Digital Electrocardiography Research Articles

Algorithm for Detecting Reference Points on a Digital Electrocardiogram in Real Time

Relevance. The use of digital electrocardiographs and cardiac monitors with built-in algorithms for automatic processing, analysis and interpretation of electrocardiograms allows the doctor to effectively diagnose cardiac arrhythmias. It is known that in order to provide emergency care to a patient, the duration of arrhythmia diagnostics should not exceed several tens of seconds, which requires the emergence of new algorithms for detecting informative features indicating arrhythmia, operating in real time. The need to introduce new and effective technologies for diagnosing cardiovascular diseases is also reflected in public health development programmes.Research goal. Development and quality indicators analysis of the algorithm for reference points detection on a digital electrocardiogram, bearing informative signs for the procedure of arteries diagnosis.The methods used. The study is based on an analysis of existing approaches to the problem of reference points detection on digital electrocardiogram, as well as conducting a test of the proposed algorithms by mathematical modelling methods. The quality indicators of the algorithms defined in accordance with the principles of signal detection theory and diagnostic testing, at the junction of which the task of electrocardiogram reference point detection is located. The proposed algorithm was tested on materials of MIT-BIH Arrhythmia Database, which is widely used for verification and validation of real-time digital electrocardiogram signal processing algorithms.The results. The study proposes an algorithm for detecting reference points on a digital electrocardiogram that carry informative features for the arrhythmia diagnostic procedure. The proposed algorithm is based on digital signal filtering using a decision rule based on a three-step two-threshold principle of pre-processed electrocardiogram signal values comparison on a sliding window. An experiment on the materials of the open verified MIT-BIH Arrhythmia DB showed that the quality of the proposed algorithm for detecting reference points is higher than that of the algorithms used in modern digital electrocardiographs and cardiac monitors. The proposed algorithm based on digital signal filtering and the three-step two-threshold decision rule have elements of scientific novelty.The significance. The results of this work can be used in the development of digital heart rate monitors, cardiac devices and for automatic processing, analysis and real-time computer-assisted digital electrocardiogram signal interpretation.

Electrocardiographic parameters of chemically immobilized giant anteaters (Myrmecophaga tridactyla).

The giant anteater (Myrmecophaga tridactyla) is a vulnerable species that is threatened mostly due to anthropogenic pressure. The anteater is a highly specialized insectivore, challenging the species' ex situ maintenance and conservation efforts. Several dietary-associated health issues have been reported in captive anteaters, including heart conditions such as dilated cardiomyopathy. On the other hand, cardiopathy is mainly diagnosed only on necropsy, and lack of clinical reference is one of the constraints. This work describes electrocardiographic parameters in twelve zoo-kept giant anteaters (Myrmecophaga tridactyla). The giant anteaters were evaluated after chemical immobilization. Surface electrocardiography using a digital electrocardiograph was performed to acquire data on the six frontal plane leads. Four animals were placed in both left and right recumbencies to assess changes in waveforms. Nine anteaters were considered healthy and included in the statistics. The mean heart rate and electrical axis were 37.8bpm ± 3.45 and 75.6º ± 11.43, respectively. ECG parameters results were P wave duration (ms) 89.7 ± 9.2, P wave amplitude (mV) 0.14 ± 0.05, PR segment duration (ms) 148.6 ± 23, R wave amplitude (mV) 1.55 ± 0.56, QRS complex duration (ms) 88.6 ± 12.0, QT interval duration (ms) 529.6 ± 71.2, and T wave amplitude (mV) 0.76 ± 0.21. There was no difference between left or right recumbency. Heart parameters of giant anteaters are similar to other mammals and seem to be influenced both by size and metabolic rate when comparing with other species. This is the first description of ECG parameters in giant anteaters.

Abstract 4137989: Notched P-wave by digital ECG analysis was associated with new-onset atrial fibrillation onset and ischemic stroke

Background: A bimodal P wave in lead II reflects left atrial remodeling and is described as a notched P wave. A notched P wave is usually defined as a dip over the smallest unit of electrocardiography (ECG) recording paper, 40 ms, but automated analysis of ECGs has shown that even a notch of 20 ms is associated with cardiovascular events. A notched P wave is also known to predict atrial fibrillation (AF) after catheter ablation. However, the relationship between automatically assessed notched P waves and new-onset AF and ischemic stroke in patients without documented AF has not been clarified. Hypothesis: A notched P-wave by digital ECG analysis was associated with both new-onset AF and ischemic stroke. Methods: We enrolled 4,216 subjects from the Cardiovascular Prognostic Coupling Study in Japan (Coupling Registry) who had one or more cardiovascular risk factors. Twelve-lead electrocardiography was conducted, and the peak-to-peak distance in the M shape was calculated automatically using a 12-lead ECG analysis system. We compared two definitions: P-waves defined as “notched” at the the peak-to-peak (“M shape”) distance in lead II of ≥20 ms or ≥40 ms. New-onset AF was confirmed through routine medical care as well as annual ECGs. We defined the primary endpoint as new-onset AF and the secondary endpoint as ischemic stroke. Results: The mean follow-up period was 53 ± 17 months, during which 17 AF cases developed. When a notched P-wave was defined as ≥20 ms (n = 319), it was a significant predictor of both new-onset AF (hazard ratio [HR] 2.91, 95% confidence interval [CI] 1.41–6.01, p=0.004) and ischemic stroke (HR 3.02, 95% CI 1.56–5.83, P=0.001). A notched P-wave defined as ≥40 ms (n = 63) was also a predictor of new-onset AF (HR 4.57; 95% CI:1.42–14.71, p = 0.011) and ischemic stroke (HR 3.84, 95% CI 1.20–12.29, P=0.024). Conclusions: A notched P-wave by digital ECG analysis was associated with ischemic stroke as well as the new-onset AF.

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based analysis of 12-lead electrocardiograms (ECGs) can already detect arrhythmias and hypertension. We performed an observational two-center study in order to develop a machine learning algorithm to proactively detect arterial hypertension from single-lead ECGs. This could serve as proof of concept with an eye towards todays wearables that record single-lead ECGs. In a prospective observational study, we enrolled 1254 consecutive subjects (539 male, aged 60.22 ± 12.46 years), with and without essential hypertension, and no indications of CVD. A 12-lead ECG of 10 seconds duration in resting position was performed on each subject using a digital electrocardiograph and lead I was isolated for analysis using a calibrated Random Forest (RF). Our RF model classified hypertensive from normotensive subjects on a hold-out test set, with 75% accuracy, ROC/AUC 0.831 (95%CI: 0.781-0.871), sensitivity 72%, and specificity 82% (sensitivity and specificity calculated using a threshold of 0.675). Increasing age, larger values of body mass index, the area under the T wave divided by the QRS complex area, and the area under QRS segment adjusted for BMI, were the four most important features that drove the classification decisions of our model. This study demonstrates the potential to opportunistically detect an undiagnosed hypertension, using a single-lead ECG. While studies with data from wearables are required to translate our findings to actual smartwatch settings, our results could pave the way to innovative technologies for hypertension awareness.

Electrocardiographic Parameters in Iranian Children Referring to Hospitals Affiliated with Mashhad University of Medical Sciences

Abstract Background: Various studies and guidelines have been published for the interpretation of electrocardiograms (ECGs) around the world. However, there is insufficient evidence supporting the relationship between race, ethnicity, and ECG parameters in the pediatric population. Objectives: This study is a pioneering effort to create a standardized registry in Iran, aiming to determine normal electrocardiographic parameters for Iranian children. Methodology: This preliminary pilot study was a descriptive cross-sectional type. Digital electrocardiography data (with 12 leads) of 382 boys and girls aged 1–216 months (18 years) who visited 3 children’s hospitals affiliated with Mashhad University of Medical Sciences were collected over 2 years. The main ECG values examined in this study included the P, QRS, T, U waves, PR interval, QT interval, QTc interval, heart rate (HR), and PR segment. For describing quantitative data, measures of central tendency such as mean and median, and measures of dispersion including standard deviation, range, and quartiles were used. For describing qualitative variables, the percentage of frequency was used. Quantitative data descriptions were done separately by gender and age groups. Results: HR and ECG parameters by sex and age group (1 month–18 years) were measured in this study. Conclusion: Our findings differ from those of previous studies. These differences might be due to the limitations of this study, such as the sample size and the diversity of racial backgrounds. To fully comprehend these discrepancies, it is important to establish registries and conduct studies with larger sample sizes.

Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network

BackgroundThe potential of utilizing artificial intelligence with electrocardiography (ECG) for initial screening of aortic dissection (AD) is promising. However, achieving a high positive predictive rate (PPR) remains challenging. Methods and resultsThis retrospective analysis of a single-center, prospective cohort study (Shinken Database 2010–2017, N = 19,170) used digital 12-lead ECGs from initial patient visits. We assessed a convolutional neural network (CNN) model's performance for AD detection with eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs via five-fold cross-validation. The mean age was 63.5 ± 12.5 years for the AD group (n = 147) and 58.1 ± 15.7 years for the non-AD group (n = 19,023). The CNN model achieved an area under the curve (AUC) of 0.936 (standard deviation [SD]: 0.023) for AD detection with eight-lead ECGs. In the entire cohort, the PPR was 7 %, with 126 out of 147 AD cases correctly diagnosed (sensitivity 86 %). When applied to patients with D-dimer levels ≥1 μg/dL and a history of hypertension, the PPR increased to 35 %, with 113 AD cases correctly identified (sensitivity 86 %). The single V1 lead displayed the highest diagnostic performance (AUC: 0.933, SD: 0.03), with PPR improvement from 8 % to 38 % within the same population. ConclusionsOur CNN model using ECG data for AD detection achieved an over 30% PPR when applied to patients with elevated D-dimer levels and hypertension history while maintaining sensitivity. A similar level of performance was observed with a single-lead V1 ECG in the CNN model.

Electrocardiographic T Wave Abnormality and Associated Factors Among Type 2 Diabetic Patients: Multicenter Institution-Based Cross-Sectional Study

Introduction: T wave abnormalities are usually found on ECG and show abnormal ventricular repolarization. The presence and magnitude of T wave abnormality are often associated with cardiovascular disease and mortality in diabetes. However, there is no study about the electrocardiographic T wave abnormalities among type 2 DM patients in Ethiopia. Therefore, this study determined the overall prevalence and the associated factors of T wave abnormalities among people living with T2DM in Amhara National Regional State referral hospitals, Ethiopia. Methods: A multicenter institution-based cross-sectional study was conducted from 01 January to 30 March 2022. Simple random sampling and systematic sampling techniques were employed to select the referral hospitals and study participants, respectively. A digital electrocardiograph was used to measure the T wave patterns, and an interviewer-administered questionnaire to collect sociodemographic and some clinical factors. Epi-data version-4.6 and Stata-14 were used for data entry and statistical analysis, respectively. The descriptive statistics were presented with tables and graphs. A binary logistic regression model was fitted to identify associated factors of T wave abnormality. In the final model, statistical significance was decided at p<0.05, and the strength of association was indicated using an adjusted odds ratio with 95% CI. Results: Two hundred and fifty-eight participants (response rate=99.6%) were included in the analysis. The prevalence of overall T wave abnormality was 21.7% (95% CI: 19, 29.2%). Higher monthly income (>90$) (AOR=0.16 [0.06, 0.46]), hypertension 5.55 (AOR=5.55 [2.12, 14.49]), fasting blood sugar of >130mg/dl 6.38 (AOR=6.38 [1.94, 20.94]), were statistically significant factors of T wave abnormality. Conclusions: Higher income, hypertension, and higher fasting blood sugar were significantly associated with ECG abnormality. The findings of this study suggest the need to institute routine ECG screening for all T2DM patients to reduce silent myocardial ischemia and further complications.

Neural network-derived electrocardiographic features have prognostic significance and important phenotypic and genotypic associations

Abstract Background Subtle, prognostically-meaningful ECG features may not be apparent to physicians. In the course of supervised machine learning (ML) training, many thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. Purpose To investigate novel neural network (NN)-derived ECG features, that may have clinical, phenotypic and genotypic associations and prognostic significance. Methods We extracted 5120 NN-derived ECG features from an AI-ECG model trained for six simple diagnoses and applied unsupervised machine learning to identify three phenogroups. The derivation set, the Clinical Outcomes in Digital Electrocardiography (CODE) cohort (n = 1,558,421), is a database of ECGs recorded in primary care in Brazil. There were four external validation cohorts. A cohort of British civil servants (WH II, n = 5,066). A longitudinal study of volunteers in the UK (UK Biobank, n = 42,386). A longitudinal cohort of Brazilian public servants (ELSA-Brasil, n = 13,739). Lastly, a cohort of patients with chronic Chagas cardiomyopathy (SaMi-Trop, n = 1,631) . Results In the derivation cohort (CODE), the three phenogroups had significantly different mortality profiles (Figure 1). After adjusting for known covariates, phenogroup B had a 1.2-fold increase in long-term mortality compared to phenogroup A (HR 1.20, 95% CI 1.17-1.23, p < 0.0001). We externally validated our findings in four diverse cohorts. Phenogroup C was poorly represented in the volunteer cohorts and therefore was excluded from those analyses. We found phenogroup B had a significantly greater risk of mortality in all cohorts (Figure 1). We performed a phenome-wide association study (PheWAS) in the UK Biobank. We found ECG phenogroup significantly associated with cardiac and non-cardiac phenotypes, including cardiac chamber volumes and cardiac output (Figure 2A). A single-trait genome-wide association study (GWAS) was conducted. The GWAS yielded four loci (Figure 2B). SCN10A, SCN5A and CAV1 have well described roles in cardiac conduction and arrhythmia. ARHGAP24 has been previously associated with ECG parameters, however, our analysis has identified for the first time ARHGAP24 as a gene associated with a prognostically significant phenogroup. Mendelian randomisation demonstrated the higher risk ECG phenogroup was causally associated with higher odds of atrioventricular (AV) block but lower odds of atrial fibrillation and ischaemic heart disease. Conclusion NN-derived ECG features have important applications beyond the original model from which they are derived and may be transferable and applicable for risk prediction in a wide range of settings, in addition to mortality prediction. We have shown the significant potential of NN-derived ECG features, as a highly transferable and potentially universal risk marker, that may be applied to a wide range of clinical contexts.

Socioeconomic status and post-covid illness trajectory

Abstract Background The associations between deprivation and illness trajectory after hospitalisation for coronavirus disease-19 (COVID-19) are uncertain. Methods A prospective, multicentre, longitudinal, cohort study involving post-COVID-19 patients enrolled in-hospital or early post-discharge (visit 1) and re-evaluated at 28-60 days post-discharge (visit 2) and in the longer term. Serial research blood tests (biomarkers), digital electrocardiography, and patient-reported outcome measures were obtained at both visits. Novel, multisystem imaging, including chest computed tomography (CT) with pulmonary and coronary angiography, cardiovascular and renal magnetic resonance imaging, was acquired at visit 2. The analysis of primary and secondary outcomes by socioeconomic status was prespecified. The Scottish Index of Multiple Deprivation (SIMD), a small-area measure of social deprivation based on domiciled post code reflecting seven factors including income, employment, education, health, access to services, crime, and housing, and categorized into general population quintiles, was used to measure social deprivation. The EQ-5D-5L, Brief Illness Perception Questionnaire (BIPQ), Patient Health Questionnaire-4 (PHQ-4) for Anxiety and Depression, and the Duke Activity Status Index (DASI) were used to assess health status. Follow-up of clinical outcomes was undertaken using electronic health records. Results Two hundred and fifty-two patients (mean±SD age 55.0 ±12.0 years, body mass index (BMI) 31.3 ±6.9 kg/m2,101 (40%) female, and 59 (23%) with diabetes) were enrolled and had follow-up completed at a median 428 (IQR 385, 538) days. Deprivation status was positively associated with BMI (p=0.0444) and diabetes (p=0.0239). One hundred and eighty-six (74%) patients reattended for investigation 28-60 days post-discharge. Lung ground glass opacity and/or consolidation (p=0.0085), obstructive coronary artery disease (p=0.0128) and renal inflammation (p=0.0421) were associated with deprivation. At 28-60 days post-discharge (n=184), health-related quality of life (EQ-5D-5L utility score, p=0.0084), illness perception, (BIPQ, p=0.0004), anxiety and depression (PHQ-4 score, p=0.0038), physical activity (DASI, p=0.002) and estimated peak oxygen consumption (ml/min/kg) (p=0.002) were worse with greater deprivation. At 3 months (n=252), deprivation status was associated with more referrals to secondary care because of ongoing COVID-19 symptoms (p=0.0438). Clinical outcomes did not associate with deprivation. Conclusion In a post-hospital COVID-19 population, socioeconomic deprivation was associated with impaired health status and secondary care episodes. Deprivation influences illness trajectory after COVID-19.

Abstract P3144: A Deep Neural Network Algorithm For Accurate Detection Of Brugada Syndrome Features In Electrocardiograms

Brugada Syndrome (BrS) is a rare but potentially fatal cardiac condition that is difficult to diagnose due to the elusive nature of its characteristic ECG pattern. The diagnosis of BrS requires identification of a specific electrocardiographic (ECG) pattern, which often appears only upon administration of sodium-channel blockers. Here, we present a deep neural network (DNN) algorithm that accurately detects BrS features in ECGs without the need for a drug challenge. We trained and validated the DNN on two independent cohorts of patients who were enrolled in electrophysiological studies for BrS diagnosis. The internal prospective validation cohort included 370 subjects, and the multicenter external validation cohort included 110 subjects from three other medical institutions in Italy. All subjects who did not present a spontaneous type 1 ECG underwent an ajmaline drug challenge to aid in confirming a BrS diagnosis. We found that the DNN approach classified ECGs for BrS with a sensitivity of 79.6%, specificity of 93.6%, accuracy of 88.4%, and area under the curve (AUC) of 0.934±0.027 in the validation cohorts. The DNN model also detected BrS with 100% accuracy in all cases in which a patient presented the spontaneous type 1 pattern. Our results demonstrate that a multivariate machine-learning algorithm can accurately detect the features of BrS in a conventional ECG without the challenge of a sodium-channel blocker. The computer-assisted analysis of digital ECG traces from current electrocardiographs might help physicians recognize patients affected by this disease. In conclusion, we have developed an advanced deep learning algorithm that can accurately detect BrS features in ECGs. Our results demonstrate that the DNN approach could be an effective tool to improve the diagnosis of BrS and potentially prevent sudden cardiac death in affected individuals.

Discrete electrocardiogram T amplitude detection based on cycle duration

In each cycle of the electrocardiogram wave there are P, Q, R, S and T amplitudes. Many studies have been conducted to obtain amplitude and QRS waves because they are related to ventricular depolarization, but to obtain T amplitude values related to ventricular repolarization are still rarely done, not even for the clinical standard (12 leads). This study aims to obtain the amplitude T value in each cycle and each electrocardiogram lead. Obtaining the amplitude T position on the reference lead will also find the amplitude T value on the other lead. Each cycle duration obtained from the duration RN to RN+1 is used to obtain the position of the endpoint of each cycle. The maximum value between the amplitude S position and the end point of the cycle is the amplitude T value. The results of research on 10 Physionet sinus rhythm samples and 10 Saiful Anwar Hospital Malang samples show that the duration of the cycle was successful in obtaining the amplitude T value for each lead. All samples can display a value. The amplitude in each cycle, where the values obtained in each cycle are still in normal conditions. The amplitude T value obtained is certainly accurate because there is only one positive value between the amplitude S position and the end of the cycle position. The position of the amplitude integer T found in a cycle in one lead will be the same as the position of the amplitude integer T in the cycle for the other lead. This occurs because of the simultaneous transmission of impulses that affect the atrial and ventricular muscle cells. The position of the amplitude T for each cycle can be found by filtering the maximum amplitude value between the amplitude position S and the final position of the cycle. Practically, this method can be programmed to be added to a digital electrocardiograph

Identification of patients with dilated phase of hypertrophic cardiomyopathy using a convolutional neural network applied to multiple, dual, and single lead electrocardiograms.

This study sought to develop an artificial intelligence-derived model to detect the dilated phase of hypertrophic cardiomyopathy (dHCM) on digital electrocardiography (ECG) and to evaluate the performance of the model applied to multiple-lead or single-lead ECG. This is a retrospective analysis using a single-center prospective cohort study (Shinken Database 2010-2017, n=19,170). After excluding those without a normal P wave on index ECG (n=1,831) and adding dHCM patients registered before 2009 (n=39), 17,378 digital ECGs were used. Totally 54 dHCM patients were identified of which 11 diagnosed at baseline, 4 developed during the time course, and 39 registered before 2009. The performance of the convolutional neural network (CNN) model for detecting dHCM was evaluated using eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs with the five-fold cross validation method. The area under the curve (AUC) of the CNN model to detect dHCM (n=54) with eight-lead ECG was 0.929 (standard deviation [SD]: 0.025) and the odds ratio was 38.64 (SD 9.10). Among the single-lead and double-lead ECGs, the AUC was highest with the single lead of V5 (0.953 [SD: 0.038]), with an odds ratio of 58.89 (SD:68.56). Compared with the performance of eight-lead ECG, the most similar performance was achieved with the model with a single V5 lead, suggesting that this single-lead ECG can be an alternative to eight-lead ECG for the screening of dHCM.

Benefits of patient-managed ECGs to verify the time of recurrent atrial fibrillation after electrical cardioversion in the PRE-ELECTRIC study

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): Vestre Viken Hospital Trust. Background Electrical cardioversion (ECV) is a common procedure to terminate persistent atrial fibrillation (AF). The recurrence rate after ECV is high, and recurrence is often unrecognized by the patients. Purpose To investigate the benefits of patient-managed out-of-hospital electrocardiography (ECG) to diagnose recurrence of AF after ECV. Methods PRE-ELECTRIC (predictors for recurrence of atrial fibrillation after electrical cardioversion) is a prospective, observational study. We included 200 adult patients with persistent AF referred for ECV at Bærum Hospital between 2018 and 2022. Time to recurrence of AF was verified by digital thumb ECG, recorded twice daily and whenever experiencing symptoms. The observation period was 28 days. We defined patient motivation as expected number of days with ECG recordings minus missed days with ECG recordings divided by expected number of days with ECG recordings. Shortly after recurrence of AF, we called the patients and investigated their awareness of recurrence. Results Mean age was 66.2 (± 9.3) years, and 42 (21.0%) were women. The most frequent comorbidities were hypertension (n=94, 47.0%) and heart failure (n=51, 25.5%). A total of 164 patients underwent ECV of AF, and the procedure was initially successful for 90.6%. Recurrence of AF had occurred in 50.3% of the successfully cardioverted patients after 4 weeks, and median time to recurrence was 5 days. Of all the cardioverted patients, 123 patients (75%) had 100% motivation with no missed days of measured thumb ECGs during the observation period. Nearly 97% of the patients had above 89% motivation with three or less missed days. More than 37% of the patients with recurrence of AF were not aware of the recurrence at the time of contact. Conclusions Recurrence of AF after ECV was frequent. Patients showed high motivation to self-managed out-of-hospital ECG-recordings, and this was useful for diagnosing recurrence of AF.

Ambulatory electrocardiographic monitoring in patients with Fabry disease: study design and cohort characterization

Aim. To develop study design and analyze the characteristics of a cohort of patients with cardiac manifestations of Fabry disease (FD) in order to study the possibilities of electrocardiography (ECG) telemonitoring for screening for cardiac arrhythmias (CA), requiring a change in therapeutic tactics.Material and methods. This prospective cohort study included 11 patients (8 men and 3 women) with documented FD, with cardiac complaints and ECG signs of heart involvement. For ECG telemonitoring, non-invasive digital event recorders ECG Dongle (Nordavind-Dubna, Russia) were used. Primary endpoint was registration of CA requiring a change in therapeutic tactics (implantation of a pacemaker/defibrillator-cardioverter/prescribing anticoagulants). Secondary endpoint was dynamics of monitored parameters for operational correction of the therapeutic regimen.Results. The cohort of patients is predominantly male (73/27%; mean age, 41±13,7 years). At the time of inclusion, 8 (73%) patients received enzyme replacement therapy for 4-73 months. Most patients are active Internet users, are aware of telemedicine possibilities and are interested in remote monitoring. The main complaints were related to cardiac function interruptions, palpitations, and chest discomfort. Left ventricular hypertrophy was diagnosed on ECG in 7 (64%) patients. Echocardiography revealed symmetrical left ventricular hypertrophy in 5 patients, asymmetric interventricular septal hypertrophy in 2 patients. Other echocardiographic abnormalities included valvular heart disease (mitral and tricuspid valve prolapse with mitral and tricuspid regurgitation, presence of supplemental chords) and aortic root dilatation. Initially, the ECG was dominated by sinus rhythm (n=10); 1 patient had documented ventricular tachycardia, 4 patients had single and coupled ventricular premature contractions; supraventricular tachycardia and premature contractions in 2 patients. Four patients had a PR interval shortening without signs of accessory pathways, 1 patient — Wolff-Parkinson-White syndrome, 1 patient — prolonged QTc interval.Conclusion. For effective management of FD, additional multicenter studies are required to develop algorithms for making tactical decisions in the process of cardiac monitoring, taking into account the characteristics of the cardiovascular manifestations of this disease. The article proposes a technology for telemedicine cardiac monitoring using ECG event recorders for outpatient CA screening.

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases.

The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events. We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 patients and two transnational data sets with 233,647 and 1631 ECGs. We distinguished the cause of mortality and additionally investigated CVD-related outcomes, including new-onset acute myocardial infarction (AMI), stroke (STK), and heart failure (HF). The DLM achieved C-indices of 0.858/0.836 in internal/external validation sets by using ECG over a 10-year period. The high-mortality-risk group identified by the proposed DLM presented a hazard ratio (HR) of 14.16 (95% confidence interval (CI): 11.33-17.70) compared to the low-risk group in the internal validation and presented a higher risk of cardiovascular (CV) mortality (HR: 18.50, 95% CI: 9.82-34.84), non-CV mortality (HR: 13.68, 95% CI: 10.76-17.38), AMI (HR: 4.01, 95% CI: 2.24-7.17), STK (HR: 2.15, 95% CI: 1.70-2.72), and HF (HR: 6.66, 95% CI: 4.54-9.77), which was consistent in an independent community hospital. The transnational validation also revealed HRs of 4.91 (95% CI: 2.63-9.16) and 2.29 (95% CI: 2.15-2.44) for all-cause mortality in the SaMi-Trop and Clinical Outcomes in Digital Electrocardiography 15% (CODE15) cohorts. The mortality risk by AI-enabled ECG may be applied in passive electronic-health-record-based CVD risk screening, which may identify more asymptomatic and unaware high-risk patients.

Comparative electrocardiographic study of the Asian freshwater box turtle Cuora flavomarginata and the Asian yellow pond turtle Mauremys mutica using non-invasive methods.

Evaluation of cardiac function is an integral part of clinical examination of chelonians. However, information about electrocardiography (ECG) in turtles and tortoises is limited and fragmentary. Its application is limited due to the lack of ECG reference values. This study aimed to compare specific ECG parameters using non-invasive methods in the Asian box turtle Cuora flavomarginata (CF) and the Asian yellow pond turtle Mauremys mutica (MM). We included 116 clinically healthy and conscious turtles. Two non-invasive methods, using adhesive patches or crocodile clips, for ECG were applied where possible. The ambient temperature was within the preferred optimum temperature zone of both species. We used specific digital ECG monitoring equipment to record the ECG data and analysed the data using specific software. The MM group showed better ECG quality and lower heart rate than the CF group. Comparing both methods, the adhesive patches method yielded higher ECG quality in the CF group, while the crocodile clips method yielded higher ECG quality in the MM group. The study population was selected as presumed healthy turtles; the presence of systemic or cardiac disease could not be excluded completely due to limited investigation. Both ECG methods were clinically potentially useful for obtaining ECG parameters; the ECG quality was influenced by the method used.

Abstract 13420: Association of Digital ECG Biomarkers With Heart Failure Status

Introduction: Electrocardiography (ECG) may be useful as an adjunct method for evaluating heart failure (HF) severity, but few studies have systematically examined the electrocardiographic parameters associated with heart failure volume status. This may include effects of hypervolemia on thoracic impedance, autonomic function, and subendocardial ischemia. We hypothesized that ECG parameters related to these mechanisms co-vary over time with brain natriuretic peptide, a measure of HF severity. Methods: A retrospective cohort was obtained from the Emory Clinical Data Warehouse for patients who had >2 ECGs performed >3 days apart and repeated BNP measures within 24 hours of each ECG. We quantified the change in HF status by calculating the differences in BNP value between each ECG acquisition time point. A total of 37 metrics were extracted from digitized ECGs as primary predictors and evaluated using multiple linear regression. Results: We evaluated 10,376 pairs of ECGs from 7,171 unique patients. The mean age was 66.9 years, 48% were women, and 51.4% were black . The median absolute change in BNP over time was 178 pg/mL (IQR 52-532), and the median sampling interval was 106 days (IQR 27-357). We evaluated interval, duration, and axis metrics from the P, QRS, and T vectors from the raw digital ECG and found 8 co-varied with BNP changes, including heart rate, P wave axis, QRS amplitude, QRS duration, QRS axis, ST segment, T wave amplitude, and frontal T wave axis (see table). Conclusions: We found several ECG metrics that quantify autonomic activity, depolarization, and repolarization associated with changes in HF status as measured by BNP. These findings may help to understand the downstream effects of hypervolemia on cardiovascular risk and also understand the potential role for certain ECG metrics in helping determine volume status, especially in low-resource settings not offering BNP.

A multisystem, cardio-renal investigation of post-COVID-19 illness

The pathophysiology and trajectory of post-Coronavirus Disease 2019 (COVID-19) syndrome is uncertain. To clarify multisystem involvement, we undertook a prospective cohort study including patients who had been hospitalized with COVID-19 (ClinicalTrials.gov ID NCT04403607). Serial blood biomarkers, digital electrocardiography and patient-reported outcome measures were obtained in-hospital and at 28–60 days post-discharge when multisystem imaging using chest computed tomography with pulmonary and coronary angiography and cardio-renal magnetic resonance imaging was also obtained. Longer-term clinical outcomes were assessed using electronic health records. Compared to controls (n = 29), at 28–60 days post-discharge, people with COVID-19 (n = 159; mean age, 55 years; 43% female) had persisting evidence of cardio-renal involvement and hemostasis pathway activation. The adjudicated likelihood of myocarditis was ‘very likely’ in 21 (13%) patients, ‘probable’ in 65 (41%) patients, ‘unlikely’ in 56 (35%) patients and ‘not present’ in 17 (11%) patients. At 28–60 days post-discharge, COVID-19 was associated with worse health-related quality of life (EQ-5D-5L score 0.77 (0.23) versus 0.87 (0.20)), anxiety and depression (PHQ-4 total score 3.59 (3.71) versus 1.28 (2.67)) and aerobic exercise capacity reflected by predicted maximal oxygen utilization (20.0 (7.6) versus 29.5 (8.0) ml/kg/min) (all P < 0.01). During follow-up (mean, 450 days), 24 (15%) patients and two (7%) controls died or were rehospitalized, and 108 (68%) patients and seven (26%) controls received outpatient secondary care (P = 0.017). The illness trajectory of patients after hospitalization with COVID-19 includes persisting multisystem abnormalities and health impairments that could lead to substantial demand on healthcare services in the future.

Patterns and associated factors of electrocardiographic abnormality among type 2 diabetic patients in Amhara National Regional State Referral Hospitals, Ethiopia: a multicenter institution-based cross-sectional study

BackgroundCardiovascular diseases are the most causes of mortality and morbidity among diabetes mellitus (DM) patients. Electrocardiographic (ECG) changes are common in the early course of the disease. Little is known about the electrocardiographic abnormalities among type 2 DM patients in Ethiopia. This study determined the overall prevalence, its patterns, and the associated factors of ECG abnormalities among people living with T2DM in Amhara National Regional State referral hospitals, Ethiopia.MethodsA multicenter institution-based cross-sectional study was conducted from 01 April to 30 May 2021. A simple random sampling and systematic sampling techniques were employed to select the referral hospitals and study participants, respectively. A digital electrocardiograph was used to measure the ECG parameters and the other data were collected using an interviewer-administered questionnaire. Epi-data version-4.6 and Stata-14 were used for data entry and statistical analysis, respectively. The descriptive statistics were presented with tables and graphs. A binary logistic regression model was fitted to identify associated factors of ECG abnormality. In the final model, statistical significance was decided at p≤0.05, and the strength of association was indicated using an adjusted odds ratio with 95% CI.ResultsTwo-hundred and fifty-eight participants (response rate = 99.6%) were included for the analysis. The prevalence of overall ECG abnormality was 45% (95% CI: 39, 51%). On the basis of the electrocardiographic patterns, 57 (21.1%; 95% CI: 14.6, 32.6%) were presented with T-wave abnormality, 36 (14%; 95% CI: 10.1, 18.8%) left axis deviation, and 24 (9.3% [6.3, 13.5%]) sinus tachycardia. Higher monthly income (> 90$) (AOR = 0.51 [0.31, 0.83]), over 10 years duration of DM (AOR = 4.5[1.05, 18.94]), hypertension (AOR = 3.9 [1.6, 9.40]), fasting blood sugar of ≥ 130 mg/dl (AOR = 5.01[2.13, 12.20]), and overweight (AOR = 2.65[1.17, 5.98]) were statistically significant factors of overall ECG abnormality.ConclusionsNearly, half of the participants had at least one ECG abnormality. Higher-income, prolonged disease duration, hypertension, higher fasting blood sugar, and overweight were significantly associated with ECG abnormality. The findings of this study suggest the need to institute routine ECG screening for all T2DM patients to reduce ECG abnormalities and further complications.

Effect of implantable cardioverter defibrillator implantation on frontal QRS-T angle.

The frontal plane QRS-T [f(QRS-T)] angle refers to the mean difference between the depolarization and repolarization vectors and can easily be measured by digital electrocardiography (ECG). Pacemaker leads can cause fibrosis of the myocardium adjacent to the electrode-tissue interface. We aimed to evaluate whether a right ventricular implantable cardioverter-defibrillator (ICD) lead modifies the f(QRS-T) angle in patients with systolic heart failure (HF). We prospectively analyzed 101 systolic HF patients who underwent single-lead ICD implantation. The f(QRS) angle before and after ICD implantation was compared with the Wilcoxon signed-rank test. No significant difference in the f(QRS-T) angle was observed before and 6 months after ICD implantation (89.5° ± 97.8° vs. 93.0° ± 90.3°, p=0.211). Histopathological studies showed that the irritating effect of ICD leads may cause fibrosis and scar tissue. However, we found that fibrosis and scar tissue did not affect the f(QRS-T) angle, which suggests that these pathologies may not have clinical significance. Further studies on the f(QRS-T) angle are warranted to understand its clinical significance in ICD-induced local fibrosis.