206 The time course of new T-wave ECG descriptors following single and double dose administration of Sotalol in healthy subjects

IntroductionThe aim of the study was to assess the time course effect of IKr blockade on ECG biomarkers of ventricular repolarization and to evaluate the accuracy of a fully automatic approach for QT duration evaluation.Methods12-lead digital ECG Holter were recorded in 38 healthy subjects (27 males, mean age=27.4±8.0 years) on baseline conditions (day 0) and after administration of 160 mg (day 1) and 320 mg (day 2) of d-l Sotalol. For each 24-hour period and each subject, ECGs were extracted every 10 minutes during the 4-hour period following drug dosage. Ventricular repolarization was characterized using 3 biomarker categories: conventional ECG time intervals, Principal Component Analysis (PCA) analysis on the T-wave, and fully automatic biomarkers computed from a mathematical model of the T-wave.ResultsQT interval was significantly prolonged starting 1h20 minutes after drug dosing with 160 mg and 1h 10 minutes after drug dosing with 320 mg. PCA ventricular repolarization parameters sotalol-induced changes were delayed (>3 hours). After sotalol dosing, the early phase of the T-wave changed earlier than the late phase prolongation. Globally, the modeled surrogate QT paralleled manual QT changes.The duration of manual QT and automatic surrogate QT were strongly correlated (R2=0.92, p<0.001). The Bland & Altman plot revealed a non-stationary systematic bias (bias =26.5ms ±1.96*SD = 16ms).ConclusionsChanges in different ECG biomarkers of ventricular repolarization display different kinetics after administration of a potent potassium channel blocker. These differences need to be taken into account when designing ventricular repolarization ECG studies

T-wave axis deviation, metabolic syndrome and cardiovascular risk: results from the MOLI-SANI study

Early recognition of patients at increased cardiovascular risk is a major challenge. The surface electrocardiogram provides a useful platform and it has been used to propose several indexes. T wave axis abnormality is associated with an increased risk of cardiovascular mortality, independently of other risk factors and can be associated with the presence of the metabolic syndrome (MetS). We assessed the prevalence of T axis abnormalities and its relationship with MetS and its components in a large population of Italian adults. Data concerning 11,143 women (54±11years) and 9742 men (55±11years) randomly recruited from a general population (Moli-sani cohort) were analyzed. After excluding subjects with incomplete data and with history of cardiac disease or left ventricular hypertrophy, T-wave axis was normal in 74.5% of men and 80.9% of women, borderline in 23.6% and 17.3% and abnormal in 1.9% and 1.8%. In subjects with MetS, the prevalence of borderline or abnormal T-wave axis deviation was higher than in subjects without MetS (in men: 26.6% vs. 22.1% and 2.5% vs. 1.7%; in women: 25% vs. 15% and 2.4% vs. 1.6%, respectively for borderline and abnormal levels, pb0.0001). Each component of MetS increased the odds of having borderline or abnormal T-wave axis deviation by 1.21 in men and 1.31 in women. T wave axis deviation is associated with MetS and its individual components. These findings confirm previous reported results, expanding them to a large and representative sample of European population of Caucasian ethnicity

T-wave axis deviation, metabolic syndrome and estimated cardiovascular risk in men and women of the MOLI-SANI Study

Aim: We aimed at investigating the association between T-wave axis deviation, metabolic syndrome (MetS), its components and estimated risk of cardiovascular disease (CVD) at 10 years in a adult Italian population. Methods: 11,143 women (54±11 years) and 9,742 men (55±11 years) were analysed from the Molisani cohort, randomly recruited from the general population. MetS was defined using the ATPIII criteria. T-wave axis deviation was measured from the standard 12-lead resting electrocardiogram. CVD risk in ten years was estimated by the CUORE score. Results: 29% of men and 27% of women with MetS showed borderline or abnormal T-wave as compared to 24% and 17% without MetS (p<0.0001 for both genders). Among components of MetS, elevated waist and blood pressure were strongly associated with Twave axis deviation, whereas glucose, HDL and triglycerides were only marginally. The odds of having borderline or abnormal T-wave axis deviation in multivariable regression analysis, was 1.38 (95% CI:1.25-1.53) in MetS men and 1.68 (95% CI:1.51-1.87) in MetS women compared to those without. Further adjustment for MetS components completely abolished the associations. Abnormal T-wave axis deviation was associated with an increased risk of CVD in 10 years in men (OR=4.4; 95% CI:1.10-17.9). Conclusion: T-wave axis deviation is strongly associated with components of the MetS, in particular high waist circumference and blood pressure and with an increased CVD risk, particularly in men. ECG monitoring to identify T-wave axis deviation in obese, hypertensive or MetS subjects can be an early indicator of vascular disease and help in reducing cardiac events

Comparison of automated interval measurements by widely used algorithms in digital electrocardiographs

Background: Automated measurements of electrocardiographic (ECG) intervals by current-generation digital electrocardiographs are critical to computer-based ECG diagnostic statements, to serial comparison of ECGs, and to epidemiological studies of ECG findings in populations. A previous study demonstrated generally small but often significant systematic differences among 4 algorithms widely used for automated ECG in the United States and that measurement differences could be related to the degree of abnormality of the underlying tracing. Since that publication, some algorithms have been adjusted, whereas other large manufacturers of automated ECGs have asked to participate in an extension of this comparison. Methods: Seven widely used automated algorithms for computer-based interpretation participated in this blinded study of 800 digitized ECGs provided by the Cardiac Safety Research Consortium. All tracings were different from the study of 4 algorithms reported in 2014, and the selected population was heavily weighted toward groups with known effects on the QT interval: included were 200 normal subjects, 200 normal subjects receiving moxifloxacin as part of an active control arm of thorough QT studies, 200 subjects with genetically proved long QT syndrome type 1 (LQT1), and 200 subjects with genetically proved long QT syndrome Type 2 (LQT2). Results: For the entire population of 800 subjects, pairwise differences between algorithms for each mean interval value were clinically small, even where statistically significant, ranging from 0.2 to 3.6 milliseconds for the PR interval, 0.1 to 8.1 milliseconds for QRS duration, and 0.1 to 9.3 milliseconds for QT interval. The mean value of all paired differences among algorithms was higher in the long QT groups than in normals for both QRS duration and QT intervals. Differences in mean QRS duration ranged from 0.2 to 13.3 milliseconds in the LQT1 subjects and from 0.2 to 11.0 milliseconds in the LQT2 subjects. Differences in measured QT duration (not corrected for heart rate) ranged from 0.2 to 10.5 milliseconds in the LQT1 subjects and from 0.9 to 12.8 milliseconds in the LQT2 subjects. Conclusions: Among current-generation computer-based electrocardiographs, clinically small but statistically significant differences exist between ECG interval measurements by individual algorithms. Measurement differences between algorithms for QRS duration and for QT interval are larger in long QT interval subjects than in normal subjects. Comparisons of population study norms should be aware of small systematic differences in interval measurements due to different algorithm methodologies, within-individual interval measurement comparisons should use comparable methods, and further attempts to harmonize interval measurement methodologies are warranted

Computer Assisted Patient Monitoring: Associated Patient, Clinical and ECG Characteristics and Strategy to Minimize False Alarms

This chapter is a review of studies that have examined false arrhythmia alarms during in-hospital electrocardiographic (ECG) monitoring in the intensive care unit. In addition, we describe an annotation effort being conducted at the UCSF School of Nursing, Center for Physiologic Research designed to improve algorithms for lethal arrhythmias (i.e., asystole, ventricular fibrillation, and ventricular tachycardia). Background: Alarm fatigue is a serious patient safety hazard among hospitalized patients. Data from the past five years, showed that alarm fatigue was responsible for over 650 deaths, which is likely lower than the actual number due to under-reporting. Arrhythmia alarms are a common source of false alarms and 90% are false. While clinical scientists have implemented a number of interventions to reduce these types of alarms (e.g., customized alarm settings; daily skin electrode changes; disposable vs. non-disposable lead wires; and education), only minor improvements have been made. This is likely as these interventions do not address the primary problem of false arrhythmia alarms, namely deficient and outdated arrhythmia algorithms. In this chapter we will describe a number of ECG features associated with false arrhythmia alarms. In addition, we briefly discuss an annotation effort our group has undertaken to improve lethal arrhythmia algorithms

Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation

BackgroundAccurate and timely detection of atrial fibrillation (AF) episodes is important in primarily and secondary prevention of ischemic stroke and heart-related problems. In this work, heart rate regularity of ECG inter-beat intervals was investigated in episodes of AF and other rhythms using a wavelet leader based multifractal analysis. Our aim was to improve the detectability of AF episodes.MethodsInter-beat intervals from 25 ECG recordings available in the MIT-BIH atrial fibrillation database were analysed. Four types of annotated rhythms (atrial fibrillation, atrial flutter, AV junctional rhythm, and other rhythms) were available. A wavelet leader based multifractal analysis was applied to 5 min non-overlapping windows of each recording to estimate the multifractal spectrum in each window. The width of the multifractal spectrum was analysed for its discrimination power between rhythm episodes.ResultsIn 10 of 25 recordings, the width of multifractal spectrum was significantly lower in episodes of AF than in other rhythms indicating increased regularity during AF. High classification accuracy (95%) of AF episodes was achieved using a combination of features derived from the multifractal analysis and statistical central moment features.ConclusionsAn increase in the regularity of inter-beat intervals was observed during AF episodes by means of multifractal analysis. Multifractal features may be used to improve AF detection accuracy

SCP-ECG V3.0: An Enhanced Standard Communication Protocol for Computer-assisted Electrocardiography

The main goal of the SCP-ECG standard is to address ECG data and related metadata structuring, semantics and syntax, with the objective of facilitating interoperability and thus supporting and promoting the exchange of the relevant information for unary and serial ECG diagnosis. Starting with version V3.0, the standard now also provides support for the storage of continuous, long-term ECG recordings and affords a repository for selected ECG sequences and the related metadata to accommodate stress tests, drug trials and protocol-based ECG recordings. The global and per-lead measurements sections have been extended and three new sections have been introduced for storing beat-by-beat and/or spike-by-spike measurements and annotations. The used terminology and the provided measurements and annotations have been harmonized with the ISO/IEEE 11073-10102 Annotated ECG standard. Emphasis has also been put on harmonizing the Universal Statement Codes with the CDISC and the categorized AHA statement codes and similarly the drug and implanted devices codes with the ATC and NASPE/BPEG codes.publishedVersionPeer reviewe