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Fleet H, Pilcher D, Bellomo R, Coulson TG. Predicting atrial fibrillation after cardiac surgery: a scoping review of associated factors and systematic review of existing prediction models. Perfusion 2023; 38:92-108. [PMID: 34405746 DOI: 10.1177/02676591211037025] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
INTRODUCTION Postoperative atrial fibrillation (POAF) is common after cardiac surgery and associated with increased hospital length of stay, patient morbidity and mortality. We aimed to identify factors associated with POAF and evaluate the accuracy of available POAF prediction models. METHODS We screened articles from Ovid MEDLINE® and PubMed Central® (PMC) and included studies that evaluated risk factors associated with POAF or studies that designed or validated POAF prediction models. We only included studies in cardiac surgical patients with sample size n ⩾ 50 and a POAF outcome group ⩾20. We summarised factors that were associated with POAF and assessed prediction model performance by reviewing reported calibration and discriminative ability. RESULTS We reviewed 232 studies. Of these, 142 fulfilled the inclusion criteria. Age was frequently found to be associated with POAF, while most other variables showed contradictory findings, or were assessed in few studies. Overall, 15 studies specifically developed and/or validated 12 prediction models. Of these, all showed poor discrimination or absent calibration in predicting POAF in externally validated cohorts. CONCLUSIONS Except for age, reporting of factors associated with POAF is inconsistent and often contradictory. Prediction models have low discrimination, missing calibration statistics, are at risk of bias and show limited clinical applicability. This suggests the need for studies that prospectively collect AF relevant data in large cohorts and then proceed to validate findings in external data sets.
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Affiliation(s)
- Hugh Fleet
- Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
| | - David Pilcher
- Department of Epidemiology and Preventative Medicine, Monash University, Melbourne, VIC, Australia
| | - Rinaldo Bellomo
- Centre for Integrated Critical Care, The University of Melbourne, Parkville, VIC, Australia
| | - Tim G Coulson
- Department of Epidemiology and Preventative Medicine, Monash University, Melbourne, VIC, Australia
- Centre for Integrated Critical Care, The University of Melbourne, Parkville, VIC, Australia
- Department of Anaesthesia, Austin Hospital, Melbourne, VIC, Australia
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2
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Feilberg Rasmussen L, Andreasen JJ, Riahi S, Lip GYH, Lundbye-Christensen S, Melgaard J, Graff C. Prediction of postoperative atrial fibrillation with postoperative epicardial electrograms. SCAND CARDIOVASC J 2022; 56:378-386. [DOI: 10.1080/14017431.2022.2130421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Louise Feilberg Rasmussen
- Department of Cardiothoracic Surgery, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Jan Jesper Andreasen
- Department of Cardiothoracic Surgery, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Atrial Fibrillation Study Group, Aalborg University Hospital, Aalborg, Denmark
| | - Sam Riahi
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Atrial Fibrillation Study Group, Aalborg University Hospital, Aalborg, Denmark
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Gregory Y. H. Lip
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
- Atrial Fibrillation Study Group, Aalborg University Hospital, Aalborg, Denmark
| | - Søren Lundbye-Christensen
- Atrial Fibrillation Study Group, Aalborg University Hospital, Aalborg, Denmark
- Unit of Clinical Biostatistics, Aalborg University Hospital, Aalborg, Denmark
| | - Jacob Melgaard
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Claus Graff
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
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Haverkamp W, Strodthoff N, Israel C. [Artificial intelligence-based ECG analysis: current status and future perspectives-Part 1 : Basic principles]. Herzschrittmacherther Elektrophysiol 2022; 33:232-240. [PMID: 35552486 PMCID: PMC9177483 DOI: 10.1007/s00399-022-00854-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 06/15/2023]
Abstract
Even though electrocardiography is a diagnostic procedure that is now more than 100 years old, medicine cannot do without it. On the contrary, interest in the procedure and its clinical significance is even increasing again. Reports on the evaluation of electrocardiograms (ECGs) with the aid of artificial intelligence (AI) are also responsible for this. Using machine learning and in particular deep learning, both AI subfields, completely new perspectives of ECG evaluation and interpretation arise. The weaknesses inherent in classical computer-assisted ECG evaluation appear to be overcome. This two-part overview deals with AI-based ECG analysis. Part 1 introduces basic aspects of the procedure. Part 2, which is published separately, is devoted to the current state of research and discusses the available studies. In addition, possible scenarios of future application of AI in ECG analysis are discussed.
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Affiliation(s)
- Wilhelm Haverkamp
- Abteilung für Kardiologie und Metabolismus, Medizinische Klinik mit Schwerpunkt Kardiologie, Campus Virchow-Klinikum, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, 13353, Berlin, Deutschland.
- Berlin Institute of Health Center for Regenerative Therapies (BCRT), Berlin, Deutschland.
| | - Nils Strodthoff
- Department für Versorgungsforschung, Fakultät VI - Medizin und Gesundheitswissenschaften, Universität Oldenburg, Oldenburg, Deutschland
| | - Carsten Israel
- Klinik für Innere Medizin - Kardiologie, Diabetologie und Nephrologie, Evangelisches Klinikum Bethel, Bielefeld, Deutschland
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Wu F, Wu Y, Tao W, Zhao H, Shen D. Preoperative P-wave duration as a predictor of atrial fibrillation after coronary artery bypass grafting: A prospective cohort study with meta-analysis. Int J Nurs Sci 2018; 5:151-156. [PMID: 31406817 PMCID: PMC6626247 DOI: 10.1016/j.ijnss.2018.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Revised: 03/03/2018] [Accepted: 04/02/2018] [Indexed: 11/28/2022] Open
Abstract
Objectives Methods Results Conclusion
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Affiliation(s)
- Fangqin Wu
- School of Nursing, Capital Medical University, Beijing, China
| | - Ying Wu
- School of Nursing, Capital Medical University, Beijing, China
- Corresponding author. School of Nursing, Capital Medical University, 10 You-an-men Wai Xi-tou-tiao, Feng-tai District, Beijing, 100069, China.
| | - Wenyan Tao
- School of Nursing, Capital Medical University, Beijing, China
| | - Haibo Zhao
- Heart Center, Beijing Chao-yang Hospital Affiliated to Capital Medical University, Beijing, China
| | - Dongyan Shen
- Heart Center, Beijing Jian-gong Hospital, Beijing, China
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5
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Diferencias entre 2 electrocardiogramas sinusales como predictoras de fibrilación auricular: estudio de cohorte. ARCHIVOS DE CARDIOLOGIA DE MEXICO 2016; 86:140-7. [DOI: 10.1016/j.acmx.2016.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 01/19/2016] [Accepted: 01/19/2016] [Indexed: 11/20/2022] Open
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Alcaraz R, Martínez A, Rieta JJ. Role of the P-wave high frequency energy and duration as noninvasive cardiovascular predictors of paroxysmal atrial fibrillation. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 119:110-119. [PMID: 25758369 DOI: 10.1016/j.cmpb.2015.01.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Revised: 12/14/2014] [Accepted: 01/21/2015] [Indexed: 06/04/2023]
Abstract
A normal cardiac activation starts in the sinoatrial node and then spreads throughout the atrial myocardium, thus defining the P-wave of the electrocardiogram. However, when the onset of paroxysmal atrial fibrillation (PAF) approximates, a highly disturbed electrical activity occurs within the atria, thus provoking fragmented and eventually longer P-waves. Although this altered atrial conduction has been successfully quantified just before PAF onset from the signal-averaged P-wave spectral analysis, its evolution during the hours preceding the arrhythmia has not been assessed yet. This work focuses on quantifying the P-wave spectral content variability over the 2h preceding PAF onset with the aim of anticipating as much as possible the arrhythmic episode envision. For that purpose, the time course of several metrics estimating absolute energy and ratios of high- to low-frequency power in different bands between 20 and 200Hz has been computed from the P-wave autoregressive spectral estimation. All the analyzed metrics showed an increasing variability trend as PAF onset approximated, providing the P-wave high-frequency energy (between 80 and 150Hz) a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF and patients less than 1h close to a PAF episode. This discriminant power was similar to that provided by the most classical time-domain approach, i.e., the P-wave duration. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 88.07%, thus constituting a reliable noninvasive harbinger of PAF onset with a reasonable anticipation. The information provided by this methodology could be very useful in clinical practice either to optimize the antiarrhythmic treatment in patients at high-risk of PAF onset and to limit drug administration in low risk patients.
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Affiliation(s)
- Raúl Alcaraz
- Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Spain..
| | - Arturo Martínez
- Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Spain
| | - José J Rieta
- Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, Spain
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Alcaraz R, Martínez A, Rieta JJ. The P Wave Time-Frequency Variability Reflects Atrial Conduction Defects before Paroxysmal Atrial Fibrillation. Ann Noninvasive Electrocardiol 2014; 20:433-45. [PMID: 25418673 DOI: 10.1111/anec.12240] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND The study of atrial conduction defects associated with the onset of paroxysmal atrial fibrillation (PAF) can be addressed by analyzing the P wave from the surface electrocardiogram (ECG). Traditionally, signal-averaged ECGs have been mostly used for this purpose. However, this alternative hinders the possibility to quantify every single P wave, its variability over time, as well as to obtain complimentary and evolving information about the arrhythmia. This work analyzes the time progression of several time and frequency P wave features as potential indicators of atrial conduction variability several hours preceding the onset of PAF. METHODS The longest sinus rhythm interval from 24-hour Holter recordings of 46 PAF patients was selected. Next, the 2 hours before the onset of PAF were extracted and divided into two 1-hour periods. Every single P wave was automatically delineated and characterized by 16 time and frequency metrics, such as its duration, absolute energy in several frequency bands and high-to-low-frequency energy ratios. Finally, the P wave variability over each 1-hour period was estimated from the 16 features making use of a least-squares linear fitting. As a reference, the same parameters were also estimated from a set of 1-hour ECG segments randomly chosen from a control group of 53 healthy subjects age-, gender-, and heart rate-matched. RESULTS All the analyzed metrics provided an increasing P wave variability trend as the onset of PAF approximated, being P wave duration and P wave high-frequency energy the most significant individual metrics. The linear fitting slope α associated with P wave duration was (2.48 ± 1.98)×10(-2) for healthy subjects, (23.8 ± 14.1)×10(-2) for ECG segments far from PAF and for (81.8 ± 48.7)×10(-2) ECG segments close to PAF p = 6.96×10(-22) . Similarly, the P wave high-frequency energy linear fitting slope was (2.42 ± 4.97)×10(-9) , (54.2 ± 107.1)×10(-9) and (274.2 ± 566.1)×10(-9) , respectively (p = 2.85×10(-20) ). A univariate discriminant analysis provided that both P wave duration and P wave high-frequency energy could discern among the three ECG sets with diagnostic ability around 80%, which was improved up to 88% by combining these metrics in a multivariate discriminant analysis. CONCLUSION Alterations in atrial conduction can be successfully quantified several hours before the onset of PAF by estimating variability over time of several time and frequency P wave features.
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Affiliation(s)
- Raúl Alcaraz
- Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Cuenca, Spain
| | - Arturo Martínez
- Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Cuenca, Spain
| | - José J Rieta
- Biomedical Synergy, Electronic Engineering Department, Universidad Politécnica de Valencia, Valencia, Spain
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Martínez A, Alcaraz R, Rieta JJ. Gaussian modeling of the P-wave morphology time course applied to anticipate paroxysmal atrial fibrillation. Comput Methods Biomech Biomed Engin 2014; 18:1775-84. [PMID: 25298113 DOI: 10.1080/10255842.2014.964219] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
This paper introduces a new algorithm to quantify the P-wave morphology time course with the aim of anticipating as much as possible the onset of paroxysmal atrial fibrillation (PAF). The method is based on modeling each P-wave with a single Gaussian function and analyzing the extracted parameters variability over time. The selected Gaussian approaches are associated with the amplitude, peak timing, and width of the P-wave. In order to validate the algorithm, electrocardiogram segments 2 h preceding the onset of PAF episodes from 46 different patients were assessed. According to the expected intermittently disturbed atrial conduction before the onset of PAF, all the analyzed Gaussian metrics showed an increasing variability trend as the PAF onset approximated. Moreover, the Gaussian P-wave width reported a diagnostic accuracy around 80% to discern between healthy subjects, patients far from PAF, and patients less than 1 h close to a PAF episode. This discriminant power was similar to those provided by the most classical time-domain approach, i.e., the P-wave duration. However, this newly proposed parameter presents the advantage of being less sensitive to a precise delineation of the P-wave boundaries. Furthermore, the linear combination of both metrics improved the diagnostic accuracy up to 86.69%. In conclusion, morphological P-wave characterization provides additional information to the metrics based on P-wave timing.
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Affiliation(s)
- Arturo Martínez
- a Innovation in Bioengineering Research Group , University of Castilla-La Mancha , Cuenca , Spain
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Martínez A, Alcaraz R, Rieta JJ. Morphological variability of the P-wave for premature envision of paroxysmal atrial fibrillation events. Physiol Meas 2013; 35:1-14. [PMID: 24345763 DOI: 10.1088/0967-3334/35/1/1] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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10
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Martínez A, Alcaraz R, Rieta JJ. Study on the P-wave feature time course as early predictors of paroxysmal atrial fibrillation. Physiol Meas 2012; 33:1959-74. [DOI: 10.1088/0967-3334/33/12/1959] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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11
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Voss A, Schulz S, Schroeder R. Monitoring in cardiovascular disease patients by nonlinear biomedical signal processing. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6564-7. [PMID: 22255843 DOI: 10.1109/iembs.2011.6091619] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Due to recent advances in technology extensive cardiovascular monitoring is widely introduced today. An essential component of cardiovascular monitoring is the analysis of several biosignals as electrocardiogram, blood pressure and other vital signs. This manuscript provides an overview about several application fields of cardiovascular monitoring with the main focus on nonlinear dynamics analysis.
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Affiliation(s)
- A Voss
- Department of Medical Engineering and Biotechnology, University of Applied Sciences Jena, Carl-Zeiss-Promenade 2, 07745 Jena, Germany. voss@ fhjena.de
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