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Naghavi M, Yankelevitz D, Reeves AP, Budoff MJ, Li D, Atlas K, Zhang C, Atlas TL, Lirette S, Wasserthal J, Roy SK, Henschke C, Wong ND, Defilippi C, Heckbert SR, Greenland P. AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CAC TM) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis. J Cardiovasc Comput Tomogr 2024; 18:383-391. [PMID: 38653606 PMCID: PMC11216863 DOI: 10.1016/j.jcct.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/04/2024] [Accepted: 04/13/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. METHODS We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CACTM) to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 s per CAC scan. We used the 5-year outcomes data for incident atrial fibrillation (AF) and assessed discrimination using the time-dependent area under the curve (AUC) of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP. The mean follow-up time to an AF event was 2.9 ± 1.4 years. RESULTS At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p < 0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p < 0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p < 0.01). CONCLUSION AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.
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Affiliation(s)
| | | | - Anthony P Reeves
- Department of Computer Engineering, Cornell University, Ithaca, NY, USA
| | | | - Dong Li
- The Lundquist Institute, Torrance, CA, USA
| | | | | | | | | | | | - Sion K Roy
- The Lundquist Institute, Torrance, CA, USA
| | | | - Nathan D Wong
- Heart Disease Prevention Program, Division of Cardiology, University of California Irvine, CA, USA
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Chen Y, Xiao X, He Q, Yao RQ, Zhang GY, Fan JR, Xue CX, Huang L. Knowledge mapping of digital medicine in cardiovascular diseases from 2004 to 2022: A bibliometric analysis. Heliyon 2024; 10:e25318. [PMID: 38356571 PMCID: PMC10864893 DOI: 10.1016/j.heliyon.2024.e25318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/22/2023] [Accepted: 01/24/2024] [Indexed: 02/16/2024] Open
Abstract
Objective To review studies on digital medicine in cardiovascular diseases (CVD), discuss its development process, knowledge structure and research hotspots, and provide a perspective for researchers in this field. Methods The relevant literature in recent 20 years (January 2004 to October 2022) were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace was used to demonstrate our knowledge of keywords, co-references and speculative frontiers. VOSviewer was used to chart the contributions of authors, institutions and countries and incorporates their link strength into the table. Results A total of 5265 English articles in set timespan were included. The number of publications increased steadily annually. The United States (US) produced the highest number of publications, followed by England. Most publications were from Harvard Medicine School, followed by Massachusetts General Hospital and Brigham Women's Hospital. The most authoritative academic journal was JMIR mHealth and uHealth. Noseworthy PA may have the highest influence in this intersected field with the highest number of citations and total link strength. The utilization of wearable mobile devices in the context of CVD, encompassing the identification of risk factors, diagnosis and prevention of diseases, as well as early intervention and remote management of diseases, has been widely acknowledged as a knowledge base and an area of current interest. To investigate the impact of various digital medicine interventions on chronic care and assess their clinical effectiveness, examine the potential of machine learning (ML) in delivering clinical care for atrial fibrillation (AF) and identifying early disease risk factors, as well as explore the development of disease prediction models using neural networks (NNs), ML and unsupervised learning in CVD prognosis, may emerge as future trends and areas of focus. Conclusion Recently, there has been a significant surge of interest in the investigation of digital medicine in CVD. This initial bibliometric study offers a comprehensive analysis of the research landscape pertaining to digital medicine in CVD, thereby furnishing related scholars with a dependable reference to facilitate further progress in this domain.
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Affiliation(s)
- Ying Chen
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
| | - Xiang Xiao
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
| | - Qing He
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Rui-Qi Yao
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Gao-Yu Zhang
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Jia-Rong Fan
- Beijing University of Chinese Medicine, Beijing, 100029, China
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Chong-Xiang Xue
- Beijing University of Chinese Medicine, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, 100053, China
| | - Li Huang
- Department of Integrative Cardiology, China-Japan Friendship Hospital, Beijing, 100029, China
- National Integrative Medicine Center for Cardiovascular Diseases, Beijing, 100029, China
- National Center for Integrative Medicine, Beijing, 100029, China
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Naghavi M, Yankelevitz D, Reeves AP, Budoff MJ, Li D, Atlas KC, Zhang C, Atlas TL, Lirette S, Wasserthal J, Henschke C, Defilippi C, Heckbert SR, Greenland P. AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.22.24301384. [PMID: 38343816 PMCID: PMC10854349 DOI: 10.1101/2024.01.22.24301384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Background Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. Methods We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC), taking on average 21 seconds per CAC scan, to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). We used the 5-year outcomes data for incident atrial fibrillation (AF) and compared the time-dependent AUC of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP (BNP). The mean follow-up time to an AF event was 2.9±1.4 years. Results At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF or BNP at year 1 (0.836, 0.742, 0.742), year 2 (0.842, 0.807,0.772), and year 3 (0.811, 0.785, 0.745) (p<0.02), but similar for year 4 (0.785, 0.769, 0.725) and year 5 (0.781, 0.767, 0.734) respectively (p>0.05). AI-CAC LA volume significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CAC score (0.74, 0.49, 0.53, 0.39, 0.44), CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and BNP (0.68, 0.44, 0.42, 0.30, 0.37) respectively (p<0.01). Conclusion AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and BNP.
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Affiliation(s)
| | | | | | | | - Dong Li
- The Lundquist Institute, 1124 W Carson St, Torrance, CA 90502
| | | | | | - Thomas L. Atlas
- Tustin Teleradiology, 13422 Newport Ave Suite I, Tustin, CA 92780
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Wolder LD, Graff C, Baadsgaard KH, Langgaard ML, Polcwiartek C, Ji-Young Lee C, Skov MW, Torp-Pedersen C, Friedman DJ, Atwater B, Overvad TF, Nielsen JB, Hansen SM, Sogaard P, Kragholm KH. Electrocardiographic P terminal force in lead V1, its components, and the association with stroke and atrial fibrillation or flutter. Heart Rhythm 2023; 20:354-362. [PMID: 36435351 DOI: 10.1016/j.hrthm.2022.11.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND The electrocardiographic (ECG) marker P terminal force V1 (PTFV1) is generally perceived as a marker of left atrial pathology and has been associated with atrial fibrillation or flutter (AF). OBJECTIVE The purpose of this study was to determine the association between PTFV1 components (duration and amplitude) and incident AF and stroke/transient ischemic attack (TIA). METHODS The study included patients with an ECG recorded at the Copenhagen General Practitioners Laboratory in 2001 to 2011. PTFV1 ≥4 mV·ms was considered abnormal. Patients with abnormal PTFV1 were stratified into tertiles based on duration (PTDV1) and amplitude (PTAV1) values. Cox regressions adjusted for age, sex, and relevant comorbidities were used to investigate associations between abnormal PTFV1 components and AF and stroke/TIA. RESULTS Of 267,636 patients, 5803 had AF and 18,176 had stroke/TIA (follow-up 6.5 years). Abnormal PTFV1 was present in 44,549 subjects (16.7%) and was associated with an increased risk of AF and stroke/TIA. Among patients with abnormal PTFV1, the highest tertile of PTDV1 (78-97 ms) was associated with the highest risk of AF (hazard ratio [HR] 1.37; 95% confidence interval [CI] 1.23-1.52) and highest risk of stroke/TIA (HR 1.13; 95% CI 1.05 -1.20). For PTAV1, the highest tertile (78-126 μV) conferred the highest risk of AF and stroke/TIA (HR 1.20; 95% CI 1.09-1.32; and HR 1.21; 95% CI 1.14-1.25, respectively). CONCLUSION Abnormal PTFV1 was associated with an increased risk of AF and stroke/TIA. Increasing PTDV1 showed a dose-response relationship with the development of AF and stroke/TIA, whereas the association between PTAV1 and AF was less apparent.
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Affiliation(s)
- Lecia Dixen Wolder
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark.
| | - Claus Graff
- Heart Centre and Clinical Institute, Aalborg University Hospital, Aalborg, Denmark; Department of Health Science and Technology, Aalborg University Hospital, Aalborg, Denmark
| | | | | | - Christoffer Polcwiartek
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark; Unit of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark
| | | | - Morten Wagner Skov
- Department of Cardiology, Sjaelland University Hospital, Roskilde, Denmark
| | - Christian Torp-Pedersen
- Department of Health Science and Technology, Aalborg University Hospital, Aalborg, Denmark; Unit of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark
| | | | - Brett Atwater
- Division of Cardiac Electrophysiology, Duke University Medical Center, Durham, North Carolina
| | - Thure Filskov Overvad
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark; Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Faculty of Health, Aalborg University, Aalborg, Denmark; Department of Clinical Pharmacology, Aalborg University Hospital, Denmark
| | - Jonas Bille Nielsen
- Laboratory for Molecular Cardiology, Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark; K.G. Jebsen Center for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | | | - Peter Sogaard
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark; Heart Centre and Clinical Institute, Aalborg University Hospital, Aalborg, Denmark
| | - Kristian H Kragholm
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark; Unit of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark
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