Electrocardiogram (ECG) for the Prediction of Incident Atrial Fibrillation: An Overview

Longitudinal association of premature atrial contractions with atrial fibrillation and brain ischemia in people with type 2 diabetes: The Hoorn Diabetes Care System cohort

Background

Premature atrial contractions (PACs) are potential markers for imminent onset of both atrial fibrillation (AF) and brain ischemia (BI; transient ischemic attack [TIA] or ischemic stroke). We investigated the association of PACs with incident AF and BI events separately, and of incident AF with BI events in people with type 2 diabetes (T2D) without pre-existing AF or cerebrovascular disease.

Methods

A prospective longitudinal study of 12,242 people with T2D without known AF or cerebrovascular disease from the Hoorn Diabetes Care System cohort. Annual measurements (1998-2018) included cardiovascular risk factors, over 85,000 ECGs, and self-reported cardiovascular events. We assessed the association of PACs with incident AF and BI events and of incident AF with BI events using time-dependent Cox-regression models, adjusted for time-varying cardiovascular risk factors and medication use (Hazard Ratios with 95%CIs).

Results

The baseline mean age was 62.2 ± 11.9 years. During a median follow-up of 7.0 (IQR 3.4-11.0) years, 1031 (8.4 %) participants had PACs, and 566 (4.6 %) had incident AF at any of the median 6 (IQR 3-10) annual ECG recordings. BI events occurred in 517 (4.2 %) people (304 TIAs, 213 ischemic strokes). After adjustment, PACs were associated with incident AF (Hazard Ratio, 1.96 (95%CI, 1.53-2.50)), but not with overall BI events (1.09 (0.76-1.56)), or with TIA (0.91 (0.57-1.46)) or ischemic stroke (1.50 (0.88-2.54)) separately. AF was not associated with BI events (0.95 (0.55-1.63)).

Conclusions

In people with T2D without a history of AF or BI events, PACs are associated with a two-fold increased risk of incident AF.

Detection of Atrial Fibrillation Using Insertable Cardiac Monitors in Patients With Cryptogenic Stroke in Japan (the LOOK Study): Protocol for a Prospective Multicenter Observational Study

BACKGROUND

Paroxysmal atrial fibrillation (AF) is a probable cause of cryptogenic stroke (CS), and its detection and treatment are important for the secondary prevention of stroke. Insertable cardiac monitors (ICMs) are clinically effective in screening for AF and are superior to conventional short-term cardiac monitoring. Japanese guidelines for determining clinical indications for ICMs in CS are stricter than those in Western countries. Differences between Japanese and Western guidelines may impact the detection rate and prediction of AF via ICMs in patients with CS. Available data on Japanese patients are limited to small retrospective studies. Furthermore, additional information about AF detection, including the number of episodes, cumulative episode duration, anticoagulation initiation (type and dose of regimen and time of initiation), rate of catheter ablation, role of atrial cardiomyopathy, and stroke recurrence (time of recurrence and cause of the recurrent event), was not provided in the vast majority of previously published studies.

OBJECTIVE

In this study, we aim to identify the proportion and timing of AF detection and risk stratification criteria in patients with CS in real-world settings in Japan.

METHODS

This is a multicenter, prospective, observational study that aims to use ICMs to evaluate the proportion, timing, and characteristics of AF detection in patients diagnosed with CS. We will investigate the first detection of AF within the initial 6, 12, and 24 months of follow-up after ICM implantation. Patient characteristics, laboratory data, atrial cardiomyopathy markers, serial magnetic resonance imaging findings at baseline, 6, 12, and 24 months after ICM implantation, electrocardiogram readings, transesophageal echocardiography findings, cognitive status, stroke recurrence, and functional outcomes will be compared between patients with AF and patients without AF. Furthermore, we will obtain additional information regarding the number of AF episodes, duration of cumulative AF episodes, and time of anticoagulation initiation.

RESULTS

Study recruitment began in February 2020, and thus far, 213 patients have provided written informed consent and are currently in the follow-up phase. The last recruited participant (May 2021) will have completed the 24-month follow-up in May 2023. The main results are expected to be submitted for publication in 2023.

CONCLUSIONS

The findings of this study will help identify AF markers and generate a risk scoring system with a novel and superior screening algorithm for occult AF detection while identifying candidates for ICM implantation and aiding the development of diagnostic criteria for CS in Japan.

TRIAL REGISTRATION

UMIN Clinical Trial Registry UMIN000039809; https://tinyurl.com/3jaewe6a.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/39307.

Electrocardiographic Predictors of Atrial Fibrillation

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.

Stacked machine learning models to classify atrial disorders based on clinical ECG features: a method to predict early atrial fibrillation

OBJECTIVES

Atrial Tachycardia (AT) and Left Atrial Enlargement (LAE) are atrial diseases that are significant precursors to Atrial Fibrillation (AF). There are ML models for ECG classification; clinical features-based classification is required. The suggested work aims to create stacked ML models that categorize Sinus Rhythm (SR), Sinus Tachycardia (ST), AT, and LAE signals based on clinical parameters for AF prognosis.

METHODS

The classification was based on thirteen clinical parameters, such as amplitude, time domain ECG aspects, and P-Wave Indices (PWI), such as the ratio of P-wave length and amplitude ((P (ms)/P (µV)), P-wave area (µV*ms), and P-wave terminal force (PTFV1(µV*ms). Apart from classifying the ECG signals, the stacked ML models prioritized the clinical features using a pie formula-based technique.

RESULTS

The Stack 1 model achieves 99% accuracy, sensitivity, precision, and F1 score, while the Stack 2 model achieves 91%, 91%, 94%, and 92% for identifying SR, ST, LAE, and AT, respectively. Both stack models obtained a computational time of 0.06 seconds. PTFV1 (µV*ms), P (ms)/P (µV)), and P-wave area (µV*ms) were ranked as crucial clinical features.

CONCLUSION

Clinical feature-based stacking ML models may help doctors obtain insight into important clinical ECG aspects for early AF prediction.

Assessment of P Wave Indices in Healthy Standardbred Horses

P wave indices are used as non-invasive electrocardiographic markers of atrial remodelling in humans. Few studies have investigated their use in animals. The aim of this study was to measure P wave duration and P wave dispersion (Pd) in healthy standardbred horses and investigate variables that might influence these measurements. A 12-lead electrocardiogram was recorded at rest and P wave indices were calculated in 53 horses. A general linear model was used to investigate the main effects: age, bodyweight, sex, resting heart rate, presence of a murmur, exercise status and the number of years raced. There were significant associations with exercise status for both the maximum P wave duration and Pd, with both values being increased in strenuously exercising versus non-active horses. Furthermore, a significant moderate positive correlation was identified between the duration of exercise (number of years raced) and both Pmax and Pd. No other significant associations were identified. These findings are similar to those reported in elite human athletes versus sedentary individuals. The increases in these P wave indices most likely occur due to prolongation and heterogeneity in atrial conduction time, which are associated with structural and electrical remodelling, and may explain the increased risk of atrial fibrillation in athletic horses.

Propofol anesthesia decreases the incidence of new-onset postoperative atrial fibrillation compared to desflurane in patients undergoing video-assisted thoracoscopic surgery: A retrospective single-center study

BACKGROUND

Postoperative atrial fibrillation (POAF) increases postoperative morbidity, mortality, and length of hospital stay. Propofol is reported to modulate atrial electrophysiology and the cardiac autonomic nervous system. Therefore, we retrospectively examined whether propofol suppresses POAF in patients undergoing video-assisted thoracoscopic surgery (VATS) compared to desflurane.

METHODS

We retrospectively recruited adult patients who underwent VATS during the period from January 2011 to May 2018 in an academic university hospital. Between continuous propofol and desflurane administration during anesthetic maintenance, we investigated the incidence of new-onset POAF (within 48 hours after surgery) before and after propensity score matching.

RESULTS

Of the 482 patients, 344 received propofol, and 138 received desflurane during anesthetic maintenance. The incidence of POAF in the propofol group was less than that in the desflurane group (4 [1.2%] vs. 8 patients [5.8%], odds ratio [OR]; 0.161, 95% confidence interval (CI), 0.040-0.653, p = 0.011) in the present study population. After adjustment for propensity score matching (n = 254, n = 127 each group), the incidence of POAF was still less in propofol group than desflurane group (1 [0.8%] vs. 8 patients [6.3%], OR; 0.068, 95% CI: 0.007-0.626, p = 0.018).

CONCLUSIONS

These retrospective data suggest propofol anesthesia significantly inhibits POAF compared to desflurane anesthesia in patients undergoing VATS. Further prospective studies are needed to elucidate the mechanism of propofol on the inhibition of POAF.

The predictive value of abnormal P-wave axis for the detection of incident atrial fibrillation: A systematic review with meta-analysis

INTRODUCTION

There is growing interest in the prediction of incident atrial fibrillation (AF). The 12-lead electrocardiogram (ECG) has been a particularly rich target for possible prediction strategies.

PURPOSE

The P-wave axis is an ECG parameter that reflects the dominant vector of atrial depolarisation and is usually 0° -75°. There is a large body of literature suggesting that AF reflects structural and conduction abnormalities of the atria, and thus the P-wave axis may represent a sensitive parameter to detect such changes.

METHODS

A systematic review and meta-analysis of published literature associating abnormal P-wave axis and the development of incident AF was performed. Electronic databases were systematically searched from inception to October 2021. A random-effects model with generic inverse variance weights was utilised to pool the most adjusted effect measure from each paper. A funnel plot was used to assess publication bias.

RESULTS

After excluding duplicate studies, 568 studies were screened. A total of eleven studies were identified that associated an abnormal P-wave axis with the subsequent detection of AF. The eight studies that considered abnormal P-wave axis as being <0° or >75° were pooled for meta-analysis. In the pooled studies a total of 78,222 patients were included with 5656 cases of incident atrial fibrillation identified. The meta-analysis of the studies suggested that an abnormal P-wave axis was associated with a pooled risk ratio of 2.12 (95% CI 1.49 to 3.01) for the detection of incident atrial fibrillation.

CONCLUSION

This comprehensive systematic review and meta-analysis, indicates the positive association of abnormal P wave axis and future detection of AF. Utilisation of abnormal P-wave axis, alongside other parameters, may allow clinicians to better risk-stratify individuals at increased risk of AF, and thus identify those who may benefit most from prolonged cardiac monitoring or targeted anticoagulation.

Atrial fibrillation prediction by combining ECG markers and CMR radiomics

Atrial fibrillation (AF) is the most common cardiac arrhythmia. It is associated with a higher risk of important adverse health outcomes such as stroke and death. AF is linked to distinct electro-anatomic alterations. The main tool for AF diagnosis is the Electrocardiogram (ECG). However, an ECG recorded at a single time point may not detect individuals with paroxysmal AF. In this study, we developed machine learning models for discrimination of prevalent AF using a combination of image-derived radiomics phenotypes and ECG features. Thus, we characterize the phenotypes of prevalent AF in terms of ECG and imaging alterations. Moreover, we explore sex-differential remodelling by building sex-specific models. Our integrative model including radiomics and ECG together resulted in a better performance than ECG alone, particularly in women. ECG had a lower performance in women than men (AUC: 0.77 vs 0.88, p < 0.05) but adding radiomics features, the accuracy of the model was able to improve significantly. The sensitivity also increased considerably in women by adding the radiomics (0.68 vs 0.79, p < 0.05) having a higher detection of AF events. Our findings provide novel insights into AF-related electro-anatomic remodelling and its variations by sex. The integrative radiomics-ECG model also presents a potential novel approach for earlier detection of AF.

Electrocardiographic Risk Factors for a Common Cardiac Condition

This case report describes a patient in their 60s with a history of several months of recurrent dizziness and occasional palpitation who presented to the emergency department after an episode of syncope.

Impact of P-wave indices in prediction of atrial fibrillation-Insight from loop recorder analysis

BACKGROUND

Several P-wave indices are associated with the development of atrial fibrillation (AF). However, previous studies have been limited in their ability to reliably diagnose episodes of AF. Implantable loop recorders allow long-term, continuous, and therefore more reliable detection of AF.

HYPOTHESIS

The aim of this study is to identify and evaluate ECG parameters for predicting AF by analyzing patients with loop recorders.

METHODS

This study included 366 patients (mean age 62 ± 16 years, mean LVEF 61 ± 6%, 175 women) without AF who underwent loop recorder implantation between 2010-2020. Patients were followed up on a 3 monthly outpatient interval.

RESULTS

During a follow-up of 627 ± 409 days, 75 patients (20%) reached the primary study end point (first detection of AF). Independent predictors of AF were as follows: age ≥68 years (hazard risk [HR], 2.66; 95% confidence interval [CI], 1.668-4.235; p < .001), P-wave amplitude in II <0.1 mV (HR, 2.11; 95% CI, 1.298-3.441; p = .003), P-wave terminal force in V₁  ≤ -4000 µV × ms (HR, 5.3; 95% CI, 3.249-8.636; p < .001, and advanced interatrial block (HR, 5.01; 95% CI, 2.638-9.528; p < .001). Our risk stratification model based on these independent predictors separated patients into 4 groups with high (70%), intermediate high (41%), intermediate low (18%), and low (4%) rates of AF.

CONCLUSIONS

Our study indicated that P-wave indices are suitable for predicting AF episodes. Furthermore, it is possible to stratify patients into risk groups for AF using simple ECG parameters, which is particularly important for patients with cryptogenic stroke.

Cardiac electrical and structural alterations in preeclampsia

BACKGROUND

Preeclampsia increases the risk of cardiovascular mortality and morbidity both during pregnancy and long term after the labor by causing cardiac changes that may lead to atrial and ventricular arrhythmias.

OBJECTIVE

We aimed to investigate noninvasive predictors of atrial and ventricular arrhythmias and cardiac structural changes in preeclampsia.

METHODS

The study included 34 preeclampsia patients as the study group and 33 healthy pregnant women as the control group. The presence of fragmented QRS morphology, P dispersion, QT dispersion, Tp-e/QTc ratio, inter- and intra-atrial electromechanical delay, left ventricular mass index was evaluated in the groups by 12 lead- ECG and standard and tissue Doppler echocardiography.

RESULTS

Left ventricular mass index and relative wall thickness, and E/Em ratio was significantly higher in preeclampsia. Inter- and intra-atrial electromechanical delay and Tp-e were prolonged, and P dispersion, QT dispersion, and Tp-e/QTc ratio were increased significantly in patients with preeclampsia. In addition, fragmented QRS morphology was seen in 76.5% of patients with preeclampsia while it was present in only 27.3% of the control group (p < .001).

CONCLUSION

Preeclampsia causes significant cardiac structural and electrocardiographic alterations that may increase the risk of atrial and/or ventricular arrhythmias. A more thorough and earlier cardiac assessment and closer follow-up of these patients would be useful to avoid further complications.

Relation of p - wave dispersion with presystolic a - wave and aortic valve sclerosis in asymptomatic subjects

BACKGROUND

P-wave duration and P-wave dispersion (PWD) are thought to be the surrogate marker of devoloping atrial fibrillation (AF). The main purpose of present study was to investigate the association between presystolic wave (PSW), aortic valve sclerosis, and PWD.

PATIENTS AND METHODS

Patients with sinus rhythm admitted to the cardiology outpatient clinic were consecutively enrolled. Maximum (Pmax) and minimum (Pmin) P-wave duration and PWD were measured. Echocardiography was used to assess the aortic valve morphology and presence of PSW. The patients were divided into two groups according to presence or absence of AVSc and PSW.

RESULTS

A total of 100 patients were enrolled consecutively. Patients with both PSW and AVSc had higher PWD values compared with those without PSW (42 ± 15 vs 65 ± 20) and AVSc (52 ± 21 vs 69 ± 19). The patients were categorized on the basis of median PWD values. According to univariate analysis, there was significant association between PWD and presence of PSW (P: .004), presence of AVS (P: .011), hypertension (P: .01) interventricular septal thickness (IVST) (P: .026), and posterior wall thickness (PWT) (P: .022). Multivariate logistic regression analyses demonstrated presence of PSW (95% confidence interval (1.058-6.505, P: .037) as an independent determinant of PWD.

CONCLUSION

Assessment of presystolic A-wave on echocardiography examination may provide important information regarding the atrial conduction velocities that is a electrophysiological cause of AF. While there was a significant association between AVSc and PWD in univariate analysis, this significance disappeared in multivariate analysis.

Bayés' Syndrome-A Comprehensive Short Review

Prediction and early detection of atrial fibrillation (AF) remain a permanent challenge in everyday practice. Timely identification of an increased risk for AF episodes (which are frequently asymptomatic) is essential in the primary and secondary prevention of cardioembolic events. One of the noninvasive modalities of AF prediction is represented by the electrocardiographic P-wave analysis. This includes the study and diagnosis of interatrial conduction block (Bachmann’s bundle block). Bayés’ Syndrome (named after its first descriptor) denotes the association between interatrial conduction defect and supraventricular arrhythmias (mainly AF) predisposing to cardioembolic events. Our short review presents an update of the most important data concerning this syndrome: brief history, main ECG features, pathophysiological background and clinical implications.

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.

Subclinical cardiac structural and electrical abnormalities in fibromyalgia syndrome

Background/aim

In the literature, there is a paucity of data about the effects of fibromyalgia syndrome (FMS) on myocardial function and electrophysiological properties of atrium and ventricles. In this study, we investigated cardiac functions and noninvasive predictors of arrhythmias in patients with FMS.

Materials and methods

The study included 43 female patients diagnosed with FMS and 30 age- and sex-matched healthy subjects. The presence of fragmented QRS (fQRS) morphology, P dispersion, QT dispersion, inter- and intraatrial electromechanical delay was evaluated in the groups with 12-lead ECG and standard and tissue Doppler echocardiography.

Results

Among electrocardiographic parameters, P dispersion, QT dispersion, and the ratio of presence of fQRS morphology were found to be significantly higher in the study group as compared to the control group. In lateral and septal, the ratio of the early transmitral flow velocity to the early diastolic tissue velocity (E/Em) was significantly higher in the study group. Additionally, intra- and interatrial electromechanical delay was found significantly prolonged in the study group.

Conclusion

FMS is found to be associated with significant cardiac electrical alterations that may indicate the increased risk of atrial and ventricular arrhythmias in this group of patients.

P-wave indices as predictors of atrial fibrillation

Background

P‐wave duration (P_DURATION) and P‐wave area (P_AREA) have been linked to risk of atrial fibrillation (AF), but they do not improve the efficacy of Framingham AF risk score. We suggest the incorporation of both variables in one index, the P‐wave area/P‐wave duration (P_{AREA/DURATION}) index, which may be considered an expression of the average amplitude of the P wave that reflects aspects of P‐wave morphology.

Objective

To assess the prognostic value of P‐wave area/P‐wave duration index (P_{AREA/DURATION} index) in lead II together with other P‐wave indices (PWIs) in incidence of AF in the Copenhagen Holter Study.

Methods

The study included 632 men and women, between 55 and 75 years with no apparent heart disease or AF. Baseline standard 12‐lead Electrocardiography (ECGs) were analyzed manually.

Results

The median follow‐up time was 14.7 (14.5;14.9) years. A total of 68 cases of AF and 233 cases of death were recorded. The restricted cubic spline method showed a U‐shaped association between P_{AREA/DURATION} and rate of AF. The lowest quintile of P_{AREA/DURATION} index in lead II was associated with increased rate of AF, HR 2.80 (1.64–4.79). The addition of the new index to the Framingham model for AF improved the model in this population. The P_AREA in lead II in its lowest quintile was also associated with increased rate of AF, HR 2.16 (1.25–3.75), but did not improve the Framingham model. P_DURATION and P‐wave terminal force (PTF) were not significantly associated with AF.

Conclusion

A flat P wave as expressed by a small P_{AREA/DURATION} index in lead II is associated with increased rate of incident AF beyond known AF risk factors.

Analysis of variability of R-R intervals for the diagnosis of atrial fibrillation: A new algorithm

Differences in successive R-R intervals (RRIs) were normalized by RRIs before and after the indexing beats (normalized DRs) in individuals with normal sinus rhythm (NSR) and 98.89% of normalized DRs were found to distribute within mean±0.100 (≒mean±3SD), whereas 73.47% were out of this range in atrial fibrillation (AF). When 7 out 20 normalized DRs fell outside of 0.000±0.100, NSR (n=129) and AF (n=108) could be discriminated with high sensitivity, specificity, and predictive values (>99.0% for all). This method will be used in detecting AF candidates from a small number of heart beats or arterial pulses.