P-wave indices as predictors of atrial fibrillation

Predictors of atrial fibrillation after embolic stroke of undetermined source in patients with implantable loop recorders

BACKGROUND

In patients with embolic stroke of undetermined source (ESUS), underlying subclinical atrial fibrillation (AF) is often suspected. Previous studies identifying predictors of AF have been limited in their ability to diagnose episodes of AF. Implantable loop recorders enable prolonged, continuous, and therefore more reliable detection of AF. The aim of this study was to identify clinical and ECG parameters as predictors of AF in ESUS patients with implantable loop recorders.

METHODS

101 ESUS patients who received an implantable loop recorder between 2012 and 2020 were included in this study. Patients were followed up regularly on a three-monthly outpatient interval.

RESULTS

During a mean follow-up of 647 ± 385 days, AF was detected in 26 patients (26%). Independent risk factors of AF were age ≥ 60 years (HR 2.753, CI 1.129-6.713, p = 0.026), P-wave amplitude in lead II ≤ 0.075 mV (HR 3.751, CI 1.606-8.761, p = 0.002), and P-wave duration ≥ 125 ms (HR 4.299, CI 1.844-10.021, p < 0.001). In patients without risk factors, the risk of developing AF was 16%. In the presence of one risk factor, the probability increased only slightly to 18%. With two or three risk factors, the risk of AF increased to 70%.

CONCLUSION

AF was detected in about one in four patients after ESUS in this study. A comprehensive evaluation involving multiple parameters and the existence of multiple risk factors yields the highest predictive accuracy for detecting AF in patients with ESUS.

Electrocardiogram prediction of atrial fibrillation risk after stroke: A protocol for systematic review and meta-analysis

BACKGROUND

Atrial fibrillation (AF) is one of the most common clinical arrhythmias. This study aims to predict the risk of post-stroke AF through electrocardiographic changes in sinus rhythm.

METHODS

We searched the MEDLINE (PubMed) and EMBASE databases to identify relevant research articles published until August 2023. Prioritized items from systematic reviews and meta-analyses were screened, and data related to AF detection rate were extracted. A meta-analysis using a random-effects model was conducted for data synthesis and analysis.

RESULTS

A total of 32 studies involving electrocardiograms (ECG) were included, with a total analysis population of 330,284 individuals. Among them, 16,662 individuals (ECG abnormal group) developed AF, while 313,622 individuals (ECG normal group) did not. ECG patterns included terminal P-wave terminal force V1, interatrial block (IAB), advanced interatrial block, abnormal P-wave axis, pulse rate prolongation, and atrial premature complexes. Overall, 15,762 patients experienced AF during the study period (4.77%). In the ECG abnormal group, the proportion was 14.21% (2367/16,662), while in the control group (ECG normal group), the proportion was 4.27% (13,395/313,622). The pooled risk ratio for developing AF was 2.45 (95% confidence interval [CI]: 2.02-2.98, P < .001), with heterogeneity (I2) of 95%. The risk ratio values of alAB, P-wave terminal force V1, interatrial block, abnormal P-wave axis, pulse rate prolongation and atrial premature complexes were 4.12 (95% CI, 2.99-5.66), 1.47 (95% CI, 1.19-1.82), 2.54 (95% CI, 1.83-3.52), 1.70 (95% CI, 0.98-2.97), 2.65 (95% CI, 1.88-3.72), 3.79 (95% CI, 2.12-6.76), respectively.

CONCLUSION

There is a significant correlation between ECG patterns and the occurrence of AF. The alAB exhibited the highest level of predictability for the occurrence of AF. These indicators support their use as screening tools to identify high-risk individuals who may benefit from further examinations or empirical anticoagulation therapy following stroke.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

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.

Influence of epicardial adipose tissue inflammation and adipocyte size on postoperative atrial fibrillation in patients after cardiovascular surgery

Epicardial adipose tissue (EAT) is an active endocrine organ that is closely associated with occurrence of atrial fibrillation (AF). However, the role of EAT in the development of postoperative AF (POAF) remains unclear. We aimed to investigate the association between EAT profile and POAF occurrence in patients who underwent cardiovascular surgery. We obtained EAT samples from 53 patients to evaluate gene expression, histological changes, mitochondrial oxidative phosphorylation (OXPHOS) capacity in the EAT, and protein secretion in EAT-conditioned medium. EAT volume was measured using computed tomography scan. Eighteen patients (34%) experienced POAF within 7 days after surgery. Although no significant difference was observed in EAT profile between patients with and without POAF, logistic regression analysis identified that the mRNA expression levels of tumor necrosis factor-alpha (TNF-α) were positively correlated and adipocyte size in the EAT was inversely correlated with onset of POAF, respectively. Mitochondrial OXPHOS capacity in the EAT was not associated with POAF occurrence; however, it showed an inverse correlation with adipocyte size and a positive correlation with adiponectin secretion. In conclusion, changes in the secretory profile and adipocyte morphology of the EAT, which represent qualitative aspects of the adipose tissue, were present before the onset of AF.

ECG predictors of AF: A systematic review (predicting AF in ischaemic stroke-PrAFIS)

In 25% of patients presenting with embolic stroke, a cause is not determined. Atrial fibrillation (AF) is a commonly identified mechanism of stroke in this population, particularly in older patients. Conventional investigations are used to detect AF, but can we predict AF in this population and generally? We performed a systematic review to identify potential predictors of AF on 12-lead electrocardiogram (ECG).

METHOD

We conducted a search of EMBASE and Medline databases for prospective and retrospective cohorts, meta-analyses or case-control studies of ECG abnormalities in sinus rhythm predicting subsequent atrial fibrillation. We assessed quality of studies based on the Newcastle-Ottawa scale and data were extracted according to PRISMA guidelines.

RESULTS

We identified 44 studies based on our criteria. ECG patterns that predicted the risk of developing AF included interatrial block, P-wave terminal force lead V1, P-wave dispersion, abnormal P-wave-axis, abnormal P-wave amplitude, prolonged PR interval, left ventricular hypertrophy, QT prolongation, ST-T segment abnormalities and atrial premature beats. Furthermore, we identified that factors such as increased age, high CHADS-VASC, chronic renal disease further increase the positive-predictive value of some of these parameters. Several of these have been successfully incorporated into clinical scoring systems to predict AF.

CONCLUSION

There are several ECG abnormalities that can predict AF both independently, and with improved predictive value when combined with clinical risk factors, and if incorporated into clinical risk scores. Improved and validated predictive models could streamline selection of patients for cardiac monitoring and initiation of oral anticoagulants.

P-wave Indices as Predictors of Atrial Fibrillation: The Lion from a Claw

The P wave, representing the electrical fingerprint of atrial depolarization, contains information regarding spatial and temporal aspects of atrial electrical-and potentially structural-properties. However, technical and biological reasons, including-but not limited to-the low amplitude of the P wave and large interindividual variations in normal or pathologic atrial electrical activity, make gathering and utilizing this information for clinical purposes a rather cumbersome task. However, even crude ECG descriptors, such as P-wave dispersion, have been shown to be of predictive value for assessing the probability that a patient already has or will shortly present with AF. More sophisticated methods of analyzing the ECG signal, on a single- or multi- beat basis, along with novel approaches to data handling, namely machine learning, seem to be leading up to more accurate and robust ways to obtain clinically useful information from the humble P wave.

Atrial and ventricular arrhythmia predictors with electrocardiographic parameters in myocardial infarction with non-obstructive coronary artery disease (MINOCA)

BACKGROUND

The clinical importance and recognition of myocardial infarction with non-obstructive coronary artery disease (MINOCA) is increasing. Nevertheless, no studies are investigating the risk of atrial fibrillation and ventricular arrhythmia in MINOCA patients. This study aimed to determine the risk of arrhythmia with electrocardiographic predictors in MINOCA patients.

METHODS

In this study, patients diagnosed with MINOCA and stable out-patients without significant lesions in their coronary arteries were compared. Morphology-voltage-Pwave duration electrocardiography (MPV ECG) score was used to determine atrial arrhythmia risk. QT interval and QT dispersion Tpeak-Tend time and Tpeak-Tend/QT interval were used to determine ventricular arrhythmia risk.

RESULTS

A total of 155 patients were included in our study. Seventy-seven of these patients were in the MINOCA group. There was no statistically significant difference between the two groups in MPV ECG score (1.95 ± 1.03 vs 1.68 ± 1.14, p = 0.128). P-wave voltage, P-wave morphology and P-wave duration, which are components of the MPV ECG score, were not statistically significantly different. The QRS complex duration (90.21 ± 14.87 vs 82.99 ± 21.59 ms, p = 0.017), ST interval (271.95 ± 45.91 vs 302.31 ± 38.40 ms, p < 0.001), corrected QT interval (438.17 ± 43.80 vs 421.41 ± 28.39, p = 0.005) and QT dispersion (60.75 ± 22.77 vs 34.19 ± 12.95, p < 0.001) were statistically significantly higher in the MINOCA group. The Tpeak-Tend (89.53 ± 32.16 vs 65.22 ± 18.11, p < 0.001), Tpeak-Tend/QT interval (0.2306 ± 0.0813 vs 0.1676 ± 0.0470, p < 0.001) and Tpeak-Tend/corrected QT interval (0.2043 ± 0.6997 vs 0.1551 ± 0.4310, p < 0.001) ratios were also significantly higher in patients with MINOCA.

CONCLUSIONS

In the MINOCA patients, there was no increase in the risk of atrial fibrillation based on ECG predictors. However, it was shown that there could be a significant increase in the risk of ventricular arrhythmia. We believe this study could be helpful for specific recommendations concerning duration of hospitalisation and follow up in MINOCA patients.

Predicting risk of AF in ischaemic stroke using sinus rhythm ECG abnormalities: A meta-analysis

OBJECTIVE

To identify ECG changes in sinus rhythm that may be used to predict subsequent development of new AF.

METHOD

We identified prospective and retrospective cohort or case control studies evaluating ECG patterns from a 12-lead ECG in sinus rhythm taken in hospital or community predicting subsequent development of new AF. For each identified ECG predictor, we then identify absolute event rates and pooled risk ratios (RR) using an aggregate level random effects meta-analysis.

RESULTS

We identified 609,496 patients from 22 studies. ECG patterns included P wave terminal force V1 (PTFV1), interatrial block (IAB) and advanced interatrial block (aIAB), abnormal P wave axis (aPWA), PR prolongation and atrial premature complexes (APCs). Pooled risk ratios reached significance for each of these; PTFV1 RR 1.48 (95% CI 1.04-2.10), IAB 2.54 (95% CI 1.64-3.93), aIAB 4.05 (95% CI 2.64-6.22), aPWA 1.89 (95% CI 1.25-2.85), PR prolongation 2.22 (95% CI 1.27-3.87) and APCs 3.71 (95% CI 2.23-6.16). Diabetes reduced the predictive value of PR prolongation.

CONCLUSION

APC and aIAB were most predictive of AF, while IAB, PR prolongation, PTFV1 and aPWA were also significantly associated with development of AF. These support their use in a screening tool to identify at risk cohorts who may benefit from further investigation, or following stroke, with empirical anticoagulation.

P-wave duration as a marker of atrial remodeling in patients referred to ablation for atrial fibrillation: A new stratification tool emerging?

Ablation of atrial fibrillation is one of the most widely applied invasive procedures in cardiovascular medicine, and populations with atrial fibrillation continuously rise. Recurrence rates are, however, consistently high, even in patients without severe comorbidities. Robust stratification algorithms to distinguish patients suitable for ablation are generally lacking. This is a fact caused by the inability to incorporate evidence of atrial remodeling and fibrosis, e.g., atrial remodeling, in the decision pathways. Cardiac magnetic resonance is a powerful tool in identifying fibrosis; however, it is costly and not routinely used. Electrocardiography has been generally underutilized in clinical practice during pre-ablative screening. One of the characteristics of the electrocardiogram that can give us valuable data depicting the existence and the extent of atrial remodeling and fibrosis is the duration of the P-wave. Currently, many studies support the implementation of P-wave duration in the routine practice of patient evaluation as a surrogate marker of existing atrial remodeling, that in turn predicts recurrence after ablation of atrial fibrillation. Further research is guaranteed to establish this electrocardiographic characteristic in our stratification quiver.

Inflammatory bowel disease and atrial fibrillation: a contemporary overview

Atrial fibrillation is the most common arrhythmia in clinical practice and it is associated with increased morbidity and mortality. Atrial fibrillation is linked with inflammatory signaling while inflammation and oxidative stress promote atrial remodeling promoting the development and perpetuation of the arrhythmia. On the other hand, inflammatory bowel disease (IBD) is considered a chronic inflammatory condition with flares and remissions. IBD has been associated with an increased risk of atherosclerotic cardiovascular disease but its relationship with atrial fibrillation has not been studied well. Recent epidemiological evidence indicates an association between IBD and atrial fibrillation, especially during flares/hospitalizations. This brief review provides a concise overview of all available data regarding the association between IBD and atrial fibrillation including the predictive role of electrocardiographic and echocardiographic markers. Several unresolved issues including the thromboembolic risk in this setting and the potential role of antiinflammatory interventions are also discussed.

CatBoost-based improved detection of P-wave changes in sinus rhythm and tachycardia conditions: a lead selection study

Examining P-wave morphological changes in Electrocardiogram (ECG) is essential for characterizing atrial arrhythmias. However, standard 12-lead ECGsuffer from diagnostic redundancy due to low signal-to-noise ratio of P-waves. To address this issue, various optimal leads have been proposed for improved atrial activity recording, but the right selection among these leads is crucial for enhancing diagnostic efficacy. This study proposes an automated lead selection technique using the CatBoost machine learning (ML) model to improve the detection of P-wave changes among optimal bipolar leads under different heart rates. ECGs were obtained from healthy participants with a mean age of 25 ± 3.81 years (34% women), including 114 in sinus rhythm (SR) and 38 in sinus tachycardia (ST). The recordings were made using a newly designed atrial lead system (ALS), standard limb lead (SLL), modified limb lead (MLL), modified Lewis lead (LLM) and P-lead. P-wave features and Atrioventricular (AV) ratio were extracted for statistical analysis and ML classification. The optimum ML model was chosen to identify the best-performing optimal lead, which was selected based on the SLL metrics among different ML classifiers. CatBoost was found to outperform the other ML models in SLL-II with the highest accuracy and sensitivity of 0.82 and 0.90, respectively. The CatBoost model, amid other optimal leads, gave the best results for AL-I and AL-II (0.86 and 0.83 in accuracy and 0.91 and 0.93 in sensitivity). The developed CatBoost model selected AL-I and AL-II as the top two best-performing optimal leads for the enhanced acquisition of P-wave changes, which may be useful for diagnosing atrial arrhythmias.

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.

Signs of left atrial disease and 10-year risk of atrial fibrillation

Background

The contribution of left atrial disease and excessive supraventricular ectopic activity (ESVEA) to the risk for incident atrial fibrillation (AF) is incompletely understood.

Objective

To analyse the ten-year risk to develop AF in patients with cardiovascular risk factors and to define the impact of parameters of left atrial disease and ESVEA on AF risk.

Methods

148 patients from the Diast-CHF trial with at least one cardiovascular risk factor and free of AF at baseline were followed for 10 years. Left atrial disease was defined as left atrial volume index (LAVI) >35 ml/m², P-terminal force in lead V₁ (PTFV₁) >4000 ms*μV or elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP) >250 pg/ml. We analyzed the association of these parameters and ESVEA (either >720 premature atrial contractions (PAC) or one atrial run >20 beats per day) on AF-free survival.

Results

After ten years, AF was newly detected in twelve patients (13.4%) with signs of left atrial disease and two patients (3.4%) without signs of left atrial disease (p = 0.04). LAVI (p = 0.005), ESVEA (p = 0.016) and NT-proBNP (p = 0.010) were significantly associated with AF-free survival in univariate analysis. A combined Cox model of left atrial disease parameters showed associations for NT-proBNP (HR 3.56; 95%CI 1.33–5.31; p = 0.04) and PAC (HR 2.66; 95%CI 1.25–10.15; p = 0.01) but not for LAVI or PTFV₁ with AF-free survival.

Conclusion

The risk for AF is higher in patients with cardiovascular risk factors and signs of left atrial disease. NT-proBNP and premature atrial contractions independently predict AF-free survival. The role of excessive supraventricular ectopic activity for the assessment of AF risk may be underestimated and requires further study.

P Wave Parameters and Indices: A Critical Appraisal of Clinical Utility, Challenges, and Future Research-A Consensus Document Endorsed by the International Society of Electrocardiology and the International Society for Holter and Noninvasive Electrocardiology

Atrial cardiomyopathy, characterized by abnormalities in atrial structure and function, is associated with increased risk of adverse cardiovascular and neurocognitive outcomes, independent of atrial fibrillation. There exists a critical unmet need for a clinical tool that is cost-effective, easy to use, and that can diagnose atrial cardiomyopathy. P wave parameters (PWPs) reflect underlying atrial structure, size, and electrical activation; alterations in these factors manifest as abnormalities in PWPs that can be readily ascertained from a standard 12-lead ECG and potentially be used to aid clinical decision-making. PWPs include P wave duration, interatrial block, P wave terminal force in V₁, P wave axis, P wave voltage, P wave area, and P wave dispersion. PWPs can be combined to yield an index (P wave index), such as the morphology-voltage-P-wave duration ECG risk score. Abnormal PWPs have been shown in population-based cohort studies to be independently associated with higher risks of atrial fibrillation, ischemic stroke, sudden cardiac death, and dementia. Additionally, PWPs, either individually or in combination (as a P wave index), have been reported to enhance prediction of atrial fibrillation or ischemic stroke. To facilitate translation of PWPs to routine clinical practice, additional work is needed to standardize measurement of PWPs (eg, via semiautomated or automated measurement), confirm their reliability and predictive value, leverage novel approaches (eg, wavelet analysis of P waves and machine learning algorithms), and finally, define the risk-benefit ratio of specific interventions in high-risk individuals. Our ultimate goal is to repurpose the ubiquitous 12-lead ECG to advance the study, diagnosis, and treatment of atrial cardiomyopathy, thus overcoming critical challenges in prevention of cardiovascular disease and dementia.

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.

Stability analysis on the effects of heart rate variability and premature activation of atrial ECG dynamics using ARMAX model

The cellular action potential of cardiac muscles generates Electrocardiogram (ECG) signals. Disturbances in cardiac cells are determined by analyzing the stability of ECG intervals. The PTa Interval (PTaI) of ECG represents the atrial Action Potential Duration (APD) and the evaluation of the causes of PTaI instability can predict the onset of arrhythmia. This study developed an Autoregressive Moving Average with Exogenous Input (ARMAX) model to explore the roles of Heart Rate Variability (HRV) and Premature Activation (PA) in PTaI dynamics using PTaI and PP Interval (PPI) as exogenous inputs. Minute ECG signals were collected from twenty Normal Sinus Rhythm (NSR) and ten Atrial Tachycardia (AT) volunteers. The EDAN PC ECG system was used in the Modified Limb Lead (MLL) configuration to evaluate instability. The instabilities of PTaI were found at the minimum model orders (A_min) of 10 and 11, in the NSR and AT groups, respectively. In the NSR group, the predominant reason for PTaI instability was HRV, whereas among AT patients, it was largely due to PA that preceded the onset of AT. The proposed model showed better prediction of PTaI with minimum Mean Square Error (MSE) between the measured and predicted PTa Intervals (PTaIs). The factor that led to PTaI instability in AT patients was found to be different from that of the NSR group. The frequency of PA (f_PA) was found to contribute more in the AT than the NSR group. The developed ARMAX model was better in predicting instability of atrial ECG dynamics in both groups than other autoregressive models currently in use.

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.