Automatically assessed P-wave predicts cardiac events independently of left atrial enlargement in patients with cardiovascular risks: The Japan Morning Surge-Home Blood Pressure Study

Prognostic value of the electrocardiogram in patients with bicuspid aortic valve disease

BACKGROUND

Identifying bicuspid aortic valve (BAV) patients at risk for cardiac events remains challenging and the role of the electrocardiogram (ECG) has not yet been described. Therefore, this study aims to describe ECG parameters in BAV patients, and investigate their prognostic value.

METHODS

In this single-center prospective study patients with BAV without a prior aortic valve replacement (AVR) were included. Transthoracic echocardiogram and 12‑lead resting-ECG were obtained. Associations between ECG parameters and the composite endpoint of all-cause mortality and AVR were assessed using Cox-proportional hazard analysis.

RESULTS

120 patients with BAV were included (median age 30 years, 61% male). Median aortic jet velocity was 2.4 m/s [IQR: 1.7-3.4] and 5 patients (4%) had severe aortic regurgitation. All patients were in sinus rhythm. Any ECG abnormality was present in 57 patients (48%). Median PR-interval was 156 [IQR: 138-170] msec. A deviating QRS axis was found in 17 patients (14%) and Cornell criteria for LVH were fulfilled in 20 patients (17%). Repolarization abnormalities were present in 12 patients (10%). Median follow-up duration was 7.0 [6.3-9.8] years, during which 23 patients underwent AVR and 2 patients died. After adjusting for age, a longer PR-interval was associated with worse intervention-free survival (HR 1.02, 95% CI: 1.01-1.04).

CONCLUSION

Almost half of the patients with BAV had abnormalities on their ECG. Moreover, the PR-interval may be an interesting prognostic marker for intervention-free survival in BAV patients.

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.

ECG and CT for the detection of left atrial enlargement in hypertensive individuals-a population-based study

Left atrial enlargement (LAE) is associated with hypertension and an increased risk of cardiovascular morbidity and mortality. Guidelines for hypertension recommend LAE evaluation. We aimed to estimate the agreement of LAE as assessed by 12-lead electrocardiogram (ECG) and cardiac computed tomography (CT) in both the general population and hypertensive individuals. Cardiac CT and ECG were used to evaluate the presence of LAE in participants in the Copenhagen General Population Study. LAE, is defined as an LA volume above the 97.5% upper confidence limit by cardiac CT, as compared with multiple ECG criteria for LAE. A total of 3507 participants (47% males, age: 60 ± 10 years) were included. The prevalence of CT-defined LAE was 5.9% in the total population and 8.7% in participants with hypertension. In hypertensive individuals, LAE was identified by CT or by ECG in 31% with only a 4% overlap. ECG signs for anatomical LAE by CT had high negative predictive values between 93 and 96% but low sensitivity and positive predictive values. Specificity ranged from 27 to 93%. P-wave duration >120 ms was the best performing criterion, with a sensitivity of 48%, a specificity of 78%, and the highest area under the curve (0.66). We found a discrepancy in LAE prevalence when participants were assessed by CT and ECG, indicating that the two diagnostic modalities reflect different phenotypes of left atrial alterations. The diagnostic performance of ECG criteria for identifying anatomical LAE was poor.

Splitting the P-Wave: Improved Evaluation of Left Atrial Substrate Modification after Pulmonary Vein Isolation of Paroxysmal Atrial Fibrillation

Atrial substrate modification after pulmonary vein isolation (PVI) of paroxysmal atrial fibrillation (pAF) can be assessed non-invasively by analyzing P-wave duration in the electrocardiogram (ECG). However, whether right (RA) and left atrium (LA) contribute equally to this phenomenon remains unknown. The present study splits fundamental P-wave features to investigate the different RA and LA contributions to P-wave duration. Recordings of 29 pAF patients undergoing first-ever PVI were acquired before and after PVI. P-wave features were calculated: P-wave duration (PWD), duration of the first (PWDon-peak) and second (PWDpeak-off) P-wave halves, estimating RA and LA conduction, respectively. P-wave onset (PWon-R) or offset (PWoff-R) to R-peak interval, measuring combined atrial/atrioventricular and single atrioventricular conduction, respectively. Heart-rate fluctuation was corrected by scaling. Pre- and post-PVI results were compared with Mann–Whitney U-test. PWD was correlated with the remaining features. Only PWD (non-scaling: Δ=−9.84%, p=0.0085, scaling: Δ=−17.96%, p=0.0442) and PWDpeak-off (non-scaling: Δ=−22.03%, p=0.0250, scaling: Δ=−27.77%, p=0.0268) were decreased. Correlation of all features with PWD was significant before/after PVI (p<0.0001), showing the highest value between PWD and PWon-R (ρmax=0.855). PWD correlated more with PWDon-peak (ρ= 0.540–0.805) than PWDpeak-off (ρ= 0.419–0.710). PWD shortening after PVI of pAF stems mainly from the second half of the P-wave. Therefore, noninvasive estimation of LA conduction time is critical for the study of atrial substrate modification after PVI and should be addressed by splitting the P-wave in order to achieve improved estimations.

Splitting the P-Wave: Improved Evaluation of Left Atrial Substrate Modification after Pulmonary Vein Isolation of Paroxysmal Atrial Fibrillation

Atrial substrate modification after pulmonary vein isolation (PVI) of paroxysmal atrial fibrillation (pAF) can be assessed non-invasively by analyzing P-wave duration in the electrocardiogram (ECG). However, whether right (RA) and left atrium (LA) contribute equally to this phenomenon remains unknown. The present study splits fundamental P-wave features to investigate the different RA and LA contributions to P-wave duration. Recordings of 29 pAF patients undergoing first-ever PVI were acquired before and after PVI. P-wave features were calculated: P-wave duration (PWD), duration of the first (PWDon-peak) and second (PWDpeak-off) P-wave halves, estimating RA and LA conduction, respectively. P-wave onset (PWon-R) or offset (PWoff-R) to R-peak interval, measuring combined atrial/atrioventricular and single atrioventricular conduction, respectively. Heart-rate fluctuation was corrected by scaling. Pre- and post-PVI results were compared with Mann-Whitney U-test. PWD was correlated with the remaining features. Only PWD (non-scaling: Δ=-9.84%, p=0.0085, scaling: Δ=-17.96%, p=0.0442) and PWDpeak-off (non-scaling: Δ=-22.03%, p=0.0250, scaling: Δ=-27.77%, p=0.0268) were decreased. Correlation of all features with PWD was significant before/after PVI (p<0.0001), showing the highest value between PWD and PWon-R (ρmax=0.855). PWD correlated more with PWDon-peak (ρ= 0.540-0.805) than PWDpeak-off (ρ= 0.419-0.710). PWD shortening after PVI of pAF stems mainly from the second half of the P-wave. Therefore, noninvasive estimation of LA conduction time is critical for the study of atrial substrate modification after PVI and should be addressed by splitting the P-wave in order to achieve improved estimations.