Diagnostic Indicators of ECG for Coronary Slow Flow Phenomenon; a Systematic Review and Meta-Analysis.

Introduction: Currently, epicardial coronary angiography is still the only diagnostic tool for Coronary Slow Flow Phenomenon (CSFP). This study aimed to systematically review studies that compared Electrocardiogram (ECG) findings between patients with and without CSFP. Methods: Using relevant key terms, we systematically searched MEDLINE, Scopus, Embase, and Web of Science to find relevant studies up to February 5th, 2023. Effect sizes in each study were calculated as mean differences and crude odds ratio; then, random-effect models using inverse variance and Mantel-Haenszel methods were used to pool standardized mean differences (SMD) and crude odds ratios, respectively. Results: Thirty-two eligible articles with a total sample size of 3,937 patients (2,069 with CSFP) were included. CSFP patients had higher P-wave maximum (Pmax) (SMD: 1.02 (95% confidence interval (CI): 0.29 - 1.76); p=0.006) and P-dispersion (Pd) (SMD: 1.63 (95% CI: 0.99 - 2.27); p<0.001) compared to the control group. CSFP group also showed significantly longer QT wave maximum duration (SMD: 0.69 (95% CI: 0.33 - 1.06); p<0.001), uncorrected QTd (SMD: 1.89(95% CI: 0.67 - 3.11); p=0.002), and corrected dispersion (QTcd) (SMD: 1.63 (95% CI: 1.09 - 2.17), p<0.001). The frontal QRS-T angle was significantly higher in the CSFP group in comparison with the control group (SMD: 1.18 (95% CI: 0.31 - 2.04; p=0.007). While CSFP patients had a significantly higher T-peak to T-end (Tp-e) (SMD:1.71 (95% CI: 0.91, 2.52), p<0.001), no significant difference was noted between groups in terms of Tp-e to QT (p=0.16) and corrected QT ratios (p=0.07). Conclusion: Our findings suggest several ECG parameters, such as P max, Pd, QT, QTc, QTd, QTcd, Tp-e, and frontal QRS-T angle, may be prolonged in CSFP patients, and they could be employed as diagnostic indicators of CSFP before angiography.

Electrocardiographic manifestations in female team handball players: analyzing ECG changes in athletes.

Introduction: Long-term intense training leads to structural, functional, and electrical remodeling of the heart. How different sports affect the heart has not been fully investigated, particularly for female athletes. The aim of the present study was to investigate the morphology of 12-lead resting electrocardiogram (ECG) in elite female handball players compared to non-athlete female subjects. Potential changes will be explored to see if they could be explained by differences in cardiac dimensions and exercise hours. Materials and methods: A cross-sectional study of 33 elite female team handball players compared to 33 sex and age-matched, non-athletic controls (age range 18-26 years) was performed. All participants underwent a resting 12-lead ECG and an echocardiographic examination. ECG variables for left ventricular hypertrophy and durations were evaluated and adjusted for cardiac dimensions and exercise hours using ANCOVA analysis. A linear regression analysis was used to describe relation between echocardiographic and ECG measures and exercise hours. Results: The female handball players had larger cardiac dimensions and significantly lower heart rate and QTc duration (Bazett's formula) as well as increased QRS and QT durations compared to controls. The 12-lead sum of voltage and the 12-lead sum of voltage ∗ QRS were significantly higher among handball players. Changes in ECG variables reflecting the left ventricle could in part be explained by left ventricular size and exercise hours. Correlation with exercise hours were moderately strong in most of the echocardiographic measures reflecting left ventricular (LV), left ventricular mass (LVM), left atrium (LA) and right atrium (RA) size. Poor to fair correlations were seen in the majority of ECG measures. Conclusions: Female team handball players had altered ECGs, longer QRS and QT durations, higher 12-lead sum of voltage and 12-lead sum of voltage ∗ QRS as well as shorter QTc (Bazett's formula) duration compared to non-athletic controls. These findings could only partly be explained by differences in left ventricular size. Despite larger atrial size in the athletes, no differences in P-wave amplitude and duration were found on ECG. This suggest that both structural, and to some degree electrical remodeling, occur in the female team handball players' heart and highlight that a normal ECG does not rule out structural adaptations. The present study adds knowledge to the field of sports cardiology regarding how the heart in female team handball players adapts to this type of sport.

Propranolol can correct prolonged QT intervals in patients with cirrhosis.

Background: Prolonged QT intervals are extremely common in patients with cirrhosis and affect their treatment outcomes. Propranolol is often used to prevent gastroesophageal variceal hemorrhage in patients with cirrhosis; however, it is uncertain whether propranolol exerts a corrective effect on QT interval prolongation in patients with cirrhosis. Aim: The study aimed to investigate the therapeutic effects of propranolol on patients with cirrhosis and prolonged QT intervals. Methods: A retrospective cohort study approach was adopted. Patients with cirrhosis complicated by moderate-to-severe gastroesophageal varices, who were hospitalized at the Affiliated Hospital of Guangdong Medical University between 1 December 2020 and 31 November 2022, were included in the study. The patients were divided into the propranolol and control groups based on whether they had received propranolol. Upon admission, the patients underwent tests on liver and kidney functions, electrolytes, and coagulation function, as well as abdominal ultrasonography and electrocardiography. In addition to conventional treatment, the patients were followed up after the use or non-use of propranolol for treatment and subsequently underwent reexamination of the aforementioned tests. Results: The propranolol group (26 patients) had an average baseline corrected QT (QTc) interval of 450.23 ± 37.18 ms, of which 14 patients (53.8%) exhibited QTc interval prolongation. Follow-up was continued for a median duration of 7.00 days after the administration of propranolol and conventional treatment. Electrocardiographic reexamination revealed a decrease in the QTc interval to 431.04 ± 34.64 ms (p = 0.014), and the number of patients with QTc interval prolongation decreased to five (19.2%; p < 0.001). After treatment with propranolol and multimodal therapy, QTc interval normalization occurred in nine patients with QTc interval prolongation, leading to a normalization rate of 64.3% (9/14). The control group (n = 58) had an average baseline QTc interval of 453.74 ± 30.03 ms, of which 33 patients (56.9%) exhibited QTc interval prolongation. After follow-up for a median duration of 7.50 days, the QTc interval was 451.79 ± 34.56 ms (p = 0.482), and the number of patients with QTc interval prolongation decreased to 30 (51.7%; p = 0.457). The QTc interval normalization rate of patients in the control group with QTc interval prolongation was merely 10.0% (3/33), which was significantly lower than that in the propranolol group (p < 0.001). Conclusion: In patients with cirrhosis complicated by QT interval prolongation, the short-term use of propranolol aids in correction of a long QT interval and provides positive therapeutic value for cirrhotic cardiomyopathy.

Can eHealth programs for cardiac arrhythmias be scaled-up by using the KardiaMobile algorithm?

Background: Remote monitoring devices for atrial fibrillation are known to positively contribute to the diagnostic process and therapy compliance. However, automatic algorithms within devices show varying sensitivity and specificity, so manual double-checking of electrocardiographic (ECG) recordings remains necessary. Objective: The purpose of this study was to investigate the validity of the KardiaMobile algorithm within the Dutch telemonitoring program (HartWacht). Methods: This retrospective study determined the diagnostic accuracy of the algorithm using assessments by a telemonitoring team as reference. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 scores were determined. Results: A total of 2298 patients (59.5% female; median age 57 ± 15 years) recorded 86,816 ECGs between April 2019 and January 2021. The algorithm showed sensitivity of 0.956, specificity 0.985, PPV 0.996, NPV 0.847, and F1 score 0.976 for the detection of sinus rhythm. A total of 29 false-positive outcomes remained uncorrected within the same patients. The algorithm showed sensitivity of 0.989, specificity 0.953, PPV 0.835, NPV 0.997, and F1 score 0.906 for detection of atrial fibrillation. A total of 2 false-negative outcomes remained uncorrected. Conclusion: Our research showed high validity of the algorithm for the detection of both sinus rhythm and, to a lesser extent, atrial fibrillation. This finding suggests that the algorithm could function as a standalone instrument particularly for detection of sinus rhythm.

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones.

BACKGROUND: Researchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG machines do not permit installation of new algorithms. OBJECTIVE: To develop a smartphone application that automatically extract ECG waveforms from photos and to convert them to voltage-time series for downstream analysis by a variety of diagnostic algorithms built by researchers. METHODS: A novel approach of using objective detection and image segmentation models to automatically extract ECG waveforms from photos taken by clinicians was devised. Modular machine learning models were developed to sequentially perform waveform identification, gridline removal, and scale calibration. The extracted data were then analysed using a machine learning-based cardiac rhythm classifier. RESULTS: Waveforms from 40 516 scanned and 444 photographed ECGs were automatically extracted. 12 828 of 13 258 (96.8%) scanned and 5399 of 5743 (94.0%) photographed waveforms were correctly cropped and labelled. 11 604 of 12 735 (91.1%) scanned and 5062 of 5752 (88.0%) photographed waveforms achieved successful voltage-time signal extraction after automatic gridline and background noise removal. In a proof-of-concept demonstration, an atrial fibrillation diagnostic algorithm achieved 91.3% sensitivity, 94.2% specificity, 95.6% positive predictive value, 88.6% negative predictive value and 93.4% F1 score, using photos of ECGs as input. CONCLUSION: Object detection and image segmentation models allow automatic extraction of ECG signals from photos for downstream diagnostics. This novel pipeline circumvents the need for costly ECG hardware upgrades, thereby paving the way for large-scale implementation of machine learning-based diagnostic algorithms.

Artificial Intelligence Electrocardiography to Predict Atrial Fibrillation in Patients With Chronic Lymphocytic Leukemia.

Background: The use of an artificial intelligence electrocardiography (AI-ECG) algorithm has demonstrated its reliability in predicting the risk of atrial fibrillation (AF) within the general population. Objectives: This study aimed to determine the effectiveness of the AI-ECG score in identifying patients with chronic lymphocytic leukemia (CLL) who are at high risk of developing AF. Methods: We estimated the probability of AF based on AI-ECG among patients with CLL extracted from the Mayo Clinic CLL database. Additionally, we computed the Mayo Clinic CLL AF risk score and determined its ability to predict AF. Results: Among 754 newly diagnosed patients with CLL, 71.4% were male (median age = 69 years). The median baseline AI-ECG score was 0.02 (range = 0-0.93), with a value ≥0.1 indicating high risk. Over a median follow-up of 5.8 years, the estimated 10-year cumulative risk of AF was 26.1%. Patients with an AI-ECG score of ≥0.1 had a significantly higher risk of AF (HR: 3.9; 95% CI: 2.6-5.7; P < 0.001). This heightened risk remained significant (HR: 2.5; 95% CI: 1.6-3.9; P < 0.001) even after adjusting for the Mayo CLL AF risk score, heart failure, chronic kidney disease, and CLL therapy. In a second cohort of CLL patients treated with a Bruton tyrosine kinase inhibitor (n = 220), a pretreatment AI-ECG score ≥0.1 showed a nonsignificant increase in the risk of AF (HR: 1.7; 95% CI: 0.8-3.6; P = 0.19). Conclusions: An AI-ECG algorithm, in conjunction with the Mayo CLL AF risk score, can predict the risk of AF in patients with newly diagnosed CLL. Additional studies are needed to determine the role of AI-ECG in predicting AF risk in CLL patients treated with a Bruton tyrosine kinase inhibitor.

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings.

Objective.Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.Approach.In this work, we proposePower-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkPower-MFagainst three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).Main results.Our results show thatPower-MFoutperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.Significance.Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

ECG-surv: A deep learning-based model to predict time to 1-year mortality from 12-lead electrocardiogram.

BACKGROUND: Electrocardiogram (ECG) abnormalities have demonstrated potential as prognostic indicators of patient survival. However, the traditional statistical approach is constrained by structured data input, limiting its ability to fully leverage the predictive value of ECG data in prognostic modeling. METHODS: This study aims to introduce and evaluate a deep-learning model to simultaneously handle censored data and unstructured ECG data for survival analysis. We herein introduce a novel deep neural network called ECG-surv, which includes a feature extraction neural network and a time-to-event analysis neural network. The proposed model is specifically designed to predict the time to 1-year mortality by extracting and analyzing unique features from 12-lead ECG data. ECG-surv was evaluated using both an independent test set and an external set, which were collected using different ECG devices. RESULTS: The performance of ECG-surv surpassed that of the Cox proportional model, which included demographics and ECG waveform parameters, in predicting 1-year all-cause mortality, with a significantly higher concordance index (C-index) in ECG-surv than in the Cox model using both the independent test set (0.860 [95% CI: 0.859- 0.861] vs. 0.796 [95% CI: 0.791- 0.800]) and the external test set (0.813 [95% CI: 0.807- 0.814] vs. 0.764 [95% CI: 0.755- 0.770]). ECG-surv also demonstrated exceptional predictive ability for cardiovascular death (C-index of 0.891 [95% CI: 0.890- 0.893]), outperforming the Framingham risk Cox model (C-index of 0.734 [95% CI: 0.715-0.752]). CONCLUSION: ECG-surv effectively utilized unstructured ECG data in a survival analysis. It outperformed traditional statistical approaches in predicting 1-year all-cause mortality and cardiovascular death, which makes it a valuable tool for predicting patient survival.

Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review.

PURPOSE OF REVIEW: Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population. AI can be applied to the standard 12-lead resting ECG and single-lead ECGs in external monitors, implantable devices, and direct-to-consumer smart devices. We summarize the current state of the literature on AI-ECG. RECENT FINDINGS: Rhythm classification was the first application of AI-ECG. Subsequently, AI-ECG models have been developed for screening structural heart disease including hypertrophic cardiomyopathy, cardiac amyloidosis, aortic stenosis, pulmonary hypertension, and left ventricular systolic dysfunction. Further, AI models can predict future events like development of systolic heart failure and atrial fibrillation. AI-ECG exhibits potential in acute cardiac events and non-cardiac applications, including acute pulmonary embolism, electrolyte abnormalities, monitoring drugs therapy, sleep apnea, and predicting all-cause mortality. Many AI models in the domain of cardiac monitors and smart watches have received Food and Drug Administration (FDA) clearance for rhythm classification, while others for identification of cardiac amyloidosis, pulmonary hypertension and left ventricular dysfunction have received breakthrough device designation. As AI-ECG models continue to be developed, in addition to regulatory oversight and monetization challenges, thoughtful clinical implementation to streamline workflows, avoiding information overload and overwhelming of healthcare systems with false positive results is necessary. Research to demonstrate and validate improvement in healthcare efficiency and improved patient outcomes would be required before widespread adoption of any AI-ECG model.

Cardiac screening in pediatric patients with neurofibromatosis type 1: similarities with Noonan syndrome?

Both Neurofibromatosis type 1 (NF1) and Noonan syndrome (NS) are RASopathies. Characteristic cardiac phenotypes of NS, including specific electrocardiographic changes, pulmonary valve stenosis and hypertrophic cardiomyopathy have not been completely studied in NF1. PURPOSE: The aims of this study were to assess: (1) similarities in the prevalence and types of ECG and conventional echocardiographic findings described in NS in asymptomatic patients with NF1, and (2) the presence of discrete myocardial dysfunction in NF1 patients using myocardial strain imaging. METHODS: Fifty-eight patients with NF1 (ages 0-18 years), and thirty-one age-matched healthy controls underwent cardiac assessment including blood pressure measurements, a 12-lead ECG, and detailed echocardiography. Quantification of cardiac chamber size, mass and function were measured using conventional echocardiography. Myocardial strain parameters were assessed using 2-Dimensional (2D) Speckle tracking echocardiography. RESULTS: Asymptomatic patients with NF1 had normal electrocardiograms, none with the typical ECG patterns described in NS. However, patients with NF1 showed significantly decreased calculated Z scores of the left ventricular internal diameter in diastole and systole, reduced left ventricular mass index and a higher incidence of cardiac abnormal findings, mainly of the mitral valve, in contrast to the frequently described types of cardiac abnormalities in NS. Peak and end systolic global circumferential strain were the only significantly reduced speckle tracking derived myocardial strain parameter. CONCLUSIONS: Children with NF1 demonstrated more dissimilarities than similarities in the prevalence and types of ECG and conventional echocardiographic findings described in NS. The role of the abnormal myocardial strain parameter needs to be explored.

Signal-averaged electrocardiography as a noninvasive tool for evaluating the ventricular substrate in patients with nonischemic cardiomyopathy: reassessment of an old tool.

Introduction: Signal-averaged electrocardiography (SAECG) provides diagnostic and prognostic information regarding cardiac diseases. However, its value in other nonischemic cardiomyopathies (NICMs) remains unclear. This study aimed to investigate the role of SAECG in patients with NICM. Methods and results: This retrospective study included consecutive patients with NICM who underwent SAECG, biventricular substrate mapping, and ablation for ventricular arrhythmia (VA). Patients with baseline ventricular conduction disturbances were excluded. Patients who fulfilled at least one SAECG criterion were categorized into Group 1, and the other patients were categorized into Group 2. Baseline and ventricular substrate characteristics were compared between the two groups. The study included 58 patients (39 men, mean age 50.4 ± 15.5 years), with 34 and 24 patients in Groups 1 and 2, respectively. Epicardial mapping was performed in eight (23.5%) and six patients (25.0%) in Groups 1 and 2 (p = 0.897), respectively. Patients in Group 1 had a more extensive right ventricular (RV) low-voltage zone (LVZ) and scar area than those in Group 2. Group 1 had a larger epicardial LVZ than Group 2. Epicardial late potentials were more frequent in Group 1 than in Group 2. There were more arrhythmogenic foci within the RV outflow tract in Group 1 than in Group 2. There was no significant difference in long-term VA recurrence. Conclusion: In our NICM population, a positive SAECG was associated with a larger RV endocardial scar, epicardial scar/late potentials, and a higher incidence of arrhythmogenic foci in the RV outflow tract.

Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery.

Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the glocal (aggregated local attributions over multiple samples) and global (concept based XAI) perspectives. We have established a set of sanity checks to identify saliency as the most sensible attribution method. We provide a dataset-wide analysis across entire patient subgroups, which goes beyond anecdotal evidence, to establish the first quantitative evidence for the alignment of model behavior with cardiologists' decision rules. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.

Expert-level sleep staging using an electrocardiography-only feed-forward neural network.

Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomnography (PSG). Unfortunately, PSG is an expensive and cumbersome process involving numerous electrodes, often conducted in an unfamiliar clinic and annotated by a professional. Although commercial devices like smartwatches track sleep, their performance is well below PSG. To address these disadvantages, we present a feed-forward neural network that achieves gold-standard levels of agreement using only a single lead of electrocardiography (ECG) data. Specifically, the median five-stage Cohen's kappa is 0.725 on a large, diverse dataset of 5 to 90-year-old subjects. Comparisons with a comprehensive meta-analysis of between-human inter-rater agreement confirm the non-inferior performance of our model. Finally, we developed a novel loss function to align the training objective with Cohen's kappa. Our method offers an inexpensive, automated, and convenient alternative for sleep stage classification-further enhanced by a real-time scoring option. Cardiosomnography, or a sleep study conducted with ECG only, could take expert-level sleep studies outside the confines of clinics and laboratories and into realistic settings. This advancement democratizes access to high-quality sleep studies, considerably enhancing the field of sleep medicine and neuroscience. It makes less-expensive, higher-quality studies accessible to a broader community, enabling improved sleep research and more personalized, accessible sleep-related healthcare interventions.

Reference ranges for ambulatory heart rate measurements in a middle-aged population.

BACKGROUND: Elevated heart rate (HR) predicts cardiovascular disease and mortality, but there are no established normal limits for ambulatory HR. We used data from the Swedish CArdioPulmonary Imaging Study to determine reference ranges for ambulatory HR in a middle-aged population. We also studied clinical correlates of ambulatory HR. METHODS: A 24-hour ECG was registered in 5809 atrial fibrillation-free individuals, aged 50-65 years. A healthy subset (n=3942) was used to establish reference values (excluding persons with beta-blockers, cardiovascular disease, hypertension, heart failure, anaemia, diabetes, sleep apnoea or chronic obstructive pulmonary disease).Minimum HR was defined as the lowest 1-minute HR. Reference ranges are reported as means±SDs and 2.5th-97.5th percentiles. Clinical correlates of ambulatory HR were analysed with multivariable linear regression. RESULTS: The average mean and minimum HRs were 73±9 and 48±7 beats per minute (bpm) in men and 76±8 and 51±7 bpm in women; the reference range for mean ambulatory HR was 57-90 bpm in men and 61-92 bpm in women. Average daytime and night-time HRs are also reported. Clinical correlates, including age, sex, height, body mass index, physical activity, smoking, alcohol intake, diabetes, hypertension, haemoglobin level, use of beta-blockers, estimated glomerular filtration rate, per cent of predicted forced expiratory volume in 1 s and coronary artery calcium score, explained <15% of the interindividual differences in HR. CONCLUSION: Ambulatory HR varies widely in healthy middle-aged individuals, a finding with relevance for the management of patients with a perception of tachycardia. Differences in ambulatory HR between individuals are largely independent of common clinical correlates.

Different degrees of anterior displacement of QRS complex.

Electrocardiogram of a patient affected by hypertrophic cardiomyopathy showed normal PR and QRS intervals and signs of left ventricular hypertrophy. In leads I,V5 and V6 the initial q waves were absent. A subsequent electrocardiogram revealed the appearance of prominent anterior QRS forces expressed by a change from rS to R pattern in leads V2 and V3 with a tall R wave in V2. PR and QRS intervals and QRS axis remained substantially unchanged. Other electrocardiograms showed day-to-day variations of the anterior displacement of QRS complex. The different degrees of anterior displacement appear to be an expression of an underlying left septal fascicular block, but a diagnosis cannot be made with certainty.

Ventricular injury in acute left circumflex occlusion: Exploration using precordial bipolar leads and regional vectorcardiograms.

BACKGROUND: Precordial Bipolar Leads (PBLs) provide new electrocardiographic information derived from standard 12­lead ECG recordings. OBJECTIVES: To explore the usefulness of PBLs in patients with acute circumflex coronary artery (CxCA) occlusion. METHODS: Twelve patients undergoing elective percutaneous transluminal coronary angioplasty (PTCA) were studied before and after acute CxCA occlusion and their data were processed with new methods based on PBLs. RESULTS: The findings were: 1. In right PBL V2-V1, a strong systolic current of injury moving in the left-to-right direction coexists with a strong right-to-left current of injury displayed in left standard unipolar precordial leads (V4, V5 and V6). 2. Ischemic changes lead to a significant increase (approximately 10 ms) in the QRS duration in different leads, although changes in the QRS loop rotation and folding were absent. 3. In the transverse, sagittal, and frontal planes, superimposing two PBLs and the corresponding Regional VCG facilitates the location of the J-point. 4. In the Regional VCGs of this group of patients, J-point and ST segment shifts produced an image that reminds the Greek letter omega (Ω). 5. The currents of injury flowing in opposite directions could result in electrical cancellation that minimizes ECG changes in the standard 12­lead recordings. CONCLUSIONS: Computerized processing of digital, standard 12­lead ECG recordings, provides new valuable diagnostic data in patients with acute CxCA occlusion. The loops revealed important information related to systolic currents of injury. Because these methods use routine 12­lead ECG data, the procedure is based only in software applications. CONDENSED ABSTRACT: Twelve patients undergoing PTCA were studied before and after acute CxCA occlusion and their data were processed with the new methods based on Precordial Bipolar Leads (PBLs) to explore their usefulness. The results showed strong systolic currents of injury in different and sometimes opposite directions in the right-to-left axis and ischemic alterations in the time and amplitude of the QRS waves. The superimposition of two-dimensional coordinates planes (x-y, x-z or z-y) helped to locate the J-point and to display the Regional VCG omega sign (Ω) of myocardial injury. In conclusion, computerized processing of digital ECG data provides new diagnostic information in patients with acute CxCA occlusion.

Prognostic significance of beat-to-beat variability of spatial heterogeneity of repolarization analyzed from a 5-minute resting electrocardiogram in coronary artery disease.

BACKGROUND: Data on the prognostic significance of temporal variability of spatial heterogeneity of electrocardiographic repolarization in coronary artery disease (CAD) are limited. OBJECTIVE: The purpose of this study was to evaluate the prognostic value of temporal variability of T-wave morphology analyzed from a 5-minute resting electrocardiogram in CAD. METHODS: The standard deviation (SD) of T-wave morphology dispersion (TMD-SD) and the SD of total cosine R-to-T were analyzed on a beat-to-beat basis from a 5-minute period of the standard resting 12-lead electrocardiogram obtained before the clinical stress test in 1702 patients with angiographically verified CAD and well-preserved left ventricular function. RESULTS: During an average of 8.7 ± 2.2 years of follow-up, 60 patients experienced sudden cardiac death/arrest (SCD/SCA) (3.5%), 69 patients nonsudden cardiac death (NSCD) (4.1%), and 161 patients noncardiac death (9.5%). TMD-SD was significantly higher in patients who experienced SCD/SCA than in other patients (1.72 ± 2.00 vs 1.12 ± 1.75; P = .01) and higher in patients who succumbed to NSCD than in other patients (1.57 ± 1.74 vs 1.12 ± 1.76; P = .04), but it did not differ significantly between patients who experienced noncardiac death and those without such an event (1.16 ± 1.42 vs 1.14 ± 1.79; P = .86). In the Cox multivariable hazards model, TMD-SD retained its significant association with the risk of SCD/SCA (hazard ratio 1.119; 95% confidence interval 1.015-1.233; P = .024) but not with the risk of NSCD (hazard ratio 1.089; 95% confidence interval 0.983-1.206; P = .103). CONCLUSION: TMD-SD is independently associated with the long-term risk of SCD/SCA in patients with CAD.

Electrocardiographic predictors of response to sacubitril/valsartan therapy in heart failure with reduced ejection fraction.

BACKGROUND: Sacubitril/valsartan (SV) is currently recommended as a first-line therapy in patients with heart failure and reduced ejection fraction (HFrEF) due to its significant clinical and prognostic benefit; however, not all patients respond to therapy and predictors of clinical response to SV remain under-studied. AIMS: To identify electrocardiographic (ECG) predictors of response to SV therapy in HFrEF patients. METHODS: A retrospective analysis of a hospital heart failure registry was undertaken. Consecutive HFrEF patients (New York Heart Association class II-III) on maximal-dose SV were studied. Response to SV was defined as ≥10% relative improvement in left ventricular ejection fraction (LVEF) at 3-months post-maximal-dose therapy. Pre-therapy ECGs were retrospectively analyzed for axes and standard wave and interval durations. Logistic regression was used to estimate odds ratios and 95% confidence intervals for associations between predictors and therapeutic response. Backward stepwise regression was employed to develop a parsimonious model. RESULTS: P-wave duration (PWD) 100-120 ms, PWD >120 ms, and QTc >460 ms were associated with response to SV on univariate analysis: OR 18.00 (4.45-122.90), 5.00 (1.47-20.42), and 3.10 (1.18-9.22), respectively. The preferred model that included the former two predictors in combination with pre-therapy creatinine, mineralocorticoid receptor antagonist use, and LVEF was highly selective (area under the ROC curve = 0.868). CONCLUSIONS: Prolongation of both PWD and QTc interval on baseline ECG in HFrEF patients is predictive of therapeutic response to maximal-dose SV therapy and may indicate early cardiac remodeling that is highly amenable to reversal.