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Liu CM, Chen WS, Chang SL, Hsieh YC, Hsu YH, Chang HX, Lin YJ, Lo LW, Hu YF, Chung FP, Chao TF, Tuan TC, Liao JN, Lin CY, Chang TY, Kuo L, Wu CI, Wu MH, Chen CK, Chang YY, Shiu YC, Lu HHS, Chen SA. Use of artificial intelligence and I-Score for prediction of recurrence before catheter ablation of atrial fibrillation. Int J Cardiol 2024; 402:131851. [PMID: 38360099 DOI: 10.1016/j.ijcard.2024.131851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/14/2024] [Accepted: 02/10/2024] [Indexed: 02/17/2024]
Abstract
BACKGROUND Based solely on pre-ablation characteristics, previous risk scores have demonstrated variable predictive performance. This study aimed to predict the recurrence of AF after catheter ablation by using artificial intelligence (AI)-enabled pre-ablation computed tomography (PVCT) images and pre-ablation clinical data. METHODS A total of 638 drug-refractory paroxysmal atrial fibrillation (AF) patients undergone ablation were recruited. For model training, we used left atria (LA) acquired from pre-ablation PVCT slices (126,288 images). A total of 29 clinical variables were collected before ablation, including baseline characteristics, medical histories, laboratory results, transthoracic echocardiographic parameters, and 3D reconstructed LA volumes. The I-Score was applied to select variables for model training. For the prediction of one-year AF recurrence, PVCT deep-learning and clinical variable machine-learning models were developed. We then applied machine learning to ensemble the PVCT and clinical variable models. RESULTS The PVCT model achieved an AUC of 0.63 in the test set. Various combinations of clinical variables selected by I-Score can yield an AUC of 0.72, which is significantly better than all variables or features selected by nonparametric statistics (AUCs of 0.66 to 0.69). The ensemble model (PVCT images and clinical variables) significantly improved predictive performance up to an AUC of 0.76 (sensitivity of 86.7% and specificity of 51.0%). CONCLUSIONS Before ablation, AI-enabled PVCT combined with I-Score features was applicable in predicting recurrence in paroxysmal AF patients. Based on all possible predictors, the I-Score is capable of identifying the most influential combination.
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Affiliation(s)
- Chih-Min Liu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Shiang Chen
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Shih-Lin Chang
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Cheng Hsieh
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yuan-Heng Hsu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Hao-Xiang Chang
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yenn-Jiang Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Li-Wei Lo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Feng Hu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Fa-Po Chung
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Tze-Fan Chao
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ta-Chuan Tuan
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jo-Nan Liao
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chin-Yu Lin
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ting-Yung Chang
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Ling Kuo
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-I Wu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Mei-Han Wu
- Department of Medical Imaging, Diagnostic Radiology, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Chun-Ku Chen
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ying-Yueh Chang
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Yang-Che Shiu
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan
| | - Henry Horng-Shing Lu
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA.
| | - Shih-Ann Chen
- Heart Rhythm Center, Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taiwan; Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan; National Chung Hsing University, Taichung, Taiwan
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2
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Pereyra Pietri M, Farina JM, Mahmoud AK, Scalia IG, Galasso F, Killian ME, Suppah M, Kenyon CR, Koepke LM, Padang R, Chao CJ, Sweeney JP, Fortuin FD, Eleid MF, Sell-Dottin KA, Steidley DE, Scott LR, Fonseca R, Lopez-Jimenez F, Attia ZI, Dispenzieri A, Grogan M, Rosenthal JL, Arsanjani R, Ayoub C. The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:295-302. [PMID: 38774378 PMCID: PMC11104461 DOI: 10.1093/ehjdh/ztae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/15/2024] [Accepted: 03/05/2024] [Indexed: 05/24/2024]
Abstract
Aims Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01-1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01-1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13-2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.
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Affiliation(s)
- Milagros Pereyra Pietri
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Juan M Farina
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Ahmed K Mahmoud
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Isabel G Scalia
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Francesca Galasso
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Michael E Killian
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Mustafa Suppah
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Courtney R Kenyon
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Laura M Koepke
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Ratnasari Padang
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Chieh-Ju Chao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - John P Sweeney
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - F David Fortuin
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Mackram F Eleid
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - David E Steidley
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Luis R Scott
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Rafael Fonseca
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | | | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Martha Grogan
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Julie L Rosenthal
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Reza Arsanjani
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Chadi Ayoub
- Department of Cardiovascular Medicine, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
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3
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Kamada H, Kawasoe S, Kubozono T, Ninomiya Y, Enokizono K, Yoshimoto I, Iriki Y, Ikeda Y, Miyata M, Miyahara H, Tokushige K, Ohishi M. Simple risk scoring using sinus rhythm electrocardiograms predicts the incidence of atrial fibrillation in the general population. Sci Rep 2024; 14:9628. [PMID: 38671212 PMCID: PMC11053076 DOI: 10.1038/s41598-024-60219-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 04/19/2024] [Indexed: 04/28/2024] Open
Abstract
Atrial fibrillation (AF) is an arrhythmic disease. Prediction of AF development in healthy individuals is important before serious complications occur. We aimed to develop a risk prediction score for future AF using participants' data, including electrocardiogram (ECG) measurements and information such as age and sex. We included 88,907 Japanese participants, aged 30-69 years, who were randomly assigned to derivation and validation cohorts in a ratio of 1:1. We performed multivariate logistic regression analysis and obtained the standardised beta coefficient of relevant factors and assigned scores to them. We created a score based on prognostic factors for AF to predict its occurrence after five years and applied it to validation cohorts to assess its reproducibility. The risk score ranged from 0 to 17, consisting of age, sex, PR prolongation, QT corrected for heart rate prolongation, left ventricular hypertrophy, premature atrial contraction, and left axis deviation. The area under the curve was 0.75 for the derivation cohort and 0.73 for the validation cohort. The incidence of new-onset AF reached over 2% at 10 points of the risk score in both cohorts. Thus, in this study, we showed the possibility of predicting new-onset AF using ECG findings and simple information.
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Affiliation(s)
- Hiroyuki Kamada
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Shin Kawasoe
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Takuro Kubozono
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan.
| | - Yuichi Ninomiya
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Kei Enokizono
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Issei Yoshimoto
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Yasuhisa Iriki
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Yoshiyuki Ikeda
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | - Masaaki Miyata
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
| | | | | | - Mitsuru Ohishi
- Department of Cardiovascular Medicine and Hypertension, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8520, Japan
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4
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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:S1934-5925(24)00079-0. [PMID: 38653606 DOI: 10.1016/j.jcct.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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5
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [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] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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6
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Christopoulos G, Attia ZI, Achenbach SJ, Rabe KG, Call TG, Ding W, Leis JF, Muchtar E, Kenderian SS, Wang Y, Hampel PJ, Koehler AB, Kay NE, Kapoor P, Slager SL, Shanafelt TD, Noseworthy PA, Friedman PA, Herrmann J, Parikh SA. Artificial Intelligence Electrocardiography to Predict Atrial Fibrillation in Patients With Chronic Lymphocytic Leukemia. JACC CardioOncol 2024; 6:251-263. [PMID: 38774001 PMCID: PMC11103028 DOI: 10.1016/j.jaccao.2024.02.006] [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: 08/14/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 05/24/2024] Open
Abstract
Background The use of an artificial intelligence electrocardiography (AI-ECG) algorithm has demonstrated its reliability in predicting the risk of atrial fibrillation (AF) within the general population. Objectives This study aimed to determine the effectiveness of the AI-ECG score in identifying patients with chronic lymphocytic leukemia (CLL) who are at high risk of developing AF. Methods We estimated the probability of AF based on AI-ECG among patients with CLL extracted from the Mayo Clinic CLL database. Additionally, we computed the Mayo Clinic CLL AF risk score and determined its ability to predict AF. Results Among 754 newly diagnosed patients with CLL, 71.4% were male (median age = 69 years). The median baseline AI-ECG score was 0.02 (range = 0-0.93), with a value ≥0.1 indicating high risk. Over a median follow-up of 5.8 years, the estimated 10-year cumulative risk of AF was 26.1%. Patients with an AI-ECG score of ≥0.1 had a significantly higher risk of AF (HR: 3.9; 95% CI: 2.6-5.7; P < 0.001). This heightened risk remained significant (HR: 2.5; 95% CI: 1.6-3.9; P < 0.001) even after adjusting for the Mayo CLL AF risk score, heart failure, chronic kidney disease, and CLL therapy. In a second cohort of CLL patients treated with a Bruton tyrosine kinase inhibitor (n = 220), a pretreatment AI-ECG score ≥0.1 showed a nonsignificant increase in the risk of AF (HR: 1.7; 95% CI: 0.8-3.6; P = 0.19). Conclusions An AI-ECG algorithm, in conjunction with the Mayo CLL AF risk score, can predict the risk of AF in patients with newly diagnosed CLL. Additional studies are needed to determine the role of AI-ECG in predicting AF risk in CLL patients treated with a Bruton tyrosine kinase inhibitor.
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Affiliation(s)
| | - Zachi I. Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Sara J. Achenbach
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Kari G. Rabe
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Timothy G. Call
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | - Wei Ding
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | - Jose F. Leis
- Division of Hematology and Medical Oncology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Eli Muchtar
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Yucai Wang
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul J. Hampel
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Neil E. Kay
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | - Prashant Kapoor
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan L. Slager
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Division of Hematology, Mayo Clinic, Rochester, Minnesota, USA
| | - Tait D. Shanafelt
- Department of Medicine, Stanford University, Palo Alto, California, USA
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul A. Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Joerg Herrmann
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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7
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Neyazi M, Bremer JP, Knorr MS, Gross S, Brederecke J, Schweingruber N, Csengeri D, Schrage B, Bahls M, Friedrich N, Zeller T, Felix S, Blankenberg S, Dörr M, Vollmer M, Schnabel RB. Deep learning-based NT-proBNP prediction from the ECG for risk assessment in the community. Clin Chem Lab Med 2024; 62:740-752. [PMID: 37982681 DOI: 10.1515/cclm-2023-0743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/20/2023] [Indexed: 11/21/2023]
Abstract
OBJECTIVES The biomarker N-terminal pro B-type natriuretic peptide (NT-proBNP) has predictive value for identifying individuals at risk for cardiovascular disease (CVD). However, it is not widely used for screening in the general population, potentially due to financial and operational reasons. This study aims to develop a deep-learning model as an efficient means to reliably identify individuals at risk for CVD by predicting serum levels of NT-proBNP from the ECG. METHODS A deep convolutional neural network was developed using the population-based cohort study Hamburg City Health Study (HCHS, n=8,253, 50.9 % women). External validation was performed in two independent population-based cohorts (SHIP-START, n=3,002, 52.1 % women, and SHIP-TREND, n=3,819, 51.2 % women). Assessment of model performance was conducted using Pearson correlation (R) and area under the receiver operating characteristics curve (AUROC). RESULTS NT-proBNP was predictable from the ECG (R, 0.566 [HCHS], 0.642 [SHIP-START-0], 0.655 [SHIP-TREND-0]). Across cohorts, predicted NT-proBNP (pNT-proBNP) showed good discriminatory ability for prevalent and incident heart failure (HF) (baseline: AUROC 0.795 [HCHS], 0.816 [SHIP-START-0], 0.783 [SHIP-TREND-0]; first follow-up: 0.669 [SHIP-START-1, 5 years], 0.689 [SHIP-TREND-1, 7.3 years]), comparable to the discriminatory value of measured NT-proBNP. pNT-proBNP also demonstrated comparable results for other incident CVD, including atrial fibrillation, stroke, myocardial infarction, and cardiovascular death. CONCLUSIONS Deep learning ECG algorithms can predict NT-proBNP concentrations with high diagnostic and predictive value for HF and other major CVD and may be used in the community to identify individuals at risk. Long-standing experience with NT-proBNP can increase acceptance of such deep learning models in clinical practice.
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Affiliation(s)
- Meraj Neyazi
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Hamburg, Germany
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jan P Bremer
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Marius S Knorr
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Stefan Gross
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Jan Brederecke
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nils Schweingruber
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dora Csengeri
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Benedikt Schrage
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Martin Bahls
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Nele Friedrich
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Institute for Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Tanja Zeller
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Stephan Felix
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Blankenberg
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Hamburg, Germany
| | - Marcus Dörr
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany
| | - Marcus Vollmer
- Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany
| | - Renate B Schnabel
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Hamburg/Kiel/Luebeck, Hamburg, Germany
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8
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Gupta U, Paluru N, Nankani D, Kulkarni K, Awasthi N. A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms. Heliyon 2024; 10:e26787. [PMID: 38562492 PMCID: PMC10982903 DOI: 10.1016/j.heliyon.2024.e26787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.
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Affiliation(s)
- Utkarsh Gupta
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Naveen Paluru
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Deepankar Nankani
- Department of Computer Science and Engineering, Indian Institute of Technology, Guwahati, Assam, 781039, India
| | - Kanchan Kulkarni
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, Pessac, Bordeaux, F-33000, France
- University of Bordeaux, INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, F-33000, France
| | - Navchetan Awasthi
- Faculty of Science, Mathematics and Computer Science, Informatics Institute, University of Amsterdam, Amsterdam, 1090 GH, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, 1081 HV, the Netherlands
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9
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Suzuki S, Motogi J, Umemoto T, Hirota N, Nakai H, Matsuzawa W, Takayanagi T, Hyodo A, Satoh K, Arita T, Yagi N, Kishi M, Semba H, Kano H, Matsuno S, Kato Y, Otsuka T, Hori T, Matsuhama M, Iida M, Uejima T, Oikawa Y, Yajima J, Yamashita T. Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography. Circ Rep 2024; 6:46-54. [PMID: 38464990 PMCID: PMC10920024 DOI: 10.1253/circrep.cr-23-0068] [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: 08/20/2023] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 03/12/2024] Open
Abstract
Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.
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Affiliation(s)
- Shinya Suzuki
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | | | | | - Naomi Hirota
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Hiroshi Nakai
- Information System Division, The Cardiovascular Institute Tokyo Japan
| | | | | | | | | | - Takuto Arita
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Naoharu Yagi
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Mikio Kishi
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Hiroaki Semba
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Hiroto Kano
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Shunsuke Matsuno
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Yuko Kato
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Takayuki Otsuka
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Takayuki Hori
- Department of Cardiovascular Surgery, The Cardiovascular Institute Tokyo Japan
| | - Minoru Matsuhama
- Department of Cardiovascular Surgery, The Cardiovascular Institute Tokyo Japan
| | - Mitsuru Iida
- Department of Cardiovascular Surgery, The Cardiovascular Institute Tokyo Japan
| | - Tokuhisa Uejima
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Yuji Oikawa
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Junji Yajima
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
| | - Takeshi Yamashita
- Department of Cardiovascular Medicine, The Cardiovascular Institute Tokyo Japan
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10
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König S, Hohenstein S, Nitsche A, Pellissier V, Leiner J, Stellmacher L, Hindricks G, Bollmann A. Artificial intelligence-based identification of left ventricular systolic dysfunction from 12-lead electrocardiograms: external validation and advanced application of an existing model. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:144-151. [PMID: 38505486 PMCID: PMC10944686 DOI: 10.1093/ehjdh/ztad081] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/06/2023] [Accepted: 12/14/2023] [Indexed: 03/21/2024]
Abstract
Aims The diagnostic application of artificial intelligence (AI)-based models to detect cardiovascular diseases from electrocardiograms (ECGs) evolves, and promising results were reported. However, external validation is not available for all published algorithms. The aim of this study was to validate an existing algorithm for the detection of left ventricular systolic dysfunction (LVSD) from 12-lead ECGs. Methods and results Patients with digitalized data pairs of 12-lead ECGs and echocardiography (at intervals of ≤7 days) were retrospectively selected from the Heart Center Leipzig ECG and electronic medical records databases. A previously developed AI-based model was applied to ECGs and calculated probabilities for LVSD. The area under the receiver operating characteristic curve (AUROC) was computed overall and in cohorts stratified for baseline and ECG characteristics. Repeated echocardiography studies recorded ≥3 months after index diagnostics were used for follow-up (FU) analysis. At baseline, 42 291 ECG-echocardiography pairs were analysed, and AUROC for LVSD detection was 0.88. Sensitivity and specificity were 82% and 77% for the optimal LVSD probability cut-off based on Youden's J. AUROCs were lower in ECG subgroups with tachycardia, atrial fibrillation, and wide QRS complexes. In patients without LVSD at baseline and available FU, model-generated high probability for LVSD was associated with a four-fold increased risk of developing LVSD during FU. Conclusion We provide the external validation of an existing AI-based ECG-analysing model for the detection of LVSD with robust performance metrics. The association of false positive LVSD screenings at baseline with a deterioration of ventricular function during FU deserves a further evaluation in prospective trials.
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Affiliation(s)
- Sebastian König
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Sven Hohenstein
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Anne Nitsche
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Vincent Pellissier
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Johannes Leiner
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Lars Stellmacher
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
| | - Andreas Bollmann
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Strümpellstr. 39, 04289 Leipzig, Germany
- Helios Health Institute, Real World Evidence & Health Technology Assessment, Schwanebecker Chaussee 50, 13125 Berlin, Germany
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11
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Kawamura Y, Vafaei Sadr A, Abedi V, Zand R. Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection? J Clin Med 2024; 13:1313. [PMID: 38592138 PMCID: PMC10932407 DOI: 10.3390/jcm13051313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 04/10/2024] Open
Abstract
(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.
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Affiliation(s)
- Yuki Kawamura
- School of Clinical Medicine, University of Cambridge, Cambridge CB3 0SP, UK
| | - Alireza Vafaei Sadr
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA (V.A.)
| | - Vida Abedi
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA (V.A.)
| | - Ramin Zand
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
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12
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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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13
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Di Costanzo A, Spaccarotella CAM, Esposito G, Indolfi C. An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review. J Clin Med 2024; 13:1033. [PMID: 38398346 PMCID: PMC10889404 DOI: 10.3390/jcm13041033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.
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Affiliation(s)
- Assunta Di Costanzo
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy
| | - Carmen Anna Maria Spaccarotella
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80126 Naples, Italy; (C.A.M.S.)
| | - Giovanni Esposito
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80126 Naples, Italy; (C.A.M.S.)
| | - Ciro Indolfi
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy
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14
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Cai Y, Cai YQ, Tang LY, Wang YH, Gong M, Jing TC, Li HJ, Li-Ling J, Hu W, Yin Z, Gong DX, Zhang GW. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Med 2024; 22:56. [PMID: 38317226 PMCID: PMC10845808 DOI: 10.1186/s12916-024-03273-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND A comprehensive overview of artificial intelligence (AI) for cardiovascular disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external validation are lacking. This systematic review aims to identify, describe, and appraise AI-Ms of CVD prediction in the general and special populations and develop a new independent validation score (IVS) for AI-Ms replicability evaluation. METHODS PubMed, Web of Science, Embase, and IEEE library were searched up to July 2021. Data extraction and analysis were performed for the populations, distribution, predictors, algorithms, etc. The risk of bias was evaluated with the prediction risk of bias assessment tool (PROBAST). Subsequently, we designed IVS for model replicability evaluation with five steps in five items, including transparency of algorithms, performance of models, feasibility of reproduction, risk of reproduction, and clinical implication, respectively. The review is registered in PROSPERO (No. CRD42021271789). RESULTS In 20,887 screened references, 79 articles (82.5% in 2017-2021) were included, which contained 114 datasets (67 in Europe and North America, but 0 in Africa). We identified 486 AI-Ms, of which the majority were in development (n = 380), but none of them had undergone independent external validation. A total of 66 idiographic algorithms were found; however, 36.4% were used only once and only 39.4% over three times. A large number of different predictors (range 5-52,000, median 21) and large-span sample size (range 80-3,660,000, median 4466) were observed. All models were at high risk of bias according to PROBAST, primarily due to the incorrect use of statistical methods. IVS analysis confirmed only 10 models as "recommended"; however, 281 and 187 were "not recommended" and "warning," respectively. CONCLUSION AI has led the digital revolution in the field of CVD prediction, but is still in the early stage of development as the defects of research design, report, and evaluation systems. The IVS we developed may contribute to independent external validation and the development of this field.
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Affiliation(s)
- Yue Cai
- China Medical University, Shenyang, 110122, China
| | - Yu-Qing Cai
- China Medical University, Shenyang, 110122, China
| | - Li-Ying Tang
- China Medical University, Shenyang, 110122, China
| | - Yi-Han Wang
- China Medical University, Shenyang, 110122, China
| | - Mengchun Gong
- Digital Health China Co. Ltd, Beijing, 100089, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co. Ltd., Shenyang, 110001, China
- Enduring Medicine Smart Innovation Research Institute, Shenyang, 110001, China
| | - Jesse Li-Ling
- Institute of Genetic Medicine, School of Life Science, State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610065, China
| | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, 610017, China
| | - Zhihua Yin
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, China.
| | - Da-Xin Gong
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
| | - Guang-Wei Zhang
- Smart Hospital Management Department, the First Hospital of China Medical University, Shenyang, 110001, China.
- The Internet Hospital Branch of the Chinese Research Hospital Association, Beijing, 100006, China.
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15
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Khurshid S, Churchill TW, Diamant N, Di Achille P, Reeder C, Singh P, Friedman SF, Wasfy MM, Alba GA, Maron BA, Systrom DM, Wertheim BM, Ellinor PT, Ho JE, Baggish AL, Batra P, Lubitz SA, Guseh JS. Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise. Eur J Prev Cardiol 2024; 31:252-262. [PMID: 37798122 PMCID: PMC10809171 DOI: 10.1093/eurjpc/zwad321] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/14/2023] [Accepted: 09/29/2023] [Indexed: 10/07/2023]
Abstract
AIMS To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET). METHODS AND RESULTS V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]. CONCLUSION Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification.
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Affiliation(s)
- Shaan Khurshid
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA
- Demoulas Center for Cardiac Arrhythmias, Division of Cardiology, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA 02142, USA
| | - Timothy W Churchill
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA
- Cardiovascular Performance Program, Division of Cardiology, Mass General Sports Medicine, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Christopher Reeder
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Samuel F Friedman
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Meagan M Wasfy
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA
- Cardiovascular Performance Program, Division of Cardiology, Mass General Sports Medicine, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA
| | - George A Alba
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- University of Maryland, Institute for Health Computing, Bethesda, MD, USA
| | - David M Systrom
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Bradley M Wertheim
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA
- Demoulas Center for Cardiac Arrhythmias, Division of Cardiology, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA 02142, USA
| | - Jennifer E Ho
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, CardioVascular Institute, Boston, MA, USA
| | - Aaron L Baggish
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA
- Cardiovascular Performance Program, Division of Cardiology, Mass General Sports Medicine, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA
- Département Coeur-Vaisseaux, Le Centre Hospitalier Universitaire Vaudois (CHUV), Institut des Sciences du Sport, Université de Lausanne, Écublens, Vaud, Switzerland
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Steven A Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA
- Demoulas Center for Cardiac Arrhythmias, Division of Cardiology, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, 415 Main Street, Cambridge, MA 02142, USA
| | - J Sawalla Guseh
- Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street Suite 3201, Boston, MA 02114, USA
- Cardiovascular Performance Program, Division of Cardiology, Mass General Sports Medicine, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114, USA
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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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17
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Raileanu G, de Jong JSSG. Electrocardiogram Interpretation Using Artificial Intelligence: Diagnosis of Cardiac and Extracardiac Pathologic Conditions. How Far Has Machine Learning Reached? Curr Probl Cardiol 2024; 49:102097. [PMID: 37739276 DOI: 10.1016/j.cpcardiol.2023.102097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is already widely used in different fields of medicine, making possible the integration of the paraclinical exams with the clinical findings in patients, for a more accurate and rapid diagnosis and treatment decision. The electrocardiogram remains one of the most important, fastest, cheapest, and noninvasive methods of diagnosis in cardiology, despite the rapid development and progression of the technology. Even if studied a long time ago, it still has a lot of less understood features that, with a better understanding, can give more clues to a correct and prompt diagnosis in a short time. The use of AI in the interpretation of the ECG improved the accuracy and the time to diagnosis in different cardiovascular diseases, and more than this, explaining the decision to make AI diagnosis improved the human understanding of the different features of the ECG that might be considered for a more accurate diagnosis. The purpose of this article is to provide an overview of the most recently published articles about the use of AI in ECG interpretation.
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Affiliation(s)
- Gabriela Raileanu
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands.
| | - Jonas S S G de Jong
- Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, The Netherlands
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18
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Rooney SR, Kaufman R, Murugan R, Kashani KB, Pinsky MR, Al-Zaiti S, Dubrawski A, Clermont G, Miller JK. Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings. J Electrocardiol 2023; 81:111-116. [PMID: 37683575 PMCID: PMC10841237 DOI: 10.1016/j.jelectrocard.2023.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/03/2023] [Accepted: 08/12/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND Despite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care. METHODS We utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF. AF episodes were defined as ≥5 min of sustained AF. Three deep learning models incorporating convolutional and transformer layers were created for forecasting, with two models focusing on the predictive nature of sinus rhythm segments and AF epochs separately preceding an AF episode, and one model utilizing all preceding waveform as input. Cross-validated performance was evaluated using area under time-dependent receiver operating characteristic curves (AUC(t)) at 7.5-, 15-, 30-, and 60-min lead times, precision-recall curves, and imminent AF risk trajectories. RESULTS There were 367 AF episodes from 84 ECG recordings. All models showed average risk trajectory divergence of those with an AF episode from those without ∼15 min before the episode. Highest AUC was associated with the sinus rhythm model [AUC = 0.74; 7.5-min lead time], though the model using all preceding waveform data had similar performance and higher AUCs at longer lead times. CONCLUSIONS In this proof-of-concept study, we demonstrated the potential utility of neural networks to forecast the onset of AF in long-term ECG recordings with a clinically relevant lead time. External validation in larger cohorts is required before deploying these models clinically.
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Affiliation(s)
- Sydney R Rooney
- Department of Pediatrics, Children's Hospital of Pittsburgh, 4401 Penn Ave, Pittsburgh, PA 15224, USA.
| | - Roman Kaufman
- Auton Lab, Carnegie Mellon University, Newell Simon Hall 3128, Forbes Ave, Pittsburgh, PA 15213, USA.
| | - Raghavan Murugan
- Program for Critical Care Nephrology, Department of Critical Care Medicine. University of Pittsburgh School of Medicine, 3550 Terrace Street, Alan Magee Scaife Hall, Suite 600, Pittsburgh, PA 15213, USA.
| | - Kianoush B Kashani
- Division of Nephrology and Hypertension, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, 200 First St. SW, Rochester, MN 55905, USA.
| | - Michael R Pinsky
- Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street Alan Magee Scaife Hall, Suite 600, Pittsburgh, PA, 15213 Pittsburgh, PA, USA.
| | - Salah Al-Zaiti
- Department of Acute & Tertiary Care, University of Pittsburgh Medical Center, School of Nursing, 3500 Victoria Street, Victoria Building, Pittsburgh, PA 15261, USA.
| | - Artur Dubrawski
- Auton Lab, Carnegie Mellon University, Newell Simon Hall 3128, Forbes Ave, Pittsburgh, PA 15213, USA.
| | - Gilles Clermont
- Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street Alan Magee Scaife Hall, Suite 600, Pittsburgh, PA, 15213 Pittsburgh, PA, USA.
| | - J Kyle Miller
- Auton Lab, Carnegie Mellon University, Newell Simon Hall 3128, Forbes Ave, Pittsburgh, PA 15213, USA.
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Habineza T, Ribeiro AH, Gedon D, Behar JA, Ribeiro ALP, Schön TB. End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks. J Electrocardiol 2023; 81:193-200. [PMID: 37774529 DOI: 10.1016/j.jelectrocard.2023.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/01/2023] [Accepted: 09/17/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovas- cular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil. METHODS We used the CODE cohort to develop and test a model for AF risk prediction for individual patients from the raw ECG recordings without the use of additional digital biomarkers. The cohort is a collection of ECG recordings and annotations by the Telehealth Network of Minas Gerais, in Brazil. A convolutional neural network based on a residual network architecture was implemented to produce class probabilities for the classification of AF. The probabilities were used to develop a Cox proportional hazards model and a Kaplan-Meier model to carry out survival analysis. Hence, our model is able to perform risk prediction for the development of AF in patients without the condition. RESULTS The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being >0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have >85% chance of remaining AF free up until after seven years. CONCLUSION We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision- making and patient management processes.
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Affiliation(s)
| | | | - Daniel Gedon
- Department of Information Technology, Uppsala University, Sweden
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion-Israel Institute of Technology, Israel
| | - Antonio Luiz P Ribeiro
- Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais-UFMG, Brazil
| | - Thomas B Schön
- Department of Information Technology, Uppsala University, Sweden
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Kim Y, Lee M, Yoon J, Kim Y, Min H, Cho H, Park J, Shin T. Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods. Diagnostics (Basel) 2023; 13:2849. [PMID: 37685387 PMCID: PMC10487044 DOI: 10.3390/diagnostics13172849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/29/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models. Experiments were conducted using two types of inputs: a combination of discrete heartbeats extracted from 12-lead ECG and an entire 12-lead ECG signal of 10 s. This study utilized 326,904 ECG signals from 134,447 patients and categorized them into three groups: true-normal sinus rhythm (T-NSR), atrial fibrillation-normal sinus rhythm (AF-NSR), and clinically important arrhythmia-normal sinus rhythm (CIA-NSR). The T-NSR group comprised patients with at least three normal rhythms in a year and no atrial fibrillation or arrhythmias history. Clinically important arrhythmia included atrial fibrillation, atrial flutter, atrial premature contraction, atrial tachycardia, ventricular premature contraction, ventricular tachycardia, right and left bundle branch block, and atrioventricular block over the second degree. The AF-NSR group included normal sinus rhythm paired with atrial fibrillation or atrial flutter within 14 days, and the CIA-NSR group comprised normal sinus rhythm paired with CIA occurring within 14 days. Three deep learning models, ResNet-18, LSTM, and Transformer-based models, were utilized to distinguish T-NSR from AF-NSR and T-NSR from CIA-NSR. The experiments demonstrated the potential of using discrete heartbeats in predicting future arrhythmia and atrial fibrillation incidences extracted from 12-lead electrocardiogram (ECG) signals alone, without any additional patient information. The analysis reveals that these discrete heartbeats contain subtle patterns that deep learning models can identify. Focusing on discrete heartbeats may lead to more timely and accurate diagnoses of these conditions, improving patient outcomes and enabling automated diagnosis using ECG signals as a biomarker.
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Affiliation(s)
- Yehyun Kim
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
| | - Myeonggyu Lee
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
| | - Jaeung Yoon
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
| | - Yeji Kim
- Department of Cardiology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea;
| | - Hyunseok Min
- Tomocube Inc., Daejeon 34141, Republic of Korea; (H.M.); (H.C.)
| | - Hyungjoo Cho
- Tomocube Inc., Daejeon 34141, Republic of Korea; (H.M.); (H.C.)
| | - Junbeom Park
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
- Department of Cardiology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea;
| | - Taeyoung Shin
- Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea; (Y.K.); (M.L.); (J.Y.)
- Department of Urology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea
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21
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Nishimura T, Senoo K, Makino M, Munakata J, Tomura N, Shimoo S, Iwakoshi H, Shiraishi H, Matoba S. Prediction model for the new onset of atrial fibrillation combining features of 24-hour Holter electrocardiogram with 12-lead electrocardiogram. IJC HEART & VASCULATURE 2023; 47:101245. [PMID: 37521520 PMCID: PMC10371783 DOI: 10.1016/j.ijcha.2023.101245] [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: 04/23/2023] [Revised: 06/06/2023] [Accepted: 07/06/2023] [Indexed: 08/01/2023]
Abstract
Background Several factors that predict new-onset atrial fibrillation (AF) have been investigated using the 24-hour Holter electrocardiogram (ECG) and 12-lead ECG; however, these have been based on each test independently. The aim of this study was to combine findings from the two tests to create a comprehensive, easy-to-use score and to examine its validity. Methods and Results A total of 502 patients underwent 24-hour Holter ECG and 12-lead ECG were followed up for 6.2 ± 3.5 years, and 66 patients developed new-onset AF. Multivariate Cox regression analyses revealed that total number of supraventricular extrasystoles (SVEs) ≥ 100 beats/day and SVE's longest run ≥ 3 beats on 24-hour Holter ECG and PR interval ≥ 185 ms, amplitude ratio of P wave (aVR/V1) < 1.0 and amplitude of RV5 + SV1 ≥ 2.2 mV on 12-lead ECG were significant independent predictors for developing AF (all p < 0.01). Using these cut-off points, the PAAFS (acronym for risk factors) score was constructed by adding one point for each parameter if the patient met each of the criteria. The area under the curve (AUC) of the PAAFS score was 0.80, compared to the AUCs of 24-hour Holter ECG-only factors (0.73) and 12-lead ECG-only factors (0.72), indicating an improvement in score. The annual incidence of AF for each PAAFS score were 0.0%, 0.2%, 0.7%, 1.9%, 5.6%, and 11.1%/year for scores 0 to 5, respectively. Conclusion The PAAFS score, which combines findings from 24-hour Holter ECG and 12-lead ECG, was superior to 24-hour Holter ECG and 12-lead ECG alone in predictive accuracy for new-onset AF.
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Affiliation(s)
- Tetsuro Nishimura
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Keitaro Senoo
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
- Department of Cardiac Arrhythmia Research and Innovation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Masahiro Makino
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jun Munakata
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Nobunari Tomura
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Satoru Shimoo
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hibiki Iwakoshi
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hirokazu Shiraishi
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
- Department of Cardiac Arrhythmia Research and Innovation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Satoaki Matoba
- Department of Cardiovascular Medicine, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
- Department of Cardiac Arrhythmia Research and Innovation, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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22
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Gruwez H, Barthels M, Haemers P, Verbrugge FH, Dhont S, Meekers E, Wouters F, Nuyens D, Pison L, Vandervoort P, Pierlet N. Detecting Paroxysmal Atrial Fibrillation From an Electrocardiogram in Sinus Rhythm: External Validation of the AI Approach. JACC Clin Electrophysiol 2023; 9:1771-1782. [PMID: 37354171 DOI: 10.1016/j.jacep.2023.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/29/2023] [Accepted: 04/10/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Atrial fibrillation (AF) may occur asymptomatically and can be diagnosed only with electrocardiography (ECG) while the arrhythmia is present. OBJECTIVES The aim of this study was to independently validate the approach of using artificial intelligence (AI) to identify underlying paroxysmal AF from a 12-lead ECG in sinus rhythm (SR). METHODS An AI algorithm was trained to identify patients with underlying paroxysmal AF, using electrocardiographic data from all in- and outpatients from a single center with at least 1 ECG in SR. For patients without AF, all ECGs in SR were included. For patients with AF, all ECGs in SR starting 31 days before the first AF event were included. The patients were randomly allocated to training, internal validation, and testing datasets in a 7:1:2 ratio. In a secondary analysis, the AF prevalence of the testing group was modified. Additionally, the performance of the algorithm was validated at an external hospital. RESULTS The dataset consisted of 494,042 ECGs in SR from 142,310 patients. Testing the model on the first ECG of each patient (AF prevalence 9.0%) resulted in accuracy of 78.1% (95% CI: 77.6%-78.5%), area under the receiver-operating characteristic curve of 0.87 (95% CI: 0.86-0.87), and area under the precision recall curve (AUPRC) of 0.48 (95% CI: 0.46-0.50). In a low-risk group (AF prevalence 3%), the AUPRC decreased to 0.21 (95% CI: 0.18-0.24). In a high-risk group (AF prevalence 30%), the AUPRC increased to 0.76 (95% CI: 0.75-0.78). This performance was robust when validated in an external hospital. CONCLUSIONS The approach of using an AI-enabled electrocardiographic algorithm for the identification of patients with underlying paroxysmal AF from ECGs in SR was independently validated.
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Affiliation(s)
- Henri Gruwez
- Department of Cardiology, Hospital East-Limburg, Genk, Belgium; Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium; Doctoral School of Medicine and Life Science, Hasselt University, Hasselt, Belgium
| | - Myrte Barthels
- Data Science Department, Hospital East-Limburg, Genk, Belgium
| | - Peter Haemers
- Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
| | - Frederik H Verbrugge
- Centre for Cardiovascular Diseases, University Hospital Brussels, Jette, Belgium; Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sebastiaan Dhont
- Department of Cardiology, Hospital East-Limburg, Genk, Belgium; Doctoral School of Medicine and Life Science, Hasselt University, Hasselt, Belgium
| | - Evelyne Meekers
- Department of Cardiology, Hospital East-Limburg, Genk, Belgium; Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium; Doctoral School of Medicine and Life Science, Hasselt University, Hasselt, Belgium
| | - Femke Wouters
- LCRC, Mobile Health Unit, Hasselt University, Hasselt, Belgium; Future Health Department, Hospital East-Limburg, Genk, Belgium
| | - Dieter Nuyens
- Department of Cardiology, Hospital East-Limburg, Genk, Belgium
| | - Laurent Pison
- Department of Cardiology, Hospital East-Limburg, Genk, Belgium
| | | | - Noëlla Pierlet
- Doctoral School of Medicine and Life Science, Hasselt University, Hasselt, Belgium; Data Science Department, Hospital East-Limburg, Genk, Belgium.
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23
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Wang X, Khurshid S, Choi SH, Friedman S, Weng LC, Reeder C, Pirruccello JP, Singh P, Lau ES, Venn R, Diamant N, Di Achille P, Philippakis A, Anderson CD, Ho JE, Ellinor PT, Batra P, Lubitz SA. Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:340-349. [PMID: 37278238 PMCID: PMC10524395 DOI: 10.1161/circgen.122.003808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 04/11/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates. METHODS We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model. RESULTS In the ECG-AI GWAS, we identified 3 signals (P<5×10-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. We also identified 2 novel loci near the genes VGLL2 and EXT1. In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model. CONCLUSIONS Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.
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Affiliation(s)
- Xin Wang
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Shaan Khurshid
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Samuel Friedman
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Lu-Chen Weng
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | | | - James P. Pirruccello
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Pulkit Singh
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Emily S. Lau
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Rachael Venn
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Nate Diamant
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Paolo Di Achille
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Anthony Philippakis
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
- Eric & Wendy Schmidt Ctr, The Broad Institute of MIT & Harvard, Cambridge
| | - Christopher D. Anderson
- Dept of Neurology, Brigham and Women’s Hospital
- Ctr for Genomic Medicine, Massachusetts General Hospital, Boston
- Henry & Allison McCance Ctr for Brain Health, Massachusetts General Hospital, Boston
| | - Jennifer E. Ho
- CardioVascular Institute & Division of Cardiology, Dept of Medicine, Beth Israel Deaconess Medical Ctr, Boston, MA
| | - Patrick T. Ellinor
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
| | - Puneet Batra
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Steven A. Lubitz
- Cardiovascular Research Ctr, Massachusetts General Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Ctr for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
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24
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Harmon DM, Sehrawat O, Maanja M, Wight J, Noseworthy PA. Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation. Arrhythm Electrophysiol Rev 2023; 12:e12. [PMID: 37427304 PMCID: PMC10326669 DOI: 10.15420/aer.2022.31] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/22/2022] [Indexed: 07/11/2023] Open
Abstract
AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.
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Affiliation(s)
- David M Harmon
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
| | - Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
| | - Maren Maanja
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - John Wight
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, US
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25
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Chousou PA, Chattopadhyay R, Tsampasian V, Vassiliou VS, Pugh PJ. Electrocardiographic Predictors of Atrial Fibrillation. Med Sci (Basel) 2023; 11:medsci11020030. [PMID: 37092499 PMCID: PMC10123668 DOI: 10.3390/medsci11020030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is the most common pathological arrhythmia, and its complications lead to significant morbidity and mortality. However, patients with AF can often go undetected, especially if they are asymptomatic or have a low burden of paroxysms. Identification of those at high risk of AF development may help refine screening and management strategies. METHODS PubMed and Embase databases were systematically searched for studies looking at electrocardiographic predictors of AF from inception to August 2021. RESULTS A total of 115 studies were reported which examined a combination of atrial and ventricular parameters that could be electrocardiographic predictors of AF. Atrial predictors include conduction parameters, such as the PR interval, p-wave index and dispersion, and partial interatrial or advanced interatrial block, or morphological parameters, such as p-wave axis, amplitude and terminal force. Ventricular predictors include abnormalities in QRS amplitude, morphology or duration, QT interval duration, r-wave progression and ST segment, i.e., t-wave abnormalities. CONCLUSIONS There has been significant interest in electrocardiographic prediction of AF, especially in populations at high risk of atrial AF, such as those with an embolic stroke of undetermined source. This review highlights the breadth of possible predictive parameters, and possible pathological bases for the predictive role of each parameter are proposed.
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Affiliation(s)
- Panagiota Anna Chousou
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Rahul Chattopadhyay
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Vasiliki Tsampasian
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Vassilios S Vassiliou
- Norwich Medical School, University of East Anglia, Norwich NR4 7TJ, UK
- Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich NR4 7UY, UK
| | - Peter John Pugh
- Addenbrookes Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
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26
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Haimovich JS, Diamant N, Khurshid S, Di Achille P, Reeder C, Friedman S, Singh P, Spurlock W, Ellinor PT, Philippakis A, Batra P, Ho JE, Lubitz SA. Artificial Intelligence Enabled Classification of Hypertrophic Heart Diseases Using Electrocardiograms. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2023; 4:48-59. [PMID: 37101945 PMCID: PMC10123506 DOI: 10.1016/j.cvdhj.2023.03.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023] Open
Abstract
Background Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care. Objective To evaluate if artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH. Methods We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression ("LVH-Net"). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I ("LVH-Net Lead I") or lead II ("LVH-Net Lead II") from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH. Results The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93-0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90-0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well. Conclusion An artificial intelligence-enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules.
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Affiliation(s)
- Julian S. Haimovich
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Nate Diamant
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Shaan Khurshid
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Christopher Reeder
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Sam Friedman
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Walter Spurlock
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Patrick T. Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts
| | - Anthony Philippakis
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Jennifer E. Ho
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Steven A. Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts
- Address reprint requests and correspondence: Dr Steven A. Lubitz, Demoulas Center for Cardiac Arrhythmias and Cardiovascular Research Center, Massachusetts General Hospital, 55 Fruit Street, GRB 109, Boston, MA 02114.
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27
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Vaid A, Argulian E, Lerakis S, Beaulieu-Jones BK, Krittanawong C, Klang E, Lampert J, Reddy VY, Narula J, Nadkarni GN, Glicksberg BS. Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction. COMMUNICATIONS MEDICINE 2023; 3:24. [PMID: 36788316 PMCID: PMC9929085 DOI: 10.1038/s43856-023-00240-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/09/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making. METHODS In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients. We developed a Natural Language Processing pipeline to extract ground-truth labels about valvular status and paired these to Electrocardiograms (ECGs). We developed and externally validated deep learning models capable of detecting valvular disease, in addition to considering scenarios of clinical deployment. RESULTS We use 617,338 ECGs paired to transthoracic echocardiograms from 123,096 patients to develop a deep learning model for detection of Mitral Regurgitation. Area Under Receiver Operating Characteristic curve (AUROC) is 0.88 (95% CI:0.88-0.89) in internal testing, and 0.81 (95% CI:0.80-0.82) in external validation. To develop a model for detection of Aortic Stenosis, we use 617,338 Echo-ECG pairs for 128,628 patients. AUROC is 0.89 (95% CI: 0.88-0.89) in internal testing, going to 0.86 (95% CI: 0.85-0.87) in external validation. The model's performance increases leading up to the time of the diagnostic echo, and it performs well in validation against requirement of Transcatheter Aortic Valve Replacement procedures. CONCLUSIONS Deep learning based tools can increase the amount of information extracted from ubiquitous investigations such as the ECG. Such tools are inexpensive, can help in earlier disease detection, and potentially improve prognosis.
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Affiliation(s)
- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Section of Biomedical Data Science, Department of Medicine, University of Chicago, Chicago, IL, USA
| | | | - Eyal Klang
- Sheba Medical Center, Department of Diagnostic Imaging, Tel Hashomer, Israel
- Sackler Medical School, Tel Aviv University, Tel Aviv, 52621, Israel
| | - Joshua Lampert
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine (D3M), The Department of Medicine, Icahn School of Medicine at Mount Siniai, New York, NY, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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28
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Alabdaljabar MS, Hasan B, Noseworthy PA, Maalouf JF, Ammash NM, Hashmi SK. Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries. J Multidiscip Healthc 2023; 16:285-295. [PMID: 36741292 PMCID: PMC9891080 DOI: 10.2147/jmdh.s383810] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 11/07/2022] [Indexed: 01/30/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients' lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.
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Affiliation(s)
- Mohamad S Alabdaljabar
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA,College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Babar Hasan
- Sindh Institute of Urology and Transplantation (SIUT), Karachi, Pakistan
| | | | - Joseph F Maalouf
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA,Department of Medicine, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates
| | - Naser M Ammash
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA,Department of Medicine, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates
| | - Shahrukh K Hashmi
- Department of Medicine, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates,Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, MN, USA,Correspondence: Shahrukh K Hashmi, Department of Medicine, SSMC, Abu Dhabi, United Arab Emirates, Email
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29
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Goudis C, Daios S, Dimitriadis F, Liu T. CHARGE-AF: A Useful Score For Atrial Fibrillation Prediction? Curr Cardiol Rev 2023; 19:e010922208402. [PMID: 36056866 PMCID: PMC10201902 DOI: 10.2174/1573403x18666220901102557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/10/2022] [Accepted: 08/01/2022] [Indexed: 11/22/2022] Open
Abstract
Atrial fibrillation (AF) is the commonest arrhythmia in clinical practice and is associated with increased morbidity and mortality. Various predictive scores for new-onset AF have been proposed, but so far, none have been widely used in clinical practice. CHARGE-AF score was developed from a pooled diverse population from three large cohorts (Atherosclerosis Risk in Communities study, Cardiovascular Health Study and Framingham Heart Study). A simple 5-year predictive model includes the variables of age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes mellitus, history of myocardial infarction and heart failure. Recent studies report that the CHARGE-AF score has good discrimination for incident AF and seems to be a promising prediction model for this arrhythmia. New screening tools (smartphone apps, smartwatches) are rapidly developing for AF detection. Therefore, the wide application of the CHARGE-AF score in clinical practice and the upcoming usage of mobile health technologies and smartwatches may result in better AF prediction and adequate stroke prevention, especially in high-risk patients.
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Affiliation(s)
- Christos Goudis
- Department of Cardiology, Serres General Hospital, Serres, Greece
| | - Stylianos Daios
- Department of Cardiology, Serres General Hospital, Serres, Greece
| | - Fotios Dimitriadis
- Department of Cardiology, George Papanikolaou General Hospital, Thessaloniki, Greece
| | - Tong Liu
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300211, People’s Republic of China
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30
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Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8002. [PMID: 36298352 PMCID: PMC9610988 DOI: 10.3390/s22208002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/06/2023]
Abstract
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
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Affiliation(s)
- Jian-Dong Huang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Jinling Wang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Gerard Leavey
- School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK
| | - Timothy J. A. Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
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31
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Chiang C, Schwedt TJ, Dodick DW. Exploring the association between migraine and atrial fibrillation utilizing a novel artificial intelligence‐ECG algorithm. Headache 2022; 62:933-934. [DOI: 10.1111/head.14366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 06/23/2022] [Indexed: 11/30/2022]
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32
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Chiang C, Chhabra N, Chao C, Wang H, Zhang N, Lim E, Baez‐Suarez A, Attia ZI, Schwedt TJ, Dodick DW, Cutrer FM, Friedman PA, Noseworthy PA. Migraine with aura associates with a higher artificial intelligence:
ECG
atrial fibrillation prediction model output compared to migraine without aura in both women and men. Headache 2022; 62:939-951. [DOI: 10.1111/head.14339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 11/29/2022]
Affiliation(s)
| | - Nikita Chhabra
- Department of Neurology Mayo Clinic Scottsdale Arizona USA
| | - Chieh‐Ju Chao
- Department of Cardiovascular Diseases Mayo Clinic Rochester Minnesota USA
| | - Han Wang
- Department of Neurology Mayo Clinic Mankato Minnesota USA
| | - Nan Zhang
- Department of Quantitative Health Research Mayo Clinic Scottsdale Arizona USA
| | - Elisabeth Lim
- Department of Quantitative Health Research Mayo Clinic Scottsdale Arizona USA
| | | | - Zachi I. Attia
- Department of Cardiovascular Diseases Mayo Clinic Rochester Minnesota USA
| | | | | | - Fred M. Cutrer
- Department of Neurology Mayo Clinic Rochester Minnesota USA
| | - Paul A. Friedman
- Department of Cardiovascular Diseases Mayo Clinic Rochester Minnesota USA
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33
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Schnabel RB, Marinelli EA, Arbelo E, Boriani G, Boveda S, Buckley CM, Camm AJ, Casadei B, Chua W, Dagres N, de Melis M, Desteghe L, Diederichsen SZ, Duncker D, Eckardt L, Eisert C, Engler D, Fabritz L, Freedman B, Gillet L, Goette A, Guasch E, Svendsen JH, Hatem SN, Haeusler KG, Healey JS, Heidbuchel H, Hindricks G, Hobbs FDR, Hübner T, Kotecha D, Krekler M, Leclercq C, Lewalter T, Lin H, Linz D, Lip GYH, Løchen ML, Lucassen W, Malaczynska-Rajpold K, Massberg S, Merino JL, Meyer R, Mont L, Myers MC, Neubeck L, Niiranen T, Oeff M, Oldgren J, Potpara TS, Psaroudakis G, Pürerfellner H, Ravens U, Rienstra M, Rivard L, Scherr D, Schotten U, Shah D, Sinner MF, Smolnik R, Steinbeck G, Steven D, Svennberg E, Thomas D, True Hills M, van Gelder IC, Vardar B, Palà E, Wakili R, Wegscheider K, Wieloch M, Willems S, Witt H, Ziegler A, Daniel Zink M, Kirchhof P. Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference. Europace 2022; 25:6-27. [PMID: 35894842 PMCID: PMC9907557 DOI: 10.1093/europace/euac062] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains difficult and AF-related complications cause unacceptable morbidity and mortality even on optimal current therapy. This document summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Eighty-three international experts met in Hamburg for 2 days in October 2021. Results of the interdisciplinary, hybrid discussions in breakout groups and the plenary based on recently published and unpublished observations are summarized in this consensus paper to support improved care for patients with AF by guiding prevention, individualized management, and research strategies. The main outcomes are (i) new evidence supports a simple, scalable, and pragmatic population-based AF screening pathway; (ii) rhythm management is evolving from therapy aimed at improving symptoms to an integrated domain in the prevention of AF-related outcomes, especially in patients with recently diagnosed AF; (iii) improved characterization of atrial cardiomyopathy may help to identify patients in need for therapy; (iv) standardized assessment of cognitive function in patients with AF could lead to improvement in patient outcomes; and (v) artificial intelligence (AI) can support all of the above aims, but requires advanced interdisciplinary knowledge and collaboration as well as a better medico-legal framework. Implementation of new evidence-based approaches to AF screening and rhythm management can improve outcomes in patients with AF. Additional benefits are possible with further efforts to identify and target atrial cardiomyopathy and cognitive impairment, which can be facilitated by AI.
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Affiliation(s)
- Renate B Schnabel
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,German Centre for Cardiovascular Research (DZHK) partner site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | | | - Elena Arbelo
- Arrhythmia Section, Cardiology Department, Hospital Clinic, Universitat de Barcelona, Barcelona, Spain,IDIBAPS, Institut d'Investigació August Pi i Sunyer, Barcelona, Spain,CIBERCV, Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
| | - Giuseppe Boriani
- Cardiology Division, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Polyclinic of Modena, Modena, Italy
| | - Serge Boveda
- Cardiology—Heart Rhythm Management Department, Clinique Pasteur, 45 Avenue de Lombez, 31076 Toulouse, France,Universiteit Ziekenhuis, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | | | - A John Camm
- Cardiology Clinical Academic Group, Molecular and Clinical Sciences Institute, St. George's University of London, London, UK
| | - Barbara Casadei
- RDM, Division of Cardiovascular Medicine, British Heart Foundation Centre of Research Excellence, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Winnie Chua
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
| | - Nikolaos Dagres
- Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | - Mirko de Melis
- Medtronic Bakken Research Center, Maastricht, The Netherlands
| | - Lien Desteghe
- Research Group Cardiovascular Diseases, University of Antwerp, Antwerp, Belgium,Department of Cardiology, Antwerp University Hospital, Antwerp, Belgium,Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium,Heart Centre Hasselt, Jessa Hospital, Hasselt, Belgium
| | - Søren Zöga Diederichsen
- Department of Cardiology, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark
| | - David Duncker
- Hannover Heart Rhythm Center, Department of Cardiology and Angiology, Hannover Medical School, Hannover, Germany
| | - Lars Eckardt
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Division of Electrophysiology, Department of Cardiology and Angiology, Münster, Germany
| | | | - Daniel Engler
- Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,German Centre for Cardiovascular Research (DZHK) partner site Hamburg/Kiel/Lübeck, Hamburg, Germany
| | - Larissa Fabritz
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Department of Cardiology, University Heart & Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany,German Centre for Cardiovascular Research (DZHK) partner site Hamburg/Kiel/Lübeck, Hamburg, Germany,Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK,University Center of Cardiovascular Science Hamburg, Hamburg, Germany
| | - Ben Freedman
- Heart Research Institute, The University of Sydney, Sydney, Australia
| | | | - Andreas Goette
- Atrial Fibrillation Network (AFNET), Muenster, Germany,St Vincenz Hospital, Paderborn, Germany
| | - Eduard Guasch
- Arrhythmia Section, Cardiology Department, Hospital Clinic, Universitat de Barcelona, Barcelona, Spain,IDIBAPS, Institut d'Investigació August Pi i Sunyer, Barcelona, Spain,CIBERCV, Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
| | - Jesper Hastrup Svendsen
- Department of Cardiology, Copenhagen University Hospital—Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Karl Georg Haeusler
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Department of Neurology, Universitätsklinikum Würzburg, Würzburg, Germany
| | - Jeff S Healey
- Population Health Research Institute, McMaster University Hamilton, ON, Canada
| | - Hein Heidbuchel
- Research Group Cardiovascular Diseases, University of Antwerp, Antwerp, Belgium,Department of Cardiology, Antwerp University Hospital, Antwerp, Belgium
| | - Gerhard Hindricks
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Department of Electrophysiology, Heart Center Leipzig at University of Leipzig, Leipzig, Germany
| | | | | | - Dipak Kotecha
- University of Birmingham & University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | | | - Thorsten Lewalter
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Hospital Munich South, Department of Cardiology, Munich, Germany,Department of Cardiology, University of Bonn, Bonn, Germany
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Dominik Linz
- Department of Cardiology, Maastricht University Medical Center and Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands,Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK,Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Maja Lisa Løchen
- Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Wim Lucassen
- Amsterdam UMC (location AMC), Department General Practice, Amsterdam, The Netherlands
| | | | - Steffen Massberg
- Department of Cardiology, University Hospital, LMU Munich, Munich, Germany,German Centre for Cardiovascular Research (DZHK), partner site: Munich Heart Alliance, Munich, Germany
| | - Jose L Merino
- Arrhythmia & Robotic EP Unit, La Paz University Hospital, IDIPAZ, Madrid, Spain
| | | | - Lluıs Mont
- Arrhythmia Section, Cardiology Department, Hospital Clinic, Universitat de Barcelona, Barcelona, Spain,IDIBAPS, Institut d'Investigació August Pi i Sunyer, Barcelona, Spain,CIBERCV, Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares, Madrid, Spain
| | | | - Lis Neubeck
- Arrhythmia & Robotic EP Unit, La Paz University Hospital, IDIPAZ, Madrid, Spain
| | - Teemu Niiranen
- Medtronic, Dublin, Ireland,Centre for Cardiovascular Health Edinburgh Napier University, Edinburgh, UK
| | - Michael Oeff
- Atrial Fibrillation Network (AFNET), Muenster, Germany
| | - Jonas Oldgren
- University of Turku and Turku University Hospital, Turku, Finland
| | | | - George Psaroudakis
- Uppsala Clinical Research Center and Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Helmut Pürerfellner
- School of Medicine, Belgrade University, Cardiology Clinic, University Clinical Centre of Serbia, Belgrade, Serbia
| | - Ursula Ravens
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Bayer AG, Leverkusen, Germany
| | - Michiel Rienstra
- Ordensklinikum Linz, Elisabethinen, Cardiological Department, Linz, Austria
| | - Lena Rivard
- Institute of Experimental Cardiovascular Medicine, University Hospital Freiburg, Freiburg, Germany
| | - Daniel Scherr
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ulrich Schotten
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Montreal Heart Institute, University of Montreal, Montreal, Canada
| | - Dipen Shah
- Division of Cardiology, Medical University of Graz, Graz, Austria
| | - Moritz F Sinner
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Amsterdam UMC (location AMC), Department General Practice, Amsterdam, The Netherlands,Royal Brompton Hospital, London, UK
| | | | - Gerhard Steinbeck
- Atrial Fibrillation Network (AFNET), Muenster, Germany,MUMC+, Maastricht, The Netherlands
| | - Daniel Steven
- Atrial Fibrillation Network (AFNET), Muenster, Germany,University Hospital of Geneva, Cardiac Electrophysiology Unit, Geneva, Switzerland
| | - Emma Svennberg
- Center for Cardiology at Clinic Starnberg, Starnberg, Germany
| | - Dierk Thomas
- Atrial Fibrillation Network (AFNET), Muenster, Germany,University Hospital Cologne, Heart Center, Department of Electrophysiology, Cologne, Germany,Karolinska Institutet, Department of Medicine Huddinge, Karolinska University Hospital, Stockholm, Sweden,Department of Cardiology, Medical University Hospital, Heidelberg, Germany
| | - Mellanie True Hills
- HCR (Heidelberg Center for Heart Rhythm Disorders), Medical University Hospital Heidelberg, Heidelberg, Germany
| | - Isabelle C van Gelder
- DZHK (German Center for Cardiovascular Research), partner site Heidelberg/Mannheim, Heidelberg, Germany
| | - Burcu Vardar
- Uppsala Clinical Research Center and Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Elena Palà
- StopAfib.org, American Foundation for Women’s Health, Decatur, TX, USA
| | - Reza Wakili
- Atrial Fibrillation Network (AFNET), Muenster, Germany,Department of Cardiology, University Medical Center Groningen, University of Groningen, The Netherlands
| | - Karl Wegscheider
- Atrial Fibrillation Network (AFNET), Muenster, Germany,German Centre for Cardiovascular Research (DZHK) partner site Hamburg/Kiel/Lübeck, Hamburg, Germany,Neurovascular Research Laboratory, Vall d’Hebron Institute of Research (VHIR), Autonomous University of Barcelona, Barcelona, Spain
| | - Mattias Wieloch
- Department of Cardiology and Vascular Medicine, Westgerman Heart and Vascular Center, University of Duisburg-Essen, Essen, Germany,Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Germany
| | - Stephan Willems
- Atrial Fibrillation Network (AFNET), Muenster, Germany,German Centre for Cardiovascular Research (DZHK) partner site Hamburg/Kiel/Lübeck, Hamburg, Germany,Department of Coagulation Disorders, Skane University Hospital, Lund University, Malmö, Sweden
| | | | | | - Matthias Daniel Zink
- Asklepios Hospital St Georg, Department of Cardiology and Internal Intensive Care Medicine, Faculty of Medicine, Semmelweis University Campus Hamburg, Hamburg, Germany
| | - Paulus Kirchhof
- Corresponding author. Tel: +49 40 7410 52438; Fax: +49 40 7410 55862. E-mail address:
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Geurts S, Lu Z, Kavousi M. Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data. Front Cardiovasc Med 2022; 9:886469. [PMID: 35898269 PMCID: PMC9309362 DOI: 10.3389/fcvm.2022.886469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 06/17/2022] [Indexed: 02/01/2023] Open
Abstract
Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, has a large impact on quality of life and is associated with increased risk of hospitalization, morbidity, and mortality. Over the past two decades advances regarding the clinical epidemiology and management of AF have been established. Moreover, sex differences in the prevalence, incidence, prediction, pathophysiology, and prognosis of AF have been identified. Nevertheless, AF remains to be a complex and heterogeneous disorder and a comprehensive sex- and gender-specific approach to predict new-onset AF is lacking. The exponential growth in various sources of big data such as electrocardiograms, electronic health records, and wearable devices, carries the potential to improve AF risk prediction. Leveraging these big data sources by artificial intelligence (AI)-enabled approaches, in particular in a sex- and gender-specific manner, could lead to substantial advancements in AF prediction and ultimately prevention. We highlight the current status, premise, and potential of big data to improve sex- and gender-specific prediction of new-onset AF.
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Kashou AH, Adedinsewo DA, Siontis KC, Noseworthy PA. Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications. Compr Physiol 2022; 12:3417-3424. [PMID: 35766831 PMCID: PMC9795459 DOI: 10.1002/cphy.c210001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.
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Affiliation(s)
- Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Rafie N, Jentzer JC, Noseworthy PA, Kashou AH. Mortality Prediction in Cardiac Intensive Care Unit Patients: A Systematic Review of Existing and Artificial Intelligence Augmented Approaches. Front Artif Intell 2022; 5:876007. [PMID: 35711617 PMCID: PMC9193583 DOI: 10.3389/frai.2022.876007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The medical complexity and high acuity of patients in the cardiac intensive care unit make for a unique patient population with high morbidity and mortality. While there are many tools for predictions of mortality in other settings, there is a lack of robust mortality prediction tools for cardiac intensive care unit patients. The ongoing advances in artificial intelligence and machine learning also pose a potential asset to the advancement of mortality prediction. Artificial intelligence algorithms have been developed for application of electrocardiogram interpretation with promising accuracy and clinical application. Additionally, artificial intelligence algorithms applied to electrocardiogram interpretation have been developed to predict various variables such as structural heart disease, left ventricular systolic dysfunction, and atrial fibrillation. These variables can be used and applied to new mortality prediction models that are dynamic with the changes in the patient's clinical course and may lead to more accurate and reliable mortality prediction. The application of artificial intelligence to mortality prediction will fill the gaps left by current mortality prediction tools.
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Affiliation(s)
- Nikita Rafie
- Department of Medicine, Mayo Clinic, Rochester, MN, United States
- *Correspondence: Nikita Rafie
| | - Jacob C. Jentzer
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, United States
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
| | - Anthony H. Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States
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Christopoulos G, Attia ZI, Van Houten HK, Yao X, Carter RE, Lopez-Jimenez F, Kapa S, Noseworthy PA, Friedman PA. Artificial intelligence-electrocardiography to detect atrial fibrillation: trend of probability before and after the first episode. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:228-235. [PMID: 36713006 PMCID: PMC9707931 DOI: 10.1093/ehjdh/ztac023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 03/09/2022] [Indexed: 02/01/2023]
Abstract
Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2-5 years prior to AF, 23.6% 1-2 years prior to AF, 34.0% 0-3 months prior to AF, 40.9% 0-3 months after AF, 35.2% 1-2 years after AF, and 42.2% 2-5 years after AF (P < 0.001). The rate of increase prior to AF was higher for age >50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1-2 years following AF and continues to increase thereafter.
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Affiliation(s)
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Holly K Van Houten
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
| | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA
- Division of Health Care Delivery Research, Mayo Clinic, Rochester, MN, USA
| | - Rickey E Carter
- Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
| | | | - Suraj Kapa
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Weil EL, Noseworthy PA, Lopez CL, Rabinstein AA, Friedman PA, Attia ZI, Yao X, Siontis KC, Kremers WK, Christopoulos G, Mielke MM, Vemuri P, Jack CR, Gersh BJ, Machulda MM, Knopman DS, Petersen RC, Graff-Radford J. Artificial Intelligence-Enabled Electrocardiogram for Atrial Fibrillation Identifies Cognitive Decline Risk and Cerebral Infarcts. Mayo Clin Proc 2022; 97:871-880. [PMID: 35512882 PMCID: PMC9179015 DOI: 10.1016/j.mayocp.2022.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/01/2021] [Accepted: 01/11/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVE To investigate whether artificial intelligence-enabled electrocardiogram (AI-ECG) assessment of atrial fibrillation (AF) risk predicts cognitive decline and cerebral infarcts. PATIENTS AND METHODS This population-based study included sinus-rhythm ECG participants seen from November 29, 2004 through July 13, 2020, and a subset with brain magnetic resonance imaging (MRI) (October 10, 2011, through November 2, 2017). The AI-ECG score of AF risk calculated for participants was 0-1. To determine the AI-ECG-AF relationship with baseline cognitive dysfunction, we compared linear mixed-effects models with global and domain-specific cognitive z-scores from longitudinal neuropsychological assessments. The AI-ECG-AF score was logit transformed and modeled with cubic splines. For the brain-MRI subset, logistic regression evaluated correlation of the AI-ECG-AF score and the high-threshold, dichotomized AI-ECG-AF score with infarcts. RESULTS Participants (N=3729; median age, 74.1 years) underwent cognitive analysis. Adjusting for age, sex, education, and APOE ɛ4-carrier status, the AI-ECG-AF score correlated with lower baseline and faster decline in global-cognitive z-scores (P=.009 and P=.01, respectively, non-linear-based spline-models tests) and attention z-scores (P<.001 and P=.01, respectively). Sinus-rhythm-ECG participants (n=1373) underwent MRI. As a continuous measure, the AI-ECG-AF score correlated with infarcts but not after age and sex adjustment (P=.52). For dichotomized analysis, an AI-ECG-AF score greater than 0.5 correlated with infarcts (OR, 4.61; 95% CI, 2.45-8.55; P<.001); even after age and sex adjustment (OR, 2.09; 95% CI, 1.06-4.07; P=.03). CONCLUSION The AI-ECG-AF score correlated with worse baseline cognition and gradual global cognition and attention decline. High AF probability by AI-ECG-AF score correlated with MRI cerebral infarcts. However, most infarcts observed in our cohort were subcortical, suggesting that AI-ECG not only predicts AF but also detects other non-AF cardiac disease markers and correlates with small vessel cerebrovascular disease and cognitive decline.
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Affiliation(s)
- Erika L Weil
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Camden L Lopez
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Xiaoxi Yao
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | | | - Walter K Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Michelle M Mielke
- Department of Neurology, Mayo Clinic, Rochester, MN, USA; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | | | - Bernard J Gersh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
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Chung CT, Bazoukis G, Lee S, Liu Y, Liu T, Letsas KP, Armoundas AA, Tse G. Machine learning techniques for arrhythmic risk stratification: a review of the literature. INTERNATIONAL JOURNAL OF ARRHYTHMIA 2022; 23. [PMID: 35449883 PMCID: PMC9020640 DOI: 10.1186/s42444-022-00062-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.
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Wang YC, Xu X, Hajra A, Apple S, Kharawala A, Duarte G, Liaqat W, Fu Y, Li W, Chen Y, Faillace RT. Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study. Diagnostics (Basel) 2022; 12:diagnostics12030689. [PMID: 35328243 PMCID: PMC8947563 DOI: 10.3390/diagnostics12030689] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/01/2022] [Accepted: 03/06/2022] [Indexed: 02/04/2023] Open
Abstract
Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.
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Affiliation(s)
- Yu-Chiang Wang
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
- Correspondence:
| | - Xiaobo Xu
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Adrija Hajra
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Samuel Apple
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Amrin Kharawala
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Gustavo Duarte
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Wasla Liaqat
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Yiwen Fu
- Department of Medicine, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA 95051, USA;
| | - Weijia Li
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Yiyun Chen
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Robert T. Faillace
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
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Sehrawat O, Kashou AH, Noseworthy PA. Artificial Intelligence and Atrial Fibrillation. J Cardiovasc Electrophysiol 2022; 33:1932-1943. [PMID: 35258136 PMCID: PMC9717694 DOI: 10.1111/jce.15440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/30/2022]
Abstract
In the context of atrial fibrillation (AF), traditional clinical practices have thus far fallen short in several domains such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. In this review we focus on the roles of artificial intelligence-enabled ECG pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with embolic stroke of undetermined source (ESUS) and who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. Role of DL models assessing AF burden from long duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, role of adequate external validation of the models and clinical trials to study true performance is discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ojasav Sehrawat
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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Martinez DSL, Noseworthy PA, Akbilgic O, Herrmann J, Ruddy KJ, Hamid A, Maddula R, Singh A, Davis R, Gunturkun F, Jefferies JL, Brown SA. Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2022; 15:100129. [PMID: 35721662 PMCID: PMC9202996 DOI: 10.1016/j.ahjo.2022.100129] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/20/2022] [Accepted: 03/21/2022] [Indexed: 01/21/2023]
Abstract
Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.
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Affiliation(s)
- Daniel Sierra-Lara Martinez
- Coronary Care Unit, National Institute of Cardiology/Instituto Nacional de Cardiologia, Ciudad de Mexico, Mexico
| | | | - Oguz Akbilgic
- Department of Health Informatics and Data Science, Parkinson School of Health Sciences and Public Health, Loyola University Chicago, Maywood, IL, USA
- Section of Cardiovascular Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Wake Forest, NC, USA
| | - Joerg Herrmann
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Ashima Singh
- Institute of Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Robert Davis
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, USA
| | - Fatma Gunturkun
- Center for Biomedical Informatics, University of Tennessee Health Sciences Center, USA
| | - John L. Jefferies
- Division of Cardiovascular Diseases, University of Tennessee Health Sciences Center, USA
- Department of Epidemiology, St. Jude Children's Research Hospital, USA
| | - Sherry-Ann Brown
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
- Cardio-Oncology Program, Division of Cardiovascular Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
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Suzuki S, Motogi J, Nakai H, Matsuzawa W, Takayanagi T, Umemoto T, Hirota N, Hyodo A, Satoh K, Otsuka T, Arita T, Yagi N, Yamashita T. Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm. IJC HEART & VASCULATURE 2022; 38:100954. [PMID: 35059494 PMCID: PMC8760502 DOI: 10.1016/j.ijcha.2022.100954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/27/2021] [Accepted: 01/03/2022] [Indexed: 11/26/2022]
Abstract
High performance of AI algorithm to detect AF using SR-ECG was confirmed in patients without structural heart disease. The performance of AI-enabled ECG to detect AF was high especially when the algorithm included SR-ECG taken after the index AF-ECG. A similar tendency was observed when the performance was tested in patients with structural heart diseases.
Background This study aimed to increase the knowledge on how to enhance the performance of artificial intelligence (AI)-enabled electrocardiography (ECG) to detect atrial fibrillation (AF) on sinus rhythm ECG (SR-ECG). Methods It is a retrospective analysis of a single-center, prospective cohort study (Shinken Database). We developed AI-enabled ECG using SR-ECG to predict AF with a convolutional neural network (CNN). Among new patients in our hospital (n = 19,170), 276 AF label (having ECG on AF [AF-ECG] in the ECG database) and 1896 SR label with following three conditions were identified in the derivation dataset: (1) without structural heart disease, (2) in AF label, SR-ECG was taken within 31 days from AF-ECG, and (3) in SR label, follow-up ≥ 1,095 days. Three patterns of AF label were analyzed by timing of SR-ECG to AF-ECG (before/after/before-or-after, CNN algorithm 1 to 3). The outcome measurement was area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. As an extra-testing dataset, the performance of AI-enabled ECG was tested in patients with structural heart disease. Results The AUC of AI-enabled ECG with CNN algorithm 1, 2, and 3 in the derivation dataset was 0.83, 0.88, and 0.86, respectively; when tested in patients with structural heart disease, 0.75, 0.81, and 0.78, respectively. Conclusion We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease.
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Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al-Alusi MA, Sarma G, Foulkes AS, Ellinor PT, Anderson CD, Ho JE, Philippakis AA, Batra P, Lubitz SA. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation 2022; 145:122-133. [PMID: 34743566 PMCID: PMC8748400 DOI: 10.1161/circulationaha.121.057480] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. METHODS We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology-Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women's Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. RESULTS The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767-0.836]; BWH, 0.752 [95% CI, 0.741-0.763]; UK Biobank, 0.732 [95% CI, 0.704-0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790-0.856]; BWH, 0.747 [95% CI, 0.736-0.759]; UK Biobank, 0.705 [95% CI, 0.673-0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r: MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). CONCLUSIONS AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.
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Affiliation(s)
- Shaan Khurshid
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Samuel Friedman
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Christopher Reeder
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Lia X. Harrington
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xin Wang
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Mostafa A. Al-Alusi
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Gopal Sarma
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Andrea S. Foulkes
- Harvard Medical School, Boston, Massachusetts, United States of America
- Biostatistics Center, Massachusetts General Hospital, Boston, MA
| | - Patrick T. Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, United States of America
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Christopher D. Anderson
- Harvard Medical School, Boston, Massachusetts, United States of America
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jennifer E. Ho
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Anthony A. Philippakis
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Eric and Wendy Schmidt Center, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Steven A. Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, United States of America
- Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, Massachusetts, USA
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Chung CT, Lee S, King E, Liu T, Armoundas AA, Bazoukis G, Tse G. Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis. INTERNATIONAL JOURNAL OF ARRHYTHMIA 2022; 23:24. [PMID: 36212507 PMCID: PMC9525157 DOI: 10.1186/s42444-022-00075-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 07/13/2022] [Indexed: 11/07/2022] Open
Abstract
Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.
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Affiliation(s)
- Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China
| | - Sharen Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China
| | - Emma King
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China
| | - Tong Liu
- grid.412648.d0000 0004 1798 6160Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211 China
| | - Antonis A. Armoundas
- grid.32224.350000 0004 0386 9924Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA USA ,grid.116068.80000 0001 2341 2786Broad Institute, Massachusetts Institute of Technology, Cambridge, MA USA
| | - George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Inomenon Polition Amerikis, Larnaca, Cyprus ,grid.413056.50000 0004 0383 4764Department of Basic and Clinical Sciences, University of Nicosia Medical School, 2414 Nicosia, Cyprus
| | - Gary Tse
- grid.412648.d0000 0004 1798 6160Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211 China ,Kent and Medway Medical School, Canterbury, UK
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Gupta H. Positioning primary care as base of health care pyramid. J Family Med Prim Care 2022; 11:2260-2261. [PMID: 35800542 PMCID: PMC9254822 DOI: 10.4103/jfmpc.jfmpc_2267_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/24/2021] [Accepted: 12/25/2021] [Indexed: 11/17/2022] Open
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Identifying Risk of Adverse Outcomes in COVID-19 Patients via Artificial Intelligence-Powered Analysis of 12-Lead Intake Electrocardiogram. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2021; 3:62-74. [PMID: 35005676 PMCID: PMC8719367 DOI: 10.1016/j.cvdhj.2021.12.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Background Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. Objective Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). Methods We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. Results A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. Conclusion Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients’ risk of mortality or MACE. Our models’ accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.
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Nakamura T, Sasano T. Artificial intelligence and cardiology: Current status and perspective: Artificial Intelligence and Cardiology. J Cardiol 2021; 79:326-333. [PMID: 34895982 DOI: 10.1016/j.jjcc.2021.11.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/19/2022]
Abstract
The development of artificial intelligence (AI) began in the mid-20th century but has been rapidly accelerating in the past decade. Reflecting the development of digital health over the past few years, this trend is also seen in medicine. The field of cardiovascular medicine uses a wide variety and a large amount of biosignals, so there are many situations where AI can contribute. The development of AI is in progress for all aspects of the healthcare system, including the prevention, screening, and treatment of diseases and the prediction of the prognosis. AI is expected to be used to provide specialist-level medical care, even in a situation where medical resources are scarce. However, like other medical devices, the concept and mechanism of AI must be fully understood when used; otherwise, it may be used inappropriately, resulting in detriment to the patient. Therefore, it is important to understand what we need to know as a cardiologist handling AI. This review introduces the basics and principles of AI, then shows how far the current development of AI has come, and finally gives a brief introduction of how to start the AI development for those who want to develop their own AI.
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Affiliation(s)
- Tomofumi Nakamura
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Tetsuo Sasano
- Department of Cardiovascular Medicine, Tokyo Medical and Dental University, Tokyo, Japan.
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Attia ZI, Harmon DM, Behr ER, Friedman PA. Application of artificial intelligence to the electrocardiogram. Eur Heart J 2021; 42:4717-4730. [PMID: 34534279 PMCID: PMC8500024 DOI: 10.1093/eurheartj/ehab649] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 06/18/2021] [Accepted: 09/02/2021] [Indexed: 01/02/2023] Open
Abstract
Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.
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Affiliation(s)
- Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - David M Harmon
- Department of Internal Medicine, Mayo Clinic School of Graduate Medical Education, 200 First Street SW, Rochester, MN 55905, USA
| | - Elijah R Behr
- Cardiology Research Center and Cardiovascular Clinical Academic Group, Molecular and Clinical Sciences Institute, St. George’s University of London and St. George’s University Hospitals NHS Foundation Trust, Blackshaw Rd, London SW17 0QT, UK
- Mayo Clinic Healthcare, 15 Portland Pl, London W1B 1PT, UK
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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Mahayni AA, Attia ZI, Medina-Inojosa JR, Elsisy MFA, Noseworthy PA, Lopez-Jimenez F, Kapa S, Asirvatham SJ, Friedman PA, Crestenallo JA, Alkhouli M. Electrocardiography-Based Artificial Intelligence Algorithm Aids in Prediction of Long-term Mortality After Cardiac Surgery. Mayo Clin Proc 2021; 96:3062-3070. [PMID: 34863396 DOI: 10.1016/j.mayocp.2021.06.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/06/2021] [Accepted: 06/02/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To assess whether an electrocardiography-based artificial intelligence (AI) algorithm developed to detect severe ventricular dysfunction (left ventricular ejection fraction [LVEF] of 35% or below) independently predicts long-term mortality after cardiac surgery among patients without severe ventricular dysfunction (LVEF>35%). METHODS Patients who underwent valve or coronary bypass surgery at Mayo Clinic (1993-2019) and had documented LVEF above 35% on baseline electrocardiography were included. We compared patients with an abnormal vs a normal AI-enhanced electrocardiogram (AI-ECG) screen for LVEF of 35% or below on preoperative electrocardiography. The primary end point was all-cause mortality. RESULTS A total of 20,627 patients were included, of whom 17,125 (83.0%) had a normal AI-ECG screen and 3502 (17.0%) had an abnormal AI-ECG screen. Patients with an abnormal AI-ECG screen were older and had more comorbidities. Probability of survival at 5 and 10 years was 86.2% and 68.2% in patients with a normal AI-ECG screen vs 71.4% and 45.1% in those with an abnormal screen (log-rank, P<.01). In the multivariate Cox survival analysis, the abnormal AI-ECG screen was independently associated with a higher all-cause mortality overall (hazard ratio [HR], 1.31; 95% CI, 1.24 to 1.37) and in subgroups of isolated valve surgery (HR, 1.30; 95% CI, 1.18 to 1.42), isolated coronary artery bypass grafting (HR, 1.29; 95% CI, 1.20 to 1.39), and combined coronary artery bypass grafting and valve surgery (HR, 1.19; 95% CI, 1.08 to 1.32). In a subgroup analysis, the association between abnormal AI-ECG screen and mortality was consistent in patients with LVEF of 35% to 55% and among those with LVEF above 55%. CONCLUSION A novel electrocardiography-based AI algorithm that predicts severe ventricular dysfunction can predict long-term mortality among patients with LVEF above 35% undergoing valve and/or coronary bypass surgery.
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Affiliation(s)
| | - Zachi I Attia
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN
| | | | | | | | | | - Suraj Kapa
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN
| | | | - Paul A Friedman
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN
| | | | - Mohamad Alkhouli
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
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