Electrocardiogram Biomarkers Research Articles

Validation of a Demography-Based Adaptive QTc Formula using Pediatric and Adult Datasets Acquired from Humans and Guinea Pigs.

A variety of QT rate-correction (QTc) formulae have been utilized for both clinical and research purposes. However, these formulae are not universally effective, likely due to significant influences of demographic diversity on the QT-HR relationship. To address this limitation, we proposed an adaptive QTc (QTcAd) formula that adjusts to subject demographics (i.e., age). Further, we compared the efficacy and accuracy of the QTcAd formula to other widely used alternatives. Using age as a demographic parameter, we tested the QTcAd formula across diverse age groups with different heart rates (HR) in both humans and guinea pigs. Utilizing retrospective human (n=1360) and guinea pig electrocardiogram (ECG) data from in-vivo (n=55) and ex-vivo (n=66) settings, we evaluated the formula's effectiveness. Linear regression fit parameters of HR-QTc (slope and R²) were utilized for performance assessment. To evaluate the accuracy of the predicted QTc, we acquired epicardial electrical and optical voltage data from Langendorff-perfused guinea pig hearts. In both human subjects and guinea pigs, the QTcAd formula consistently outperformed other formulae across all age groups. For instance, in a 20-year-old human group (n=300), the QTcAd formula successfully nullified the inverse HR-QT relationship (R²=5.1E-09, slope=-3.5E-05), while the Bazett formula (QTcB) failed to achieve comparable effectiveness (R²= 0.20, slope=0.91). Moreover, the QTcAd formula exhibited better accuracy than the age-specific Benatar formula (QTcBe), which overcorrected QTc (1-week human QT: 263.8±14.8 ms, QTcAd: 263.8±7.3 ms, p=0.62; QTcBe: 422.5±7.3 ms, p<0.0001). The optically measured pseudo-QT interval (143±22.5 ms, n=44) was better approximated by QTcAd (180.6±17.0 ms) compared to all other formulae. Furthermore, we demonstrated that the QTcAd formula was not inferior to individual-specific QTc formulae. The demography-based QTcAd formula showed superior performance across human and guinea pig age groups, which may enhance the efficacy of QTc for cardiovascular disease diagnosis, risk stratification, and drug safety testing. Corrected QT (QTc) is a well-known ECG biomarker for cardiovascular disease risk stratification and drug safety testing. Various QT rate-correction formulae have been developed, but these formulae do not perform consistently across diverse datasets (e.g., sex, age, disease, species). We introduce a novel QTc formula (QTcAd) that adapts to demographic variability, as the parameters can be modified based on the characteristics of the study population. The formula (QTcAd = QT + (|m|*(HR-HR mean )) - includes the absolute slope (m) of the linear regression of QT and heart rate (HR) and the mean HR of the population (HR mean ) as population characteristics parametersˍUsing datasets from both pediatric and adult human subjects and an animal model, we demonstrate that the QTcAd formula is more effective at eliminating the QT-HR inverse relationship, as compared to other commonly used correction formulae.

Robustness of Deep Learning models in electrocardiogram noise detection and classification

Background and Objective: Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy lifestyles. However, ECG signals are susceptible to noise, which adversely affects the performance of ECG signal analysers. Traditional blind filtering methods use predefined noise frequency and filter order, but they alter ECG biomarkers. Several Deep Learning-based ECG noise detection and classification methods exist, but no study compares recurrent neural network (RNN) and convolutional neural network (CNN) architectures and their complexity. Methods:This paper introduces a knowledge-based ECG filtering system using Deep Learning to classify ECG noise types and compare popular computer vision model architectures in a practical Internet of Medical Things (IoMT) framework. Experimental results demonstrate that the CNN-based ECG noise classifier outperforms the RNN-based model in terms of performance and training time. Results:The study shows that AlexNet, visual geometry group (VGG), and residual network (ResNet) achieved over 70% accuracy, specificity, sensitivity, and F1 score across six datasets. VGG and ResNet performances were comparable, but VGG was more complex than ResNet, with only a 4.57% less F1 score. Conclusions:This paper introduces a Deep Learning (DL) based ECG noise classifier for a knowledge-driven ECG filtering system, offering selective filtering to reduce signal distortion. Evaluation of various CNN and RNN-based models reveals VGG and Resnet outperform. Further, the VGG model is superior in terms of performance. But Resnet performs comparably to VGG with less model complexity.

Electrocardiographic Associations of Cardiac Biomarkers and Cardiac Magnetic Resonance Measures of Fibrosis in the Multiethnic Study of Atherosclerosis (MESA)

Abnormalities in myocardial substrate, including diffuse and replacement fibrosis, increase the risk of cardiovascular disease (CVD).Data are sparse on whether electrocardiogram (ECG) measures, coupled with circulating biomarkers, may aid in identifying cardiac fibrosis. This study aimed to determine whether 12-lead ECG and biomarkers together augment the prediction of cardiac fibrosis in participants who are free of known CVD. This is a cross-sectional analysis in the MESA (Multiethnic Study of Atherosclerosis) study at visit 5 (2010 to 2012), with measurements of biomarkers (cardiac troponin T and growth differentiation factor-15), gadolinium-enhanced cardiac magnetic resonance imaging, and ECG.Logistic regression associations of ECG measures with cardiac magnetic resonance surrogates of fibrosis (highest quartile extracellular volume [interstitial fibrosis] and late gadolinium enhancement [replacement fibrosis]) were adjusted for demographics and risk factors.Using the C-statistic, we evaluated whether adding ECG measures and biomarkers to clinical characteristics improved the prediction of either type of fibrosis. There were 1,170 eligible participants (aged 67.1 ± 8.6years).Among the ECG measures, QRS duration (odds ratio [OR] 1.41 per 10ms, 95% confidence interval [CI] 1.10 to 1.81), major ST-T abnormalities (OR 3.03, 95%CI 1.20, 7.65),and abnormal QRS-T angle (OR 6.32, 95%CI 3.00, 13.33) were associated with replacement fibrosis, whereas only abnormal QRS-T angle (OR 3.05, 95%CI,1.69, 5.48) was associated with interstitial fibrosis.ECG markers, in addition to clinical characteristics, improved the prediction of replacement fibrosis (p=0.002) but not interstitial fibrosis. The addition of cardiac troponin T and growth differentiation factor-15 to the ECG findings did not significantly improve the model discrimination for either type of cardiac fibrosis.In CVD free participants, simple ECG measures are associated with replacement fibrosis and interstitial fibrosis. The addition of these measures improves identification of replacement but not interstitial fibrosis. These findings may help refine the identification of myocardial scar in the general population.

Classifying hypertension mediated left ventricular hypertrophy phenotypes from the 12-lead electrocardiogram using machine learning

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Pat Merriman Clinical Research Training Fellowship Background Hypertension is the commonest cause of left ventricular hypertrophy (LVH), an established independent predictor of cardiovascular morbidity and mortality. Four distinct LVH phenotypes with varying prognostic implications have been reported using cardiac magnetic resonance (CMR) LV mass to volume ratio: normal LV, LV remodelling, eccentric LVH and concentric LVH. Current electrocardiogram (ECG) criteria can detect LVH but their ability to differentiate between hypertension mediated LVH phenotypes is unknown. Purpose The aim of this study was to classify hypertension mediated LVH phenotypes using 12-lead electrocardiogram (ECG) and clinical data for cost-effective patient stratification. Methods 24,535 hypertensive participants from UK Biobank imaging study were categorised into the CMR-defined LVH phenotypes. ECG biomarkers with a physiological association with LVH (including QRS biomarkers, Sokolow-Lyon and Cornell voltage) were extracted from the 12-lead ECG of each participant using in-house algorithms. Vascular risk factors known to be associated with LVH were also ascertained from the clinical data of each participant. Classification models integrating ECG biomarkers and clinical data were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% train and 20% test sets for performance measurement. Ten-fold cross validation was applied and the training set was downsampled to the minority class. Results Among the participants (mean age ± standard deviation 65.3 ± 7.4 years), 23,312 (95.0%) had normal LV, 900 (3.7%) LV remodelling, 220 (0.8%) eccentric LVH and 103 (0.4%) concentric LVH. R wave amplitude V5, S wave amplitude V3 and QT duration V6 (P&amp;lt;0.001) were the top ECG biomarkers and systolic blood pressure (p&amp;lt;0.001) the top clinical predictor of LVH phenotypes (Figure 1). Classification with logistic regression had an accuracy of 77% (sensitivity 55%, specificity 85%, Area under the receiver operator curve [AUC] 0.76), SVM 80% accuracy (sensitivity 61%, specificity 87%, AUC 0.75) and RF 75% accuracy (sensitivity 49%, specificity 83%, AUC 0.75). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical data alone. Conclusion A combination of automatically extracted ECG biomarkers and clinical data were able to discriminate the hypertension mediated LVH phenotypes. An advanced classification approach improves selection of hypertensive patients with LVH, thereby adding prognostic value to traditional risk models. We are currently validating our findings in an external population and using deep learning for comparison.

Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning.

Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification. We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (P < 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models. A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.

PREDICTING THE PATTERN OF ANATOMICAL LEFT VENTRICULAR HYPERTROPHY USING ELECTROPHYSIOLOGICAL BIOMARKERS FROM THE ECG QRS COMPLEX IN HYPERTENSIVE INDIVIDUALS FROM UK BIOBANK

Objective: Hypertension has progressive end-organ effects such as left ventricular hypertrophy (LVH), an established independent predictor of cardiovascular morbidity and mortality. Four distinct LVH phenotypes with varying prognostic implications have been described using cardiac magnetic resonance (CMR) LV mass to volume ratio; normal LV, LV remodelling, eccentric LVH and concentric LVH. Current electrocardiogram (ECG) criteria can detect LVH but their ability to differentiate between LVH phenotypes is unclear. Design and method: As a preliminary analysis, 5,065 participants in the UK Biobank were categorised into the 4 CMR-defined LVH phenotypes. The 12 lead ECG of each participant was analysed using MATLAB to derive ECG biomarkers known to have an association with LVH (QRS duration, QRS ascending and descending slopes, Q wave, R wave, S wave and QRS wave amplitudes). ANOVA compared differences in ECG biomarkers across LVH phenotypes. Univariate logistic regression was used to test association of each ECG biomarker with the LVH phenotypes. ECG biomarkers with a significant association (P &lt; 0.05) were included in the multinomial logistic regression model. Results: In combination, the set of 7 ECG biomarkers detected a difference (P &lt; 2.2e-16) across the LVH phenotypes. Using logistic regression, QRS duration, S wave and QRS waves amplitude were able to differentiate between the 4 LVH phenotypes. With normotensive group as a reference, there was a significant association between S wave amplitude and normal LV (Odds Ratio 1.80, P 1.46e-03), QRS wave amplitude and normal LV (OR 2.23, P 9.01e-05), global QRS duration with eccentric LVH (OR 1.05, P 4.45e-06) and concentric LVH (OR 1.03, P 1.02e-05). Conclusions: We identified a set of ECG markers from the QRS complex that can differentiate between the 4 LVH phenotypes, providing support for the ECG to identify subclinical LVH identified using CMR. We are currently extending our analyses to the full 45,000 UK Biobank imaging cohort for validation.

Early Diagnosis and Management of Non ST-Segment Elevation Acute Myocardial Infarction

Acute myocardial infarction (MI) is a cardiovascular disease with high mortality. Acute MI is usually divided into ST-segment elevation MI (STEMI) and non ST-segment elevation MI (NSTEMI) based on the electrocardiogram (ECG). NSTEMI is often misdiagnosed in patients presenting to emergency departments with acute chest pain because its clinical course varies and is atypical compared to STEMI. The symptoms can be vague and the ECG is often not diagnostic. In this setting, an early accurate diagnosis and risk stratification could improve the mortality of patients with NSTEMI. Cardiac biomarkers such as high-sensitivity cardiac troponin (hs-cTn) help to diagnose NSTEMI. Serial hs-cTn assays should be considered to differentiate other conditions, especially in an ambiguous clinical situation. If acute MI is strongly suspected based on the symptoms, ECG, and cardiac biomarkers, catheterization laboratory activation should be timely considered to evaluate the coronary arteries and possible revascularization with percutaneous or surgical strategies depending on risk factors. After successful revascularization, antianginal medications, risk factor control, and early recognition of heart failure are essential to improve the cardiovascular prognosis.

Hypertension Predisposition and Thermoregulation Delays in Adolescents with Polycystic Ovary Syndrome: A Pilot Study.

Background: Polycystic ovary syndrome (PCOS) is a heterogeneous disorder in which clinical, sonographic, and endophenotypic features have been underinvestigated or inconclusive, especially in the early stages of the disease (adolescence/young adulthood). Objective: This prospective pilot study focused on the differences of multiple physiological functions between Greek adolescent/young adult females suffering from PCOS and age- and body mass index (BMI)- matched healthy controls. Study design: Nineteen PCOS patients and eighteen healthy controls (aged 13 to 23 years) were studied for: (i) biochemical and hormonal dysfunction by measuring circulating glucose, insulin, and androgen levels; (ii) arterial stiffness with pulse wave analysis (PWA) by Sphygmocord; (iii) intima-media thickness (IMT) by ultrasound; (iv) heart rate variability (HRV) by Task Force Monitor; and (v) QT, QRS, QT, P, QRSD by electrocardiogram (ECG). Statistical analysis included Hedge’s g correction for small samples bias, and the results are shown using the Hedge’s g effect size and 95% CI, in line with precision medicine prerequisites. Results: Significant differences in pulse wave velocity (PWV) (g = 0.964 [0.296, 1.632]), subendocardial viability ratio (SEVR) carotid (g = −0.679 [−1.329, −0.030]), pulse pressure (PP) carotid (g = 0.942 [0.275, 1.608]), systolic pressure (SP) carotid (g = 0.785 [0.129, 1.440]), free-testosterone (g = 0.677 [0.042, 0.312]), and Delta4-androstenedione (g = 0.735 [0.097, 0.373]) were observed between PCOS patients and controls. No differences were detected in the remaining endocrine and PWA or ECG biomarkers. Conclusions: Our multidisciplinary approach showed early onset of vascular dysfunction, predisposition to hypertension, thermoregulation delays, and metabolic syndrome changes in adolescent/young adult PCOS.

Post myocardial infarction ionic remodelling promotes repolarisation dispersion and electrocardiogram abnormalities in acute and chronic stages

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Wellcome Trust Background Sudden cardiac death (SCD) occurs in both acute and chronic stages post myocardial infarction (MI), and the left ventricular ejection fraction (LVEF) and electrocardiogram (ECG) characteristics are important biomarkers for clinical decision making. While various ionic remodelling has been reported in literature at various stages post-MI, the effect of them on clinical biomarkers have not been fully explored. Purpose Evaluate the effects of cellular ionic remodelling in acute and chronic post myocardial infarction on ECG and LVEF biomarkers through computational modelling and simulations of human biventricular electromechanics. Methods An electromechanical coupled biventricular model with ToR-ORd (Tomek 2019) human electrophysiology coupled with human excitation-contraction machinery (Land 2017) and orthotropic passive mechanics (Holzapfel 2009) was used as baseline model. The 12-lead ECG were simulated at standard body-surface electrode locations, and the biventricular pressure volume loop was simulated to quantify the simulated LVEF. A cardiac magnetic resonance (CMR) derived human biventricular geometry (Strocchi 2020) was used in combination with rigidly aligned torso geometry (Minchole 2019). An anterior sub-endocardial infarction was delineated with infarct, border zone (BZ) and remote zone (RZ) regions. For acute infarction, three types of BZ ionic remodelling from literature, and their effects on simulated LVEF and ECG characteristics were quantified. For chronic infarction, a single chronic BZ ionic remodelling was combine with two different models of RZ ionic remodelling from literature, and the effects of these remodelling on the simulated LVEF and ECG were quantified. The electromechanical properties of the sub-endocardial infarct region did not significantly affect simulated ECG or LVEF biomarkers. Results For the acute phase, simulations showed pre-cordial ECG abnormalities for all three BZ models, with T-wave inversion and QT prolongation (80 ms) in BZ1, ST-segment elevation with T-wave inversion in BZ2, and decreased T-wave amplitude in BZ3. Activation maps showed conduction block in BZ2 simulations, leading to ST-segment elevation. Repolarisation maps showed high dispersion in BZ1 simulations, leading to T-wave inversion. LVEF for BZ2 decreased by 4% (from 57% in control) due to conduction block in BZ and lack of contraction in that region, LVEF remained constant for BZ1 and BZ3 simulations. For the chronic phase, there was QT prolongation in both RZ1 (80 ms) and RZ2 (170 ms) as well as decreased T-wave amplitude, reflected by elevated repolarisation dispersion. LVEF was not significantly altered for either simulations. Conclusions Post-MI ionic remodelling at the acute and chronic stages cause varying degrees of dispersion of repolarisation and T-wave and ST-segment abnormalities, with minimal effect on mechanical function, except in the case of severe conduction abnormality. Abstract Figure.

Cardiovascular sequalae in uncomplicated COVID-19 survivors.

BackgroundA high proportion of COVID-19 patients were reported to have cardiac involvements. Data pertaining to cardiac sequalae is of urgent importance to define subsequent cardiac surveillance.MethodsWe performed a systematic cardiac screening for 97 consecutive COVID-19 survivors including electrocardiogram (ECG), echocardiography, serum troponin and NT-proBNP assay 1–4 weeks after hospital discharge. Treadmill exercise test and cardiac magnetic resonance imaging (CMR) were performed according to initial screening results.ResultsThe mean age was 46.5 ± 18.6 years; 53.6% were men. All were classified with non-severe disease without overt cardiac manifestations and did not require intensive care. Median hospitalization stay was 17 days and median duration from discharge to screening was 11 days. Cardiac abnormalities were detected in 42.3% including sinus bradycardia (29.9%), newly detected T-wave abnormality (8.2%), elevated troponin level (6.2%), newly detected atrial fibrillation (1.0%), and newly detected left ventricular systolic dysfunction with elevated NT-proBNP level (1.0%). Significant sinus bradycardia with heart rate below 50 bpm was detected in 7.2% COVID-19 survivors, which appeared to be self-limiting and recovered over time. For COVID-19 survivors with persistent elevation of troponin level after discharge or newly detected T wave abnormality, echocardiography and CMR did not reveal any evidence of infarct, myocarditis, or left ventricular systolic dysfunction.ConclusionCardiac abnormality is common amongst COVID-survivors with mild disease, which is mostly self-limiting. Nonetheless, cardiac surveillance in form of ECG and/or serum biomarkers may be advisable to detect more severe cardiac involvement including atrial fibrillation and left ventricular dysfunction.

Mechanistic Model-Informed Proarrhythmic Risk Assessment of Drugs: Review of the "CiPA" Initiative and Design of a Prospective Clinical Validation Study.

The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative is developing and validating a mechanistic‐based assessment of the proarrhythmic risk of drugs. CiPA proposes to assess a drug's effect on multiple ion channels and integrate the effects in a computer model of the human cardiomyocyte to predict proarrhythmic risk. Unanticipated or missed effects will be assessed with human stem cell‐derived cardiomyocytes and electrocardiogram (ECG) analysis in early phase I clinical trials. This article provides an overview of CiPA and the rationale and design of the CiPA phase I ECG validation clinical trial, which involves assessing an additional ECG biomarker (J‐Tpeak) for QT prolonging drugs. If successful, CiPA will 1) create a pathway for drugs with hERG block / QT prolongation to advance without intensive ECG monitoring in phase III trials if they have low proarrhythmic risk; and 2) enable updating drug labels to be more informative about proarrhythmic risk, not just QT prolongation.

Best Clinical Practice: Current Controversies in Evaluation of Low-Risk Chest Pain—Part 1

BackgroundChest pain is a common presentation to the emergency department (ED), though the majority of patients are not diagnosed with acute coronary syndrome (ACS). Many patients are admitted to the hospital due to fear of ACS. ObjectiveOur aim was to investigate controversies in low-risk chest pain evaluation, including risk of missed ACS, stress test, and coronary computed tomography angiography (CCTA). DiscussionChest pain accounts for 10 million ED visits in the United States annually. Many patients are at low risk for a major cardiac adverse event (MACE). With negative troponin and nonischemic electrocardiogram (ECG), the risk of MACE and myocardial infarction (MI) is < 1%. The American Heart Association recommends further evaluation in low- to intermediate-risk patients within 72 h. These modalities add little to further risk stratification. These evaluations do not appropriately risk stratify patients who are already at low risk, nor do they diagnose acute MI. CCTA is an anatomic evaluation of the coronary vasculature with literature support to decrease ED length of stay, though it is associated with downstream testing. Literature is controversial concerning further risk stratification in already low-risk patients. ConclusionsWith nonischemic ECG and negative cardiac biomarker, the risk of ACS approaches < 1%. Use of stress test and CCTA for risk stratification of low-risk chest pain patients is controversial. These tests may allow prognostication but do not predict ACS risk beyond ECG and troponin. CCTA may be useful for intermediate-risk patients, though further studies are required.

Eighth TRM Forum on Computer Simulation and Experimental Assessment of Cardiac Function: Towards Integration of Cardiac Functions.

Since 1998, the Theo Rossi di Montelera (TRM) forum has brought together researchers working on computer modelling, experimental research, and clinical research, with the objective to facilitate the translation of research findings in computer modelling of the heart into the clinical field. The 8th TRM forum was held at Lugano, Switzerland, on 6–8 December 2015, organized by the TRM foundation and hosted by the Center for Computational Medicine in Cardiology in Lugano. The theme was ‘Towards Integration of Cardiac Functions’. In this forum, cardiac function was addressed from the perspectives of electrical cardiac function, mechanical cardiac function, and circulatory function. Electrical cardiac function is generally assessed clinically with a body-surface electrocardiogram (ECG). However, the ECG represents an integrative measure of whole heart electrical activity, from which it is sometimes challenging to derive information about local cardiac activity. Computer models can help us to derive better patient-specific knowledge from routine ECG and determine the underlying electrical activation sequence. An example was presented by Cardone-Noott et al. 1 with a multi-scale computational study on the role of ventricular endocardial activation sequences on QRS complex variations in both healthy hearts and in the presence of intraventricular block. This study provides physiological insights on ECG biomarkers in terms of QRS morphology and location of early endocardial activation sites. Van Dam et al. 2 presented a novel method to localize premature ventricular contractions on the papillary muscles using a 12-lead ECG together with cardiac anatomy and torso geometry derived from magnetic resonance imaging (MRI). The precise localization of the site of origin of premature ventricular contractions could facilitate the planning and execution of an ablation procedure. Saha et al. 3 developed a computer model of the atria and torso to study the changes in P wave morphology resulting from pulmonary vein (PV) ablation. Results were …

RECURRENT CHEST PAIN IN PATIENTS WITH A HISTORY OF ACUTE CORONARY SYNDROMES: ASSESSING THE GESTALT OF CANADIAN CARDIOLOGISTS

The diagnosis of an acute coronary syndrome (ACS) depends on history, physical examination, biomarkers and electrocardiogram (ECG) findings. Previous studies have validated the importance of specific historical features in the diagnosis of an ACS. However, it is not known if chest pain identical in quality to a patient’s past ACS-related chest pain has diagnostic value. We sought to determine whether clinicians use this historical feature to aid in the diagnosis of ACS. Attendees at the 2014 Canadian Cardiovascular Congress were approached to complete a survey which asked them to score the importance of “identical chest pain quality” in the diagnosis of recurrent ACS using clinical vignettes without new ECG changes or biomarker elevation. A linear scale from 1 to 10 (low-to-high) was utilized to record the likelihood of recurrent ACS. The clinical vignettes varied for age, sex, presence of diabetes, remoteness of ACS and severity of coronary disease. Recent ACS was defined as an ACS event within the preceding year, and an elderly patient was defined as 80 years of age or older. Of the 80 respondents, the majority were cardiologists and trainees (90%), and 68% were males. Sex of survey respondents for all vignettes combined did not influence ACS diagnosis (p=0.680). Early-career clinicians (<5 years in practice) accounted for approximately half of responses (49%), and were more likely to diagnose ACS compared to experienced clinicians (≥5 years in practice) (p=0.004). Respondents were more likely to diagnose ACS in male patients than in female patients (p<0.001). In elderly patients, known multi-vessel disease increased the likelihood of ACS (p<0.001), however, older age alone did not reach statistical significance (p=0.098). Presence of diabetes, or recent ACS history did not correlate with ACS diagnosis (p=0.089, and p=0.680, respectively). Median scores for vignettes are summarized in Table 1. Regardless of patient characteristics, the recurrence of “identical chest pain quality” to the incident ACS is perceived as an important aspect of ACS diagnosis. However, more experienced cardiologists appear less reliant on this historical feature. ACS was diagnosed less frequently in women reporting “identical chest pain quality.” Responses for vignettes describing patients with the highest risk of atherosclerosis were varied, with underlying multi-vessel coronary artery disease, but not diabetes increasing the likelihood of an ACS diagnosis. Future studies are needed to determine the validity of this potential diagnostic predictor.

Affordable low complexity heart/brain monitoring methodology for remote health care.

This paper introduces a dual-mode low complex on-chip methodology for processing of ECG (Electrocardiogram) and EEG (Electroencephalography) signals, wherein based on the input switch the architecture can be dynamically configured to operate either as an ECG bio-marker or EEG signal de-noising system. In both the modes the signal processing technique depends on the output of the DWT (Discrete Wavelet Transform), hence a low complex methodology has been developed in which both ECG and EEG processing blocks sharing the same DWT block resulting in low area and low power consumption. The integrated ECG and EEG methodology has been implemented in Matlab, for verifying the ECG processing block the ECG database is taken from MIT-BIH PTBDB and IITH DB, similarly for EEG processing block the EEG signals are taken from PhysioNet database. The outcome of methodology in Matlab is equal to the results obtained from individual ECG and EEG blocks.

Low-risk Chest Pain Patients Younger Than 40 Years Do Not Benefit From Admission and Stress Testing

A number of studies have suggested clinical decision rules for patients age <40 who are at low risk for acute coronary syndrome (ACS) and may be safe for discharge from the emergency department. Despite this, many such patients continue to be admitted for observation in low-risk observation units. We hypothesized that patients age <40 without coronary artery disease, with a nonischemic electrocardiogram (ECG), and normal initial troponin I (TnI) who are admitted to a CPU are at very low risk (<1%) for ACS or 30-day major adverse cardiac event (MACE) and would not benefit from observation care. This was a prospective, observational study of consecutive patients admitted to the CPU in a large-volume academic urban emergency department. Eligibility criteria included age >18 but <40, American Heart Association low-to-intermediate risk, nonischemic ECGs, and normal initial TnI. Standard descriptive statistics were used for demographics, cardiac comorbidities, and risk scores. Our primary outcomes were CPU ACS rate and 30-day MACE. MACE was defined as death, nonfatal AMI, revascularization, or out of hospital cardiac arrest. A sample size of at least 400 was chosen to have 1% precision about an expected outcome rate of 0.3% (based on prior CPU data of patients of all ages). Confidence intervals (CIs) were calculated using the refined Wilson simple asymptotic method with continuity correction. All patients were called at 30 days. All charts on index visit and any subsequent visit within 30 days were reviewed using standardized chart abstractions forms by 2 trained abstractors blinded to the hypothesis of the study. A Social Security Death Index search was performed on all patients. Three hundred eighty-four patients accounting for 403 CPU admissions were enrolled over a 28-month period. Mean age was 34.3 ± 4.5; 42% were women; and 89%, 8%, 2%, and 1% had Thrombolysis in Myocardial Infarction scores of 0, 1, 2, and 3, respectively. No patient had an abnormal TnI. The ACS rate was 0 (95% CI, 0-0.8%). The 30-day MACE rate was 0 (95% CI, 0-0.8%). Forty-two percentage of these patients received stress testing but 0 (95% CI, 0-1.8%) were positive. Patients age <40 with a normal ECG and normal first biomarker have <1% risk of ACS or 30-day MACE, such that admission and stress testing are of no benefit.

Stress testing before discharge is not required for patients with low and intermediate risk of acute coronary syndrome after emergency department short stay assessment

To investigate the usefulness of stress testing before discharge in patients assessed low to intermediate risk of acute coronary syndrome (ACS). A prospective observational study was undertaken of patients presenting to the ED with suspected myocardial ischaemia. After negative initial electrocardiogram (ECG) and serum troponin testing, patients were admitted to the emergency short stay unit (ESSU) for further evaluation using a chest pain protocol that included stress testing as the final risk stratification tool. The primary outcome measure was evidence of myocardial ischaemia at stress testing. Of the 300 patients enrolled and followed up, there were no deaths at 30 days and no myocardial infarcts in patients discharged from the short stay. Two patients (0.67%) had positive serum troponin levels at 6 h after the onset of chest pain and were diagnosed with non-ST segment elevation myocardial infarctions. Three patients (1%) had abnormal stress testing and were admitted to hospital from ESSU. On review, all three patients were high risk, according to The National Heart Foundation of Australia/Cardiac Society of Australia and New Zealand guidelines. The present study showed that an ED short stay unit can effectively evaluate and manage patients with low and intermediate risk of ACS. The study suggests that patients with low and intermediate risk for ACS might safely be discharged after normal serial ECG and cardiac biomarkers, with a view to early outpatient stress testing. With strict adherence to admission criteria, there does not appear to be any benefit of stress testing before discharge.

Non-Invasive Magnetocardiography for the Early Diagnosis of Coronary Artery Disease in Patients Presenting With Acute Chest Pain

Accurate identification of patients with acute coronary syndrome (ACS) is often difficult especially when an electrocardiogram (ECG) does not show typical elevation of ST segment. The aim of the present study was therefore to evaluate the efficacy of magnetocardiography (MCG) for diagnosis of ACS in patients with acute chest pain presenting without ST segment elevation. In the present retrospective study 364 patients with the suspected ACS without ST segment elevation were selected. Significant coronary artery disease (CAD) was defined as a stenosis > or =50% in at least one of 16 segments of the 3 major coronary arteries and their branches. The MCG recordings were obtained at resting state using a 64-channel MCG system in a magnetically shielded room. The patients were classified on the basis of the probability distribution. The presence of significant CAD was identified with a sensitivity of 84.0% and a specificity of 85.0%, compared to 44.7% and 89.8% on ECG. In the subgroup of patients without specific findings on ECG or biomarker test, MCG had a sensitivity of 73.5% and a specificity of 82.3%. MCG was acceptably sensitive and specific in identifying patients with ACS even in the absence of specific findings on ECG and positive biomarker tests. Thus, MCG seems beneficial for the early triage of patients with acute chest pain.

Perplexing Epigastric Pain-Coincident Myocardial Infarction and Acute Pancreatitis

Acute myocardial infarction (MI) complicated with acute pancreatitis has been rarely reported. A 68-year-old man presented to our department 15 hours after development of epigastric pain. In addition to his symptoms, the elevated serum pancreatic enzymes and the image study on abdominal computerized tomography all led to the diagnosis of acute pancreatitis. Elevated cardiac biomarkers and a standard 12-lead electrocardiogram (ECG) demonstrating ST-segment elevation in 5 of the 6 precordial leads suggested an attack of MI. Oral intake was resumed after medical management for his acute pancreatitis and acute MI. Coronary angiogram on day 11 revealed total occlusion of the middle segment of the left anterior-descending coronary artery. Subsequently, angioplasty with stenting was done. The patient was discharged without significant complications. It is critical to make a rapid but detailed differential diagnosis of abdominal pain. Even though acute pancreatitis-associated ECG abnormalities have been reported previously, any ECG abnormalities in a patient presenting abdominal pain should be evaluated and treated cautiously. Thorough clinical evidence, including history, physical findings, ECG, image studies and serum biomarkers, are informative in seeking and analyzing possible etiologies.

The early risk stratification of the patients with acute chest pain

Objective This investigation was designed to stratify patients with acute chest pain based on their symptoms, electrocardiogram (ECG), cardiac injury markers and the number of accompanying traditional risk factors(smoking, obesity, hyperlipemia, hypertension, diabetes), and to assess the effect of the above factors to obtain a risk stratification for patients with chest pain. Methods We identified 139 patients with acute chest pain, including 45 myocardiac infarction patients, 65 unstable angina patients and 29 chest pain patients without identified acute coronary syndrome(ACS) admitted to our Coronary Heart Center during December 2004 to February 2005. All patients accepted coronary angiography. All data was collected using questionnaires. Based on reported symptom, electrocardiogram (ECG), cardiac injury markers and the number of the accompanying traditional risk factors, we stratified all patients into four groups: Group 1, patients with acute chest pain, ECG changes and abnormal cardiac injury biomarkers. Group 2, patients with acute chest pain and ECG changes(without abnormal cardiac injury biomarkers). Group 3, patients with acute chest pain, normal ECG, normal cardiac injury biomarkers and > 2 traditional risk factors. Group 4, patients with acute chest pain, normal ECG and normal cardiac injury biomarkers, but only ≤ 2 traditional risk factors. From this data we examined the difference of ACS incidence in the four groups. Results After stratification the ACS incidence of the grouped patients in turn was 100%, 84%, 69.6% and 53.3%. The combination of early phase ECG and cardiac injury markers identified 70.9% patients with ACS(the specificity being 90.7%). The mortality of group 3 was higher compared with group 4(69.6% vs 53.3%), however the P value was more than 0.05 and didn't show significant statistical difference. The correlation analysis found the number of the traditional risk factors had a significant positive correlation ( r = 0.202, P = 0.044) with the number of stenosis being more than 50% of the artery diameter. Multiple linear regression showed the hypertension had a significant correlation with the number of the diseased regions(P = 0.014). Conclusions The risk stratification based on the symptom, ECG, cardiac injury markers and accompanying traditional risk factors is both important and available in practice. It is unsuitable for patients with a normal ECG and cardiac injury markers to differentiate ACS from non-cardiac chest pain relying only on the number of the accompanying traditional risk factors. However we found the number of the risk factors can indicate the disease severity.