Electrocardiogram-based predictors of sudden cardiac death in patients with coronary artery disease

Enhancing Comprehensive Assessments in Chronic Heart Failure Caused by Ischemic Heart Disease: The Diagnostic Utility of Holter ECG Parameters

Background and Objectives: Chronic heart failure (CHF) caused by ischemic heart disease (IHD) is the leading cause of death worldwide and presents significant health challenges. Effective management of IHD requires prevention, early detection, and treatment to improve patient outcomes. This study aims to expand the diagnostic utility of various 24 h Holter ECG parameters, such as T-wave alternans (TWA), late ventricular potentials (LVPs), and heart rate variability (HRV) in patients with CHF caused by IHD. Additionally, we seek to explore the association between these parameters and other comorbid conditions affecting the prognosis of CHF patients. Materials and Methods: We conducted a prospective case-control study with 150 patients divided into two subgroups: 100 patients with CHF caused by IHD, and 50 patients in the control group. Data included medical history, physical examination, laboratory tests, echocardiography, and 24 h Holter monitoring. Results: Our comparative analysis demonstrated that both TWA and LVPs were significantly higher in patients with CHF compared to the control group (p < 0.01), indicating increased myocardial electrical vulnerability in CHF patients. Both time and frequency-domain HRV parameters were significantly lower in the CHF group. However, the ratio of NN50 to the total count of NN intervals (PNN50) showed a borderline significance (p = 0.06). While the low-frequency (LF) domain was significantly lower in CHF patients, the high-frequency (HF) domain did not differ significantly between groups. Acceleration and deceleration capacities were also significantly altered in CHF patients. Categorizing CHF patients by left ventricular ejection fraction (LVEF) revealed that the mean of the 5-min normal-to-normal intervals over the complete recording (SDNN Index) was significantly higher in patients with LVEF ≥ 50% compared to those with CHF with reduced EF and CHF with mildly reduced EF (p < 0.001), whereas the other HRV parameters showed no significant differences among the groups. Conclusions: Holter ECG parameters can become a reliable tool in the assessment of patients with CHF. The integration of multiple Holter ECG parameters, such as TWA, LVPs, and HRV, can significantly enhance the diagnostic assessment of CHF caused by IHD. This comprehensive approach allows for a more nuanced understanding of the patient's condition and potential outcomes.

ECG Scoring for the Evaluation of Therapy-Naïve Cancer Patients to Predict Cardiotoxicity

Simple Summary

Due to improved survival upon effective anti-cancer therapies, the management of treatment-related side-effects is of increasing interest and importance. Cardiovascular side-effects of chemo-, targeted- and/or immunotherapies are common and can be harmful. To date, the identification of patients who could experience those cardiovascular side-effects prior to the anti-cancer therapy start is difficult. We show that the use of a simple electrocardiographic (ECG) score can help to predict the occurrence of cardiovascular toxicity of anti-cancer therapies.

Abstract

Objective: To evaluate a new electrocardiographic (ECG) score reflecting domains of electrical and structural alterations in therapy-naïve cancer patients to assess their risk of cardiotoxicity. Methods: We performed a retrospective analysis of 134 therapy-naïve consecutive cancer patients in our two university hospitals concerning four ECG score parameters: Contiguous Q-waves, markers of left ventricular (LV) hypertrophy, QRS duration and JTc prolongation. Cardiotoxicity was assessed after a short-term follow-up (up to 12 months). Results: Of all the patients (n = 25), 19% reached 0 points, 50% (n = 67) reached 1 point, 25% (n = 33) reached 2 points, 5% (n = 7) reached 3 points and 0.7% reached 4 or 5 points (n = 1 respectively). The incidence of cardiotoxicity (n = 28 [21%]) increased with the ECG score, with 0 points at 0%, 1 point 7.5%, 2 points 55%, 3 points 71% and ≥3 points 50%. In the ROC (Receiver operating curves) analysis, the best cut-off for predicting cardiotoxicity was an ECG score of ≥2 points (sensitivity 82%, specificity 82%, AUC 0.84, 95% CI 0.77–0.92, p < 0.0001) which was then defined as a high-risk score. High-risk patients did not differ concerning their age, LV ejection fraction, classical cardiovascular risk factors or cardiac biomarkers compared to those with a low-risk ECG score. Conclusion: ECG scoring prior to the start of anti-cancer therapies may help to identify therapy-naïve cancer patients at a higher risk for the development of cardiotoxicity.

Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network

Sudden cardiac death (SCD) is a major cause of death among patients with heart diseases. It occurs mainly due to ventricular tachyarrhythmia (VTA) which includes ventricular tachycardia (VT) and ventricular fibrillation (VF) conditions. The main challenging task is to predict the VTA condition at a faster rate and timely application of automatic external defibrillator (AED) for saving lives. In this study, a VF/VT classification scheme has been proposed using a deep neural network (DNN) approach using hybrid time-frequency-based features. Two annotated public domain ECG databases (CUDB and VFDB) were used as training, test, and validation of datasets. The main motivation of this study was to implement a deep learning model for the classification of the VF/VT conditions and compared the results with other standard machine learning algorithms. The signal is decomposed with the wavelet transform, empirical mode decomposition (EMD) and variable mode decomposition (VMD) approaches and twenty-four are extracted to form a hybrid model from a window of length 5 s length. The DNN classifier achieved an accuracy (Acc) of 99.2%, sensitivity (Se) of 98.8%, and specificity (Sp) of 99.3% which is comparatively better than the results of the standard classifier. The proposed algorithm can detect VTA conditions accurately, hence could reduce the rate of misinterpretations by human experts and improves the efficiency of cardiac diagnosis by ECG signal analysis.

Single Lead ECG-based Ventricular Repolarization Classification for Early Identification of Unexpected Ventricular Fibrillation

Malignant ventricular arrhythmia (especially ventricular fibrillation (VF)) is the main reason which causes sudden cardiac death (SCD). This paper presents an automatic SCD-patient classifier we developed to identify patients with unexpected VF using 60-minutes continuous single-lead electrocardiograms (ECG) signals before that. Patients are classified as having SCD if the majority of their recorded ventricular repolarization (VR) is recognized as characteristic of unexpected VF. Thus, the classifier's underlying task is to recognize individual VR delineated from single-lead ECG signals as SCD VR, where VR from non-SCD patients are used as controls. With the reported clinical practices of SCD, we extracted five morphological and temporal features (both commonly used and newly developed ones) from ECG signals for VR classification. To evaluate classification performance, we trained and tested k nearest neighbor classifier, a decision tree classifier, and a Naïve Bayes classifier using five-fold cross validation on 36 one-hour ECG signals (18 from patients at risk of SCD and 18 from control people). We compared the performance of these three classifiers, and the patient-classification sensitivity is approximately 98.02-99.51%. Moreover, the k nearest neighbor with a higher accuracy (98.89%) and specificity (98.27%) performed better than the other two. Importantly, the results show obvious superiorities of performance over that in the same duration and of usefulness over several minutes given by related works.Clinical Relevance- This could be integrated into a real-time, long-term out-of-hospital SCD predictor to improve the warning veracity and bring forward the warning time, especially for patients with implantable cardiac defibrillators or pacemakers, etc..

Electrocardiographic predictors of mortality in patients after percutaneous coronary interventions - a nested case-control study

Background: The outcome of patients undergoing percutaneous coronary interventions (PCIs) varies considerably. Several ECG parameters have recently emerged (PQ interval, P-wave, T-peak-to-T-end interval, T-wave, T/R ratio, J-wave) beyond traditional markers (rhythm, QRS, Q-wave, QT interval, ST segment) and were attributed important prognostic value in the setting of coronary artery disease. The present study integrated for the first time these ECG parameters altogether with the aim to determine their role in predicting patients' outcome after a PCI. Methods: A total of 3342 patients were enrolled in the present study between 2009 and 2013. In a nested case-control design, 644 patients who died within a year post-PCI (cases) were matched 1:4 with patients alive at that particular date (controls). Results: Our data showed that only the presence of a longer QT interval (heart rate-corrected using Bazett formula) was associated with increased risk of death after adjusting for multiple clinical and angiographic risk factors (adjusted OR 1.07; 95%CI 1.01-1.12, p = .022). Conclusion: Our study emphasises the prognostic importance of the QT interval in identifying patients at increased risk of death during the first year after PCI. Clinical Trial Registration - URL: https://www.clinicaltrials.gov . Unique identifier: NCT02241291.

A Deviation Analysis Framework for ECG Signals Using Controlled Spatial Transformation

Current automated heart monitoring tools use supervised learning methods to recognize heart disorders based on ECG signal morphology. We develop a new ECG processing algorithm that enables early prediction of disorders through a novel deviation analysis. The idea is developing a patient-specific ECG baseline and characterizing the deviation of signal morphology towards any of the abnormality classes with specific morphological features. To enable this feature, a novel controlled non-linear transformation is designed to achieve maximal symme- try in the feature space. Our results using benchmark MIT-BIH database show that the proposed method achieves a classification accuracy of 96% and can be used to trigger yellow alarms to warn patients from increased risk of upcoming heart abnormalities (5% to 10% increase with respect to normal conditions). This feature can be used in health monitoring devices to advise patients to take preventive and precaution actions before critical situations.

ECG Parameters for Malignant Ventricular Arrhythmias: A Comprehensive Review

Many studies showed electrocardiogram (ECG) parameters are useful for predicting fatal ventricular arrhythmias (VAs). However, the studies have several shortcomings. Firstly, all studies lack of effective way to present behavior of various ECG parameters prior to the occurrence of the VAs. Secondly, they also lack of discussion on how to consider the parameters as abnormal. Thirdly, the reports do not include approaches to increase the detection accuracy for the abnormal patterns. The purpose of this study is to address the aforementioned issues. It identifies ten ECG parameters from various sources and then presents a review based on the identified parameters. From the review, it has been found that the increased risk of VAs can be represented by presence and certain abnormal range of the parameters. The variation of parameters range could be influenced by either gender or age. This study also has discovered the facts that averaging, outliers elimination and morphology detection algorithms can contribute to the detection accuracy.

Analysis of T-wave Alternans in ambulatory recordings using the ADTWA index

Implantable cardioverter defibrillators (ICD) are the most effective way of preventing sudden cardiac death (SCD). However, the implantation of an ICD is an invasive procedure with associated risks and a high cost. Therefore, it is necessary to determine non-invasive risk markers that identify patients at a higher risk of suffering malignant arrhytmias. One of the most promising non-invasive indices is T-wave alternans (TWA). This work assesses T-wave alternans using the Amplitude of Dominant T-Wave Alternans (ADTWA), that is derived from the dominant T wave associated to a number of consecutive beats. Data from 650 patients with heart failure enrolled in the MUSIC study were analyzed. ADTWA have higher values increasing heart rate. ADTWA was also significantly higher in SCD patients than in survivors (survivors vs. SCD: 6.60±1.98 vs. 7.55±2.53, p=0.01). ADTWA seems a promising index to identify patients with heart failure at higher risk of SCD.

Electrocardiographic Predictors of Cardiovascular Mortality

Cardiovascular diseases are the main causes of mortality. Sudden cardiac death may also appear in athletes, due to underlying congenital or inherited cardiac abnormalities. The electrocardiogram is used in clinical practice and clinical trials, as a valid, reliable, accessible, inexpensive method. The aim of the present paper was to review electrocardiographic (ECG) signs associated with cardiovascular mortality and the mechanisms underlying those associations, providing a brief description of the main studies in this area, and consider their implication for clinical practice in the general population and athletes. The main ECG parameters associated with cardiovascular mortality in the present paper are the P wave (duration, interatrial block, and deep terminal negativity of the P wave in V1), prolonged QT and Tpeak-Tend intervals, QRS duration and fragmentation, bundle branch block, ST segment depression and elevation, T waves (inverted, T wave axes), spatial angles between QRS and T vectors, premature ventricular contractions, and ECG hypertrophy criteria.