ECG interpretation during the acute phase of coronary syndromes: in need of improvement?

QUESTION UNDER STUDY: Emergency room (ER) interpretation of the ECG is critical to assessment of patients with acute coronary syndromes (ACS). Our aim was to assess its reliability in our institution, a tertiary teaching hospital. METHODS: Over a 6-month period all consecutive patients admitted for ACS were included in the study. ECG interpretation by emergency physicians (EPs) was recorded on a preformatted sheet and compared with the interpretation of two specialist physicians (SPs). Discrepancies between the 2 specialists were resolved by an ECG specialist. RESULTS: Over the 6-month period, 692 consecutive patients were admitted with suspected ACS. ECG interpretation was available in 641 cases (93%). Concordance between SPs was 87%. Interpretation of normality or abnormality of the ECG was concordant between EPs and SPs in 475 cases (74%, kappa = 0.51). Interpretation of ischaemic modifications was concordant in 69% of cases, and as many ST segment elevations were unrecognised as overdiagnosed (5% each). The same findings occurred for ST segment depressions and negative T waves (12% each). CONCLUSIONS: Interpretation of the ECG recorded during ACS by 2 SPs was discrepant in 13% of cases. Similarly, EP interpretation was discrepant from SP interpretation in 25% of cases, equally distributed between over- and underdiagnosing of ischaemic changes. The clinical implications and impact of medical education on ECG interpretation require further study.

Avaliação da dose de radiação: angiotomografia coronariana - métodos retrospectivo e prospectivo

The aim of this study was to evaluate the effective dose received by patients undergoing CCTA in both acquisition methods in the period June 1st to October 30th, 2013. Data collection was performed at the Clínica Sabedotti in Ponta Grossa/PR, with General Electric Equipment VCT XT, 64 detections lines. The effective dose was measured from the thirty cases randomly selected of Picture Archival and Communication System – PACS, reported by Dose Lenght Product (DLP) equipment for each examination and the conversion factor (EDLP) set by the European Commission for cardiac region (EDLP = 0.014). The results showed significant differences in radiation dose delivered to the patient according to the employee acquisition method, Retrospective or Prospective of ECG.

Variability of ascending aorta diameter measurements as assessed with electrocardiography-gated multidetector computerized tomography and computer assisted diagnosis software.

Recently, morphometric measurements of the ascending aorta have been done with ECG-gated multidector computerized tomography (MDCT) to help the development of future novel transcatheter therapies (TCT); nevertheless, the variability of such measurements remains unknown. Thirty patients referred for ECG-gated CT thoracic angiography were evaluated. Continuous reformations of the ascending aorta, perpendicular to the centerline, were obtained automatically with a commercially available computer aided diagnosis (CAD). Then measurements of the maximal diameter were done with the CAD and manually by two observers (separately). Measurements were repeated one month later. The Bland-Altman method, Spearman coefficients, and a Wilcoxon signed-rank test were used to evaluate the variability, the correlation, and the differences between observers. The interobserver variability for maximal diameter between the two observers was up to 1.2 mm with limits of agreement [-1.5, +0.9] mm; whereas the intraobserver limits were [-1.2, +1.0] mm for the first observer and [-0.8, +0.8] mm for the second observer. The intraobserver CAD variability was 0.8 mm. The correlation was good between observers and the CAD (0.980-0.986); however, significant differences do exist (P<0.001). The maximum variability observed was 1.2 mm and should be considered in reports of measurements of the ascending aorta. The CAD is as reproducible as an experienced reader.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

Markers for silent atrial fibrillation in esophageal long-term electrocardiography.

PURPOSE Paroxysmal atrial fibrillation (PAF) often remains undiagnosed. Long-term surface ECG is used for screening, but has limitations. Esophageal ECG (eECG) allows recording high quality atrial signals, which were used to identify markers for PAF. METHODS In 50 patients (25 patients with PAF; 25 controls) an eECG and surface ECG was recorded simultaneously. Partially A-V blocked atrial runs (PBARs) were quantified, atrial signal duration in eECG was measured. RESULTS eECG revealed 1.8‰ of atrial premature beats in patients with known PAF to be PBARs with a median duration of 853ms (interquartile range (IQR) 813-1836ms) and a median atrial cycle length of 366ms (IQR 282-432ms). Even during a short recording duration of 2.1h (IQR 1.2-17.2h), PBARs occurred in 20% of PAF patients but not in controls (p=0.05). Left atrial signal duration was predictive for PAF (72% sensitivity, 80% specificity). CONCLUSIONS eECG reveals partially blocked atrial runs and prolonged left atrial signal duration - two novel surrogate markers for PAF.

A comparison of different choices for the regularization parameter in inverse electrocardiography models

Calculating the potentials on the heart’s epicardial surface from the body surface potentials constitutes one form of inverse problems in electrocardiography (ECG). Since these problems are ill-posed, one approach is to use zero-order Tikhonov regularization, where the squared norms of both the residual and the solution are minimized, with a relative weight determined by the regularization parameter. In this paper, we used three different methods to choose the regularization parameter in the inverse solutions of ECG. The three methods include the L-curve, the generalized cross validation (GCV) and the discrepancy principle (DP). Among them, the GCV method has received less attention in solutions to ECG inverse problems than the other methods. Since the DP approach needs knowledge of norm of noises, we used a model function to estimate the noise. The performance of various methods was compared using a concentric sphere model and a real geometry heart-torso model with a distribution of current dipoles placed inside the heart model as the source. Gaussian measurement noises were added to the body surface potentials. The results show that the three methods all produce good inverse solutions with little noise; but, as the noise increases, the DP approach produces better results than the L-curve and GCV methods, particularly in the real geometry model. Both the GCV and L-curve methods perform well in low to medium noise situations.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.