCOMES AND MEASURES The main outcome was the accuracy of the deep learning algorithm in measuring left ventricular dimensions and identifying patients with increased LV wall thickness diagnosed with hypertrophic cardiomyopathy and cardiac amyloidosis. RESULTS The study included 23 745 patients: 12 001 from Stanford Health Care (6509 [54.2%] female; mean [SD] age, 61.6 [17.4] years) and 1309 from CSMC (808 [61.7%] female; mean [SD] age, 62.8 [17.2] years) with parasternal long-axis videos and 8084 from Stanford Health Care (4201 [54.0%] female; mean [SD] age, 69.1 [16.8] years) and 2351 from CSMS (6509 [54.2%] female; mean [SD] age, 69.6 [14.7] years) with apical 4-chamber videos. The deep learning algorithm accurately measured intraventricular wall thickness (mean absolute error [MAE], 1.2 mm; 95% CI, 1.1-1.3 mm), LV diameter (MAE, 2.4 mm; 95% CI, 2.2-2.6 mm), and posterior wall thickness (MAE, 1.4 mm; 95% CI, 1.2-1.5 mm) and classified cardiac amyloidosis (area under the curve [AUC], 0.83) and hypertrophic cardiomyopathy (AUC, 0.98) separately from other causes of LV hypertrophy. In external data sets from independent domestic and international health care systems, the deep learning algorithm accurately quantified ventricular parameters (domestic: R2, 0.96; international: R2, 0.90). For the domestic data set, the MAE was 1.7 mm (95% CI, 1.6-1.8 mm) for intraventricular septum thickness, 3.8 mm (95% CI, 3.5-4.0 mm) for LV internal dimension, and 1.8 mm (95% CI, 1.7-2.0 mm) for LV posterior wall thickness. For the international data set, the MAE was 1.7 mm (95% CI, 1.5-2.0 mm) for intraventricular septum thickness, 2.9 mm (95% CI, 2.4-3.3 mm) for LV internal dimension, and 2.3 mm (95% CI, 1.9-2.7 mm) for LV posterior wall thickness. The deep learning algorithm accurately detected cardiac amyloidosis (AUC, 0.79) and hypertrophic cardiomyopathy (AUC, 0.89) in the domestic external validation site. CONCLUSIONS AND RELEVANCE In this cohort study, the deep learning model accurately identified subtle changes in LV wall geometric measurements and the causes of hypertrophy. Unlike with human experts, the deep learning workflow is fully automated, allowing for reproducible, precise measurements, and may provide a foundation for precision diagnosis of cardiac hypertrophy.
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Affiliation(s)
- Grant Duffy
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Paul P. Cheng
- Department of Medicine, Division of Cardiology, Stanford University, Stanford, California
| | - Neal Yuan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Bryan He
- Department of Computer Science, Stanford University, Stanford, California
| | - Alan C. Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Matthew J. Shun-Shin
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Kevin M. Alexander
- Department of Medicine, Division of Cardiology, Stanford University, Stanford, California
| | - Joseph Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | | | - Florian Rader
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - David H. Liang
- Department of Medicine, Division of Cardiology, Stanford University, Stanford, California
| | - Ingela Schnittger
- Department of Medicine, Division of Cardiology, Stanford University, Stanford, California
| | - Euan A. Ashley
- Department of Medicine, Division of Cardiology, Stanford University, Stanford, California
| | - James Y. Zou
- Department of Computer Science, Stanford University, Stanford, California,Department of Biomedical Data Science, Stanford University, Stanford, California
| | - Jignesh Patel
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Ronald Witteles
- Department of Medicine, Division of Cardiology, Stanford University, Stanford, California
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California,Division of Artificial Intelligence in Medicine, Cedars-Sinai Medical Center, Los Angeles, California
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Popescu DM, Shade JK, Lai C, Aronis KN, Ouyang D, Moorthy MV, Cook NR, Lee DC, Kadish A, Albert CM, Wu KC, Maggioni M, Trayanova NA. Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart. Nat Cardiovasc Res 2022; 1:334-343. [PMID: 35464150 DOI: 10.1038/s44161-022-00041-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. Here, we develop a novel deep learning (DL) approach that blends neural networks and survival analysis to predict patient-specific survival curves from contrast-enhanced cardiac magnetic resonance images and clinical covariates for patients with ischemic heart disease. The DL-predicted survival curves offer accurate predictions at times up to 10 years and allow for estimation of uncertainty in predictions. The performance of this learning architecture was evaluated on multi-center internal validation data and tested on an independent test set, achieving concordance index of 0.83 and 0.74, and 10-year integrated Brier score of 0.12 and 0.14. We demonstrate that our DL approach with only raw cardiac images as input outperforms standard survival models constructed using clinical covariates. This technology has the potential to transform clinical decision-making by offering accurate and generalizable predictions of patient-specific survival probabilities of arrhythmic death over time.
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Affiliation(s)
- Dan M Popescu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA
| | - Julie K Shade
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA
| | - Changxin Lai
- Johns Hopkins University School of Medicine, Department of Biomedical Engineering, Baltimore, 21224, USA
| | - Konstantinos N Aronis
- University of Pittsburgh Medical Center, Heart and Vascular Institute, Pittsburgh, 15237, USA
| | - David Ouyang
- Cedar-Sinai Medical Center, Department of Cardiology, Los Angeles, 90048, USA
| | - M Vinayaga Moorthy
- Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, USA
| | - Nancy R Cook
- Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, USA
| | - Daniel C Lee
- Northwestern University, Feinberg School of Medicine, Chicago, 60611, USA
| | - Alan Kadish
- Touro College and University System, Valhalla, 10595, USA
| | - Christine M Albert
- Cedar-Sinai Medical Center, Department of Cardiology, Los Angeles, 90048, USA
| | - Katherine C Wu
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA.,Johns Hopkins University School of Medicine, Department of Medicine, Division of Cardiology, Baltimore, 21224, USA
| | - Mauro Maggioni
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA.,Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, 21224, USA
| | - Natalia A Trayanova
- Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, 21224, USA.,Johns Hopkins University School of Medicine, Department of Biomedical Engineering, Baltimore, 21224, USA
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49
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Abstract
It is well known that cardiovascular disease manifests differently in women and men. The underlying causes of these differences during the aging lifespan are less well understood. Sex differences in cardiac and vascular phenotypes are seen in childhood and tend to track along distinct trajectories related to dimorphism in genetic factors as well as response to risk exposures and hormonal changes during the life course. These differences underlie sex-specific variation in cardiovascular events later in life, including myocardial infarction, heart failure, ischemic stroke, and peripheral vascular disease. With respect to cardiac phenotypes, females have intrinsically smaller body size-adjusted cardiac volumes and they tend to experience greater age-related wall thickening and myocardial stiffening with aging. With respect to vascular phenotypes, sexual dimorphism in both physiology and pathophysiology are also seen, including overt differences in blood pressure trajectories. The majority of sex differences in myocardial and vascular alterations that manifest with aging seem to follow relatively consistent trajectories from the very early to the very later stages of life. This review aims to synthesize recent cardiovascular aging-related research to highlight clinically relevant studies in diverse female and male populations that can inform approaches to improving the diagnosis, management, and prognosis of cardiovascular disease risks in the aging population at large.
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Affiliation(s)
- Hongwei Ji
- Department of Cardiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, China,Department of Cardiology, Shanghai Tenth People’s Hospital, Tongji University, Shanghai, China
| | - Alan C. Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Melanie Chen
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Joseph E. Ebinger
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan P. Bell
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Teemu Niiranen
- Department of Internal Medicine, University of Turku, Turku, Finland,Department of Public Health Solutions, Finnish Institute for Health and Welfare, Turku, Finland
| | - Natalie A. Bello
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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50
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Kwan AC, Wei J, Lee BP, Luong E, Salto G, Nguyen TT, Botting PG, Liu Y, Ouyang D, Ebinger JE, Li D, Noureddin M, Thomson L, Berman DS, Merz CNB, Cheng S. Subclinical hepatic fibrosis is associated with coronary microvascular dysfunction by myocardial perfusion reserve index: a retrospective cohort study. Int J Cardiovasc Imaging 2022; 38:10.1007/s10554-022-02546-7. [PMID: 35107770 PMCID: PMC9343468 DOI: 10.1007/s10554-022-02546-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 01/27/2022] [Indexed: 11/05/2022]
Abstract
The heart-liver axis is of growing importance. Previous studies have identified independent association of liver dysfunction and fibrosis with adverse cardiac outcomes, but mechanistic pathways remain uncertain. We sought to understand the relations between the degree of hepatic fibrosis identified by the Fibrosis-4 (Fib-4) risk score and comprehensive cardiac MRI (CMR) measures of subclinical cardiac disease. We conducted a retrospective single-center cohort study of patients between 2011 and 2021. We identified consecutive patients who underwent a comprehensive CMR imaging protocol including contrast enhanced with stress/rest perfusion, and lacked pre-existing cardiovascular disease or perfusion abnormalities on CMR. We examined the association of hepatic fibrosis, using the Fib-4 score, with subclinical cardiac disease on CMR while adjusting for cardiometabolic traits. Given known associations of hepatic disease and coronary microvascular dysfunction, we prioritized analyses with the myocardial perfusion reserve index (MPRI), a marker of coronary microvascular function. Of the 66 patients in our study cohort, 54 were female (81%) and the mean age was 53.7 ± 15.3 years. We found that higher Fib-4 was associated with reduction in the MPRI (β [SE] - 1.12 [0.46], P = 0.02), after adjusting for cardiometabolic risk factors. Importantly, Fib-4 was not significantly associated with any other CMR phenotypes including measures of cardiac remodeling, inflammation, fibrosis, or dysfunction. We found evidence that hepatic fibrosis associated with coronary microvascular dysfunction, in the absence of overt associations with any other subclinical cardiac disease measures. These findings highlight a potentially important precursor pathway leading to development of subsequent heart-liver disease.
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Affiliation(s)
- Alan C Kwan
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA.
| | - Janet Wei
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Brian P Lee
- Department of Medicine, Keck School of Medicine of USC, Los Angeles, USA
| | - Eric Luong
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Gerran Salto
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Trevor-Trung Nguyen
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Patrick G Botting
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Yunxian Liu
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - David Ouyang
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Joseph E Ebinger
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Debiao Li
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Mazen Noureddin
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Louise Thomson
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Daniel S Berman
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - C Noel Bairey Merz
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
| | - Susan Cheng
- Division of Digestive and Liver Diseases, and Department of Imaging, Barbra Streisand Women's Heart Center, Biomedical Imaging Research Institute, Smidt Heart Institute Department of Cardiology, Cedars Sinai Medical Center, 127 S San Vicente Blvd #A3600, Los Angeles, CA, 90048, USA
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