The contribution of intermittent handheld electrocardiogram and continuous electrocardiogram monitoring with an implantable loop recorder to detect incident and recurrent atrial fibrillation during 1 year after coronary artery bypass graft surgery: A prospective cohort study

Sandgren, Emma; Wickbom, Anders; Kalm, Torbjörn; Ahlsson, Anders; Edvardsson, Nils; Engdahl, Johan

doi:10.1016/j.hroo.2021.05.001

Cited by 9 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pacing capture from surface electrocardiogram (ECG) monitoring is a common bedside technique [ 9 ], also known as blind insertion, which easily leads to atrial perforation, and the probability of implantation in the best position (right ventricular apex) is only 44%. Blind insertion (ECG monitoring on body surface) and implantation under X-ray cannot judge the degree of contact between the electrode tip and electrode [ 10 , 11 ]. Using these two methods, it is not uncommon for electrodes to insert into the myocardium and penetrate through the atrial or ventricular wall in severe cases.…”

Section: Introductionmentioning

confidence: 99%

Evidence-Based Analysis of the Emergency Temporary Cardiac Pacing (Electrical Stimulation from Metal Wire Electrode)

Lin

Tan

et al. 2021

Bioinorganic Chemistry and Applications

View full text Add to dashboard Cite

The patient’s Cr (creatinine), BUN (blood urea nitrogen), HBG (hemoglobin), VT (ventricular tachycardia), pacing frequency, puncture point, emergency to permanent pacing time, pacing current (mA), pacing threshold current (mA), and admission diagnosis data were collected. The data were subjected to frequency statistics, curve regression analysis, PLS regression analysis, adjustment analysis, chi-square test, ridge regression analysis, discriminant analysis, negative binomial regression analysis, Poisson regression analysis, and stepwise regression analysis. Some findings include the following: (1) Cr has a significant positive effect on HBG, and BUN has a significant negative effect on HBG. (2) VT has a negative correlation with age and a positive correlation with CK-MB and CK (creatinine kinase). (3) Myocarditis has a negative correlation with age and a significant positive correlation with CTnI (cardiac troponin I). (4) AST (aspartate transaminase) and ALT (alanine aminotransferase) have a significant positive impact on DDI (D-dimer), while CTnI has a significant negative impact on DDI. MYO (myoglobin) has no impact relationship to DDI. (5) ALT has a significant positive relationship with APTT (partial thromboplastin time). (6) Alb (albumin) and TBIL (total bilirubin) have a significant positive effect on PLT (platelet) count, while pro-BNP (B-type natriuretic peptide) and MYO have a significant negative effect on PLT. (7) CK has a significant positive effect on INR (international normalized ratio). (8) The relationship between sinus node dysfunction and VT significantly affect the pacing frequency (beats/minute). For third-degree atrioventricular block, different samples of sinus node dysfunction showed significant differences. (9) There is a significant positive correlation between pacing current (mA) and pacing threshold current (mA). (10) There was a significant positive correlation between perceived voltage (mV) and the time from emergency to permanent pacing. Admission diagnosis has a significant positive impact on the time from emergency to permanent pacing. The change (increase) in time from emergency to permanent pacing was 1.137-fold when an additional condition was diagnosed on admission.

show abstract

Section: Introductionmentioning

confidence: 99%

Evidence-Based Analysis of the Emergency Temporary Cardiac Pacing (Electrical Stimulation from Metal Wire Electrode)

Lin

Tan

et al. 2021

Bioinorganic Chemistry and Applications

View full text Add to dashboard Cite

show abstract

“…In a study comparing 30-day continuous electrocardiogram monitoring with standard of care, the underreporting of subclinical POAF in cardiac surgery patients was highlighted by a 17.9% absolute difference in AF detection rate . Using continuous cardiac rhythm monitoring, 2 studies demonstrated that both the incidence of POAF and the AF recurrence rate after CABG are most likely underestimated …”

Section: Discussionmentioning

confidence: 99%

Recurrence of Atrial Fibrillation in Patients With New-Onset Postoperative Atrial Fibrillation After Coronary Artery Bypass Grafting

Herrmann,

Taha,

Nielsen

et al. 2024

JAMA Netw Open

View full text Add to dashboard Cite

ImportanceNew-onset postoperative atrial fibrillation (POAF) occurs in approximately 30% of patients undergoing coronary artery bypass grafting (CABG). It is unknown whether early recurrence is associated with worse outcomes.ObjectiveTo test the hypothesis that early AF recurrence in patients with POAF after CABG is associated with worse outcomes.Design, Setting, and ParticipantsThis Swedish nationwide cohort study used prospectively collected data from the SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) registry and 3 other mandatory national registries. The study included patients who underwent isolated first-time CABG between January 1, 2007, and December 31, 2020, and developed POAF. Data analysis was performed between March 6 and September 16, 2023.ExposureEarly AF recurrence defined as an episode of AF leading to hospital care within 3 months after discharge.Main Outcomes and MeasuresThe primary outcome was all-cause mortality. Secondary outcomes included ischemic stroke, any thromboembolism, heart failure hospitalization, and major bleeding within 2 years after discharge. The groups were compared with multivariable Cox regression models, with early AF recurrence as a time-dependent covariate. The hypothesis tested was formulated after data collection.ResultsOf the 35 329 patients identified, 10 609 (30.0%) developed POAF after CABG and were included in this study. Their median age was 71 (IQR, 66-76) years. The median follow-up was 7.1 (IQR, 2.9-9.0) years, and most patients (81.6%) were men. Early AF recurrence occurred in 6.7% of patients. Event rates (95% CIs) per 100 patient-years with vs without early AF recurrence were 2.21 (1.49-3.24) vs 2.03 (1.83-2.25) for all-cause mortality, 3.94 (2.92-5.28) vs 2.79 (2.56-3.05) for heart failure hospitalization, and 3.97 (2.95-5.30) vs 2.74 (2.51-2.99) for major bleeding. No association between early AF recurrence and all-cause mortality was observed (adjusted hazard ratio [AHR], 1.17 [95% CI, 0.80-1.74]; P = .41). In exploratory analyses, there was an association with heart failure hospitalization (AHR, 1.80 [95% CI, 1.32-2.45]; P = .001) and major bleeding (AHR, 1.92 [1.42-2.61]; P &lt; .001).Conclusions and RelevanceIn this cohort study of early AF recurrence after POAF in patients who underwent CABG, no association was found between early AF recurrence and all-cause mortality. Exploratory analyses showed associations between AF recurrence and heart failure hospitalization, oral anticoagulation, and major bleeding.

show abstract

“…Consequently, longer ECG monitoring duration may be necessary to increase the chances of detecting AF episodes. Additionally, ECG requires direct contact with electrodes placed on the body, which can be inconvenient for continuous monitoring in everyday settings [38].…”

Section: The Role Of Ecg In Af Detectionmentioning

confidence: 99%

A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

et al. 2023

View full text Add to dashboard Cite

Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model’s capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.

show abstract

The contribution of intermittent handheld electrocardiogram and continuous electrocardiogram monitoring with an implantable loop recorder to detect incident and recurrent atrial fibrillation during 1 year after coronary artery bypass graft surgery: A prospective cohort study

Cited by 9 publications

References 38 publications

Evidence-Based Analysis of the Emergency Temporary Cardiac Pacing (Electrical Stimulation from Metal Wire Electrode)

Evidence-Based Analysis of the Emergency Temporary Cardiac Pacing (Electrical Stimulation from Metal Wire Electrode)

Recurrence of Atrial Fibrillation in Patients With New-Onset Postoperative Atrial Fibrillation After Coronary Artery Bypass Grafting

A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

Contact Info

Product

Resources

About