Spatiotemporal repolarization dispersion before and after exercise in patients with long QT syndrome type 1 versus controls: probing into the arrhythmia substrate

Congenital long QT syndrome (LQTS) carries an increased risk for syncope and sudden death. QT prolongation promotes ventricular extrasystoles, which, in the presence of an arrhythmia substrate, might trigger ventricular tachycardia degenerating into fibrillation. Increased electrical heterogeneity (dispersion) is the suggested arrhythmia substrate in LQTS. In the most common subtype LQT1, physical exercise predisposes for arrhythmia and spatiotemporal dispersion was therefore studied in this context. Thirty-seven patients (57% on β-blockers) and 37 healthy controls (mean age, 31 vs. 35; range, 6-68 vs. 6-72 yr) performed an exercise test. Frank vectorcardiography was used to assess spatiotemporal dispersion as Tampl, Tarea, the ventricular gradient (VG), and the Tpeak-end interval from 10-s signal averages before and 7 ± 2 min after exercise; during exercise too much signal disturbance excluded analysis. Baseline and maximum heart rates as well as estimated exercise intensity were similar, but heart rate recovery was slower in patients. At baseline, QT and heart rate-corrected QT (QTcB) were significantly longer in patients (as expected), whereas dispersion parameters were numerically larger in controls. After exercise, QTpeakcB and Tpeak-endcB increased significantly more in patients (18 ± 23 vs. 7 ± 10 ms and 12 ± 17 vs. 2 ± 6 ms; P < 0.001 and P < 0.01). There was, however, no difference in the change in Tampl, Tarea, and VG between groups. In conclusion, although temporal dispersion of repolarization increased significantly more after exercise in patients with LQT1, there were no signs of exercise-induced increase in global dispersion of action potential duration and morphology. The arrhythmia substrate/mechanism in LQT1 warrants further study.NEW & NOTEWORTHY Physical activity increases the risk for life-threatening arrhythmias in LQTS type 1 (LQT1). The arrhythmia substrate is presumably altered electrical heterogeneity (a.k.a. dispersion). Spatiotemporal dispersion parameters were therefore compared before and after exercise in patients versus healthy controls using Frank vectorcardiography, a novelty. Physical exercise prolonged the time between the earliest and latest complete repolarization in patients versus controls, but did not increase parameters reflecting global dispersion of action potential duration and morphology, another novelty.

Spatial Feature Extraction of Vectorcardiography via Minimum Volume Ellipsoid Enclosure in Classifying Left Ventricular Hypertrophy

The voltage criteria used to diagnose left ventricular hypertrophy (LVH) in the chest and limb leads are by no means absolute. In addition to QRS voltages, QRS axis and duration, and P wave characteristics, repolarization (STT) changes have been focused attention due to their representing left ventricular overload. Vectorcardiography (VCG) has been studied specifically on its repolarization abnormality. The present study aims to devise spatial feature extraction of VCG and assess it in the LVH classification task. A minimum volume ellipsoid enclosure was applied to six segments obtained from upstroke and downstroke of each P, QRS, and T loops of a single-beat VCG. For the evaluation, VCG and 12 lead ECG dataset along with LVH labels of 61 subjects were derived from public open data, PTB-XL. These classification performances were compared with the LVH diagnosis criteria in the standard 12 lead ECG. As a result, the Random Forest classifier trained by the proposed spatial VCG feature resulted in accuracy of 0.904 (95% confidence interval: 0.861-0.947) when the classbalanced dataset was evaluated, which slightly exceeded the feature of 12 lead ECG. The feature importance analysis provided the quantitative ranking of the spatial feature of VCG, which were practically similar to those of ECG in the LVH classification task. Since the VCG are spatially comparable with three-dimensional data of CT, MRI, or Echocardiography, VCG will shed light on the spatial behavior of electrical depolarization and repolarization abnormalities in cardiac diseases.

Detection of myocardial ischemia due to clinically asymptomatic coronary artery stenosis at rest using supervised artificial intelligence-enabled vectorcardiography - A five-fold cross validation of accuracy

BACKGROUND

Coronary artery disease (CAD) is a leading cause of death and disability. Conventional non-invasive diagnostic modalities for the detection of stable CAD at rest are subject to significant limitations: low sensitivity, and personal expertise. We aimed to develop a reliable and time-cost efficient screening tool for the detection of coronary ischemia using machine learning.

METHODS

We developed a supervised artificial intelligence algorithm combined with a five lead vectorcardiography (VCG) approach (i.e. Cardisiography, CSG) for the diagnosis of CAD. Using vectorcardiography, the excitation process of the heart can be described as a three-dimensional signal. A diagnosis can be received, by first, calculating specific physical parameters from the signal, and subsequently, analyzing them with a machine learning algorithm containing neuronal networks. In this multi-center analysis, the primary evaluated outcome was the accuracy of the CSG Diagnosis System, validated by a five-fold nested cross-validation in comparison to angiographic findings as the gold standard. Individuals with 1, 2, or 3- vessel disease were defined as being affected.

RESULTS

Of the 595 patients, 62·0% (n = 369) had 1, 2 or 3- vessel disease identified by coronary angiography. CSG identified a CAD at rest with a sensitivity of 90·2 ± 4·2% for female patients (male: 97·2 ± 3·1%), specificity of 74·4 ± 9·8% (male: 76·1 ± 8·5%), and overall accuracy of 82·5 ± 6·4% (male: 90·7 ± 3·3%).

CONCLUSION

These findings demonstrate that supervised artificial intelligence-enabled vectorcardiography can overcome limitations of conventional non-invasive diagnostic modalities for the detection of coronary ischemia at rest and is capable as a highly valid screening tool.

Importance of the heart vector origin point definition for an ECG analysis: The Atherosclerosis Risk in Communities (ARIC) study

AIM

Our goal was to investigate the effect of a global XYZ median beat construction and the heart vector origin point definition on predictive accuracy of ECG biomarkers of sudden cardiac death (SCD).

METHODS

Atherosclerosis Risk In Community study participants with analyzable digital ECGs were included (n = 15,768; 55% female, 73% white, mean age 54.2 ± 5.8 y). We developed an algorithm to automatically detect the heart vector origin point on a median beat. Three different approaches to construct a global XYZ beat and two methods to locate origin point were compared. Global electrical heterogeneity was measured by sum absolute QRST integral (SAI QRST), spatial QRS-T angle, and spatial ventricular gradient (SVG) magnitude, azimuth, and elevation. Adjudicated SCD served as the primary outcome.

RESULTS

There was high intra-observer (kappa 0.972) and inter-observer (kappa 0.984) agreement in a heart vector origin definition between an automated algorithm and a human. QRS was wider in a median beat that was constructed using R-peak alignment than in time-coherent beat (88.1 ± 16.7 vs. 83.7 ± 15.9 ms; P < 0.0001), and on a median beat constructed using QRS-onset as a zeroed baseline, vs. isoelectric origin point (86.7 ± 15.9 vs. 83.7 ± 15.9 ms; P < 0.0001). ROC AUC was significantly larger for QRS, QT, peak QRS-T angle, SVG elevation, and SAI QRST if measured on a time-coherent median beat, and for SAI QRST and SVG magnitude if measured on a median beat using isoelectric origin point.

CONCLUSION

Time-coherent global XYZ median beat with physiologically meaningful definition of the heart vector's origin point improved predictive accuracy of SCD biomarkers.

Myocardial Ischemia Diagnosis Using a Reduced Lead System

This research presents a novel statistical model for diagnosing acute myocardial infarction (AMI). The model is based on features extracted from a reduced lead system consisting of a subset of three leads from the standard 12-lead ECG. We selected a set of relevant parameters commonly used in the clinical practice for ECG-based AMI diagnosis, namely ST elevation and T-wave maximum. We also selectedfeatures, not used in clinical practice, that were derived from vectorcardiography and computed on the reduced three-lead system (pseudo-VCG parameters). To validate the model, we used 104 patients coming from the Physionet STAFF III database which contains 12-lead ECG recordings at baseline and in coronary artery occlusion condition during angioplasty (PTCA). Results show that pseudo-VCG features are able to diagnose AMI slightly better than ST elevation and T-wave maximum features together (area under the ROC curve (AUC) 0.87 vs AUC 0.85). When combining pseudo-VCG features together with ST elevation, and T-wave maximum, the performance improved significantly (AUC 0.95, sensitivity 89.6% and specificity 82.7%). Results indicate a potential for diagnosing AMI using the proposed reduced lead system and the selected set of features. We suggest its possible use for diagnosing AMI in long-term, ambulatory and home monitoring situations, allowing an earlier and faster diagnosis.

Refining success of cardiac resynchronization therapy using a simple score predicting the amount of reverse ventricular remodelling: results from the Markers and Response to CRT (MARC) study

Aims

Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in systolic heart failure patients with ventricular conduction delay. Variability of individual response to CRT warrants improved patient selection. The Markers and Response to CRT (MARC) study was designed to investigate markers related to response to CRT.

Methods and results

We prospectively studied the ability of 11 clinical, 11 electrocardiographic, 4 echocardiographic, and 16 blood biomarkers to predict CRT response in 240 patients. Response was measured by the reduction of indexed left ventricular end-systolic volume (LVESVi) at 6 months follow-up. Biomarkers were related to LVESVi change using log-linear regression on continuous scale. Covariates that were significant univariately were included in a multivariable model. The final model was utilized to compose a response score. Age was 67 ± 10 years, 63% were male, 46% had ischaemic aetiology, LV ejection fraction was 26 ± 8%, LVESVi was 75 ± 31 mL/m2, and QRS was 178 ± 23 ms. At 6 months LVESVi was reduced to 58 ± 31 mL/m2 (relative reduction of 22 ± 24%), 130 patients (61%) showed ≥ 15% LVESVi reduction. In univariate analysis 17 parameters were significantly associated with LVESVi change. In the final model age, QRSAREA (using vectorcardiography) and two echocardiographic markers (interventricular mechanical delay and apical rocking) remained significantly associated with the amount of reverse ventricular remodelling. This CAVIAR (CRT-Age-Vectorcardiographic QRSAREA -Interventricular Mechanical delay-Apical Rocking) response score also predicted clinical outcome assessed by heart failure hospitalizations and all-cause mortality.

Conclusions

The CAVIAR response score predicts the amount of reverse remodelling after CRT and may be used to improve patient selection. Clinical Trials: NCT01519908.

Orthogonal P-wave morphology is affected by intra-atrial pressures

BACKGROUND

It has previously been shown that the morphology of the P-wave neither depends on atrial size in healthy subjects with physiologically enlarged atria nor on the physiological anatomical variation in transverse orientation of the left atrium. The present study aimed to investigate if different pressures in the left and right atrium are associated with different P-wave morphologies.

METHODS

38 patients with isolated, increased left atrial pressure, 51 patients with isolated, increased right atrial pressure and 76 patients with biatrially increased pressure were studied. All had undergone right heart catheterization and had 12-lead electrocardiographic recordings, which were transformed into vectorcardiograms for detailed P-wave morphology analysis.

RESULTS

Normal P-wave morphology (type 1) was more common in patients with isolated increased pressure in the right atrium while abnormal P-wave morphology (type 2) was more common in the groups with increased left atrial pressure (P = 0.032). Moreover, patients with increased left atrial pressure, either isolated or in conjunction with increased right atrial pressure, had significantly more often a P-wave morphology with a positive deflection in the sagittal plane (P = 0.004).

CONCLUSION

Isolated elevated right atrial pressure was associated with normal P-wave morphology while left-sided atrial pressure elevation, either isolated or in combination with right atrial pressure elevation, was associated with abnormal P-wave morphology.

Beat-to-beat P-wave morphology as a predictor of paroxysmal atrial fibrillation

BACKGROUND AND OBJECTIVES

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. The initiation and the perpetuation of AF is linked with phenomena of atrial remodeling, referring to the modification of the electrical and structural characteristics of the atrium. P-wave morphology analysis can reveal information regarding the propagation of the electrical activity on the atrial substrate. The purpose of this study is to investigate patterns on the P-wave morphology that may occur in patients with Paroxysmal AF (PAF) and which can be the basis for distinguishing between PAF and healthy subjects.

METHODS

Vectorcardiographic signals in the three orthogonal axes (X, Y and Z), of 3-5 min duration, were analyzed during SR. In total 29 PAF patients and 34 healthy volunteers were included in the analysis. These data were divided into two distinct datasets, one for the training and one for the testing of the proposed approach. The method is based on the identification of the dominant and the secondary P-wave morphology by combining adaptive k-means clustering of morphologies and a beat-to-beat cross correlation technique. The P-waves of the dominant morphology were further analyzed using wavelet transform whereas time domain characteristics were also extracted. Following a feature selection step, a SVM classifier was trained, for the discrimination of the PAF patients from the healthy subjects, while its accuracy was tested using the independent testing dataset.

RESULTS

In the cohort study, in both groups, the majority of the P-waves matched a main and a secondary morphology, while other morphologies were also present. The percentage of P-waves which simultaneously matched the main morphology in all three leads was lower in PAF patients (90.4 ± 7.8%) than in healthy subjects (95.5 ± 3.4%, p= 0.019). Three optimal scale bands were found and wavelet parameters were extracted which presented statistically significant differences between the two groups. Classification between the two groups was based on a feature selection process which highlighted 7 features, while an SVM classifier resulted a balanced accuracy equal to 93.75%. The results show the virtue of beat-to-beat analysis for PAF prediction.

CONCLUSION

The difference in the percentage of the main P-wave-morphology and in the P-wave time-frequency characteristics suggests a higher electrical instability of the atrial substrate in patients with PAF and different conduction patterns in the atria.

Myotonic dystrophy and Brugada syndrome: A common pathophysiologic pathway?

Type 1 myotonic dystrophy (DM1) is a hereditary neuromuscular disease affecting multiple organs in human adults. Here we report a 42-year-old man diagnosed with DM1. Having a history of progressive muscular weakness and gradual loss of visual acuity, he was referred to us by his ophthalmologist for risk assessment of undergoing cataract surgery. Cardiology workup revealed type 1 Brugada ECG pattern, positive late potentials and inducible ventricular fibrillation in an electrophysiology study. Literature review revealed that those ECG changes may be observed in DM1, suggesting that DM1 and Brugada syndrome may share a common pathophysiologic pathway.

Performance of Dower's inverse transform and Frank lead system for Identification of Myocardial Infarction

This study performs a comparison between Dower's inverse transform and Frank lead system for Myocardial Infarction (MI) identification. We have selected a set of relevant features for MI detection from the vectorcardiogram and used the lasso method after that to build a model for the Dower's inverse transform and one for the Frank leads system. Then we analyzed the performance between both models on MI detection. The proposed methods have been tested using PhysioNet PTB database that contains 550 records from which 368 are MIs. Two main conclusions are coming from this study. The first one is that Dower's inverse transform performs equally well than Frank leads in identification of MI patients. The second one is that lead positions have a large influence on the accuracy of MI patient identification.