Joel Xue

Reference values of electrocardiogram repolarization variables in a healthy population.

INTRODUCTION Reference values for T-wave morphology analysis and evaluation of the relationship with age, sex, and heart rate are lacking in the literature. In this study, we characterized T-wave morphology in a large sample of healthy individuals. METHOD A total of 1081 healthy subjects (83% men; range, 17-81 years) were included. T-wave morphology variables describing the duration, area, slopes, amplitude, and distribution were calculated using 10-second digital electrocardiogram recordings. Multivariate regression was used to test for dependence of T-wave variables with the subject age, sex, and heart rate. RESULTS Lead V5 (men vs women) T-wave variables were as follows: amplitude, 444 versus 317 muV; area, 48.4 versus 33.2 ms mV; Tpeak-Tend interval, 94 versus 92 milliseconds; maximal descending slope, -5.15 versus -3.69 muV/ms; skewness, -0.24 versus -0.22; and kurtosis, -0.36 versus -0.35. Tpeak-Tend interval, skewness, and kurtosis were independent of age, sex, and heart rate (r(2) < 0.05), whereas Bazett-corrected QT-interval was more dependent (r(2) = 0.40). CONCLUSION A selection of T-wave morphology variables is found to be clinically independent of age, sex, and heart rate, including Tpeak-Tend interval, skewness, and kurtosis.

Comparison of automated measurements of electrocardiographic intervals and durations by computer-based algorithms of digital electrocardiographs

BACKGROUND AND PURPOSE Automated measurements of electrocardiographic (ECG) intervals are widely used by clinicians for individual patient diagnosis and by investigators in population studies. We examined whether clinically significant systematic differences exist in ECG intervals measured by current generation digital electrocardiographs from different manufacturers and whether differences, if present, are dependent on the degree of abnormality of the selected ECGs. METHODS Measurements of RR interval, PR interval, QRS duration, and QT interval were made blindly by 4 major manufacturers of digital electrocardiographs used in the United States from 600 XML files of ECG tracings stored in the US FDA ECG warehouse and released for the purpose of this study by the Cardiac Safety Research Consortium. Included were 3 groups based on expected QT interval and degree of repolarization abnormality, comprising 200 ECGs each from (1) placebo or baseline study period in normal subjects during thorough QT studies, (2) peak moxifloxacin effect in otherwise normal subjects during thorough QT studies, and (3) patients with genotyped variants of congenital long QT syndrome (LQTS). RESULTS Differences of means between manufacturers were generally small in the normal and moxifloxacin subjects, but in the LQTS patients, differences of means ranged from 2.0 to 14.0 ms for QRS duration and from 0.8 to 18.1 ms for the QT interval. Mean absolute differences between algorithms were similar for QRS duration and QT intervals in the normal and in the moxifloxacin subjects (mean ≤6 ms) but were significantly larger in patients with LQTS. CONCLUSIONS Small but statistically significant group differences in mean interval and duration measurements and means of individual absolute differences exist among automated algorithms of widely used, current generation digital electrocardiographs. Measurement differences, including QRS duration and the QT interval, are greatest for the most abnormal ECGs.

A robust method for quantification of IKr-related T-wave morphology abnormalities

The QTc interval plays an important role in premarket testing of new drugs, but the intrinsic variability of the measurement is critical. Most arrhythmogenic drugs inhibit the IKr current and cause both QTc prolongation and changes in T-wave morphology. Quantification of T-wave morphology may be useful in drug testing, but no robust method exists for this purpose. We present a method for quantification of IKr-related T-wave morphology changes: T-wave asymmetry, flatness and the presence of notches on the T-wave combined to an overall morphology combination score (MCS). In a population of 30 LQT2-subjects (congenital IKr inhibition) and 1096 healthy subjects, both QTcF and MCS yield clear separation between the groups (p<0.001), sensitivity 90%, specificity 95%.

Study of repolarization heterogeneity and electrocardiographic morphology with a modeling approach

BACKGROUND Increase of heart repolarization heterogeneity has been linked to severe or even life-threatening arrhythmia like torsades de pointes and other forms of ventricular tachycardia. Although electrocardiography (ECG) still remains as the most convenient and cost-effective method of monitoring electrical activity of the heart, the link between ECG morphology and repolarization heterogeneity is not clear. Previous attempts of using QT interval dispersion from multiple leads to assess the heterogeneity changes were not successful either. METHOD The aim of this study is to use a cell-to-ECG model to study ECG morphology changes while varying transmural heterogeneity. The heterogeneity is simulated by increasing the difference of M cell Ikr block factors from either endocardial or epicardial cells. The model-simulated ECGs were processed and measured. The ECG parameters studied include QT interval dispersion of standard 12-lead ECG, QT peak dispersion, and T-peak to T-end interval (TpTe). An ECG vector magnitude signal based on 12-lead ECG was formed to estimate the global QT interval (vs lead-by-lead QT interval used for calculating QT dispersion) and also the global TpTe (TpTe_VM). RESULTS The results based on the model simulation show that the TpTe_VM is highly correlated with transmural dispersion of repolarization (TDR), with a correlation coefficient of 0.97. The correlation coefficients of QT interval dispersion and QT peak dispersion with TDR are 0.44 and 0.80, respectively. CONCLUSION In conclusion, the cell-to-ECG model provides a unique way to study electrophysiology and to link physiologic factors to ECG morphology changes. The simulation results suggest that global TpTe can be a strong indicator of TDR.

Identify drug-induced T wave morphology changes by a cell-to-electrocardiogram model and validation with clinical trial data.

BACKGROUND Increase of repolarization heterogeneity has been identified as a major factor for drug-induced arrhythmia event like torsade de pointes. In recent years, there have been quite a few efforts for studying T wave morphology changes, hoping to identify more sensitive proarrhythmia electrocardiogram (ECG) biomarkers than QT interval. However, the associations among ECG morphologies and the repolarization heterogeneities are still not clear. METHOD A cell-to-ECG model has been built by our group to study relationship between multiple factors of ion channels on the heart tissue and ECG morphology changes measured on the torso. More specifically, we varied both transmural (from Epi to Endo myocardium layers) and apex-to-base heterogeneities by blocking rapid delayed rectifier potassium current (Ikr), slow delayed rectifier potassium current (Iks), and late sodium current (InaL) with different extents on Epi, M, and Endo myocardium. On ECG measurement part, the study was focused on some new morphology-related features including T-peak to T-end (TpTe) interval, T wave flatness, T wave symmetric, and T wave notch. Two types of transmural dispersion of repolarization (TDR) were created: global and localized heterogeneities. Vector magnitude and principal component-based composite leads were formed from multiple chest leads for robustness against large variation of individual lead due to placement and noise issues. Cross-correlation methods were used to determine the relationship of the new ECG morphology features with the heterogeneities. All the ECG morphology measurements were first analyzed with the cell-to-ECG model and then validated with previously acquired clinical trial ECG data (d-sotalol). RESULTS The results based on our cell-to-ECG model showed that the new TpTe interval of the composite signal based on V2, V3, and V4 leads has the correlation coefficients of 0.99 and 0.98 with the simulated global and localized TDR, respectively, highest among other tested ECG parameters. The combined T wave morphology score has the correlation coefficients of 0.98 and 0.92 with the simulated global and localized TDR, respectively. The validation results of d-sotalol show that new TpTe measurement has a correlation coefficient of 0.90 with plasma concentration, and the parameters correlation with heart rate is 0.02. CONCLUSIONS The study provided preliminary results showing the usefulness of the cell-to-ECG model for studying relationship between multiple ion-channel factors with ECG morphology changes. The global and localized TDR generate very different T wave morphologies. The newly identified T wave morphology parameters are highly correlated with transmural dispersion and are heart rate independent.

Electrocardiographic morphology changes with different type of repolarization dispersions

BACKGROUND T-wave morphology changes have been linked to heterogeneity of ventricular repolarization and increase of arrhythmia vulnerability. Therefore, century-long debates around the genesis of T wave become even more relevant. Here are some interesting questions for the debates: (1) why T waves are usually concordant with QRS complex? (2) Is there a significant and consistent transmural dispersion of repolarization across heart wall? (3) What kind of T-wave morphology changes can be induced by either transmural or apical-basal dispersion of repolarization? METHOD The previously developed GEs cell-to-electrocardiogram (ECG) model (GE Healthcare, Milwaukee, WI) was used to study the relation between cellular behavior and the T-wave morphology. The study focused on 2 types of repolarization dispersions: (1) Transmural (from endocardium to epicardium) and (2) Apical-basal (from apex to base of ventricles). More specifically, the transmural dispersions were created by adjusting the slow and fast delayed potassium rectifier current (Iks, Ikr) and transient outward current (Ito), on endocardial, midmyocardial (M cell) and epicardial cells separately. The apical-basal dispersion was adjusted according to the coordinates along the axis from the base to the apex of the ventricle. The contribution of M cell toward T-wave morphology were studied by adjusting the M cells repolarization time in the range of shorter to longer than those of endocardial repolarization time. RESULTS In the global transmural dispersion cases, QT interval is prolonged from 350 to 450 milliseconds, T-peak to T-end interval (TpTe) is prolonged from 50 to 130 milliseconds, and T-wave notches appeared when the heterogeneity is increased. In the localized transmural dispersion cases, significant T-wave morphology features such as TpTe, T-wave notches appeared in very limited precordial leads. In the global apical-basal dispersion cases, main T-wave change is on the amplitude, and T waves in several precordial leads and lead II turn to positive from negative. And the localized apical-basal dispersion does not generate significant T-wave morphology changes. CONCLUSIONS The cell-to-ECG model provides a unique way to study electrophysiology and to link physiologic factors to ECG morphology changes. The simulation results suggest that the apical-basal dispersion of repolarization contributes to positive T wave more than the transmural dispersion. The contribution of localized transmural dispersion to surface ECG is very much localized to certain precordial leads.

Fuzzy Expert Systems For Sequential Pattern Recognition For Patient Status Monitoring in Operating Room

In this paper, we present several fuzzy inference systems for monitoring patient status in an operating room. The algorithms used include recursive fuzzy inference (RFIS), and non-recursive with sequential patterns as inputs. The RFIS algorithm combines current patient status data with previous output of the inference system, therefore is able to reinforce the current finding based on previous sequential system output. The results show that the RFIS system can be tuned towards higher sensitivity for more critical status, while generating smoother inference output

Principal component analysis of the T-wave in patients with chest pain and conduction disturbances.

There is a need for markers reflecting the increased risk in patients with conduction disturbances. Conduction disturbances presumably cause inhomogeneous repolarization that may create an arrhythmogenic substrate. In patients with normal conduction, parameters derived from principal components analysis (PCA) of the T wave contain prognostic information. The nondipolar PCA components are assumed to reflect repolarization inhomogeneity. This study examined the PCA parameters in relation to conduction disturbances. PCA was performed on continuously recorded 12‐lead ECGs in 800 patients with chest pain and nondiagnostic ECG on admission. The patients with conduction disturbance on admission were classified into separate groups and related to comparison groups without conduction disturbance recruited from the same series. For each patient, the dipolar and nondipolar components were quantified by medians of the ratio of the two largest eigenvalues (S2/S1 Median), the residue that summarizes the eigenvalues S4–S8 (TWRabsMedian) and the ratio of this residue to the total power of the T wave (TWRrelMedian). The parameters were assessed with respect to common clinical and ECG parameters, discharge diagnosis, and total mortality during a 35‐month follow‐up. TWRabsMedian increased with increasing conduction disturbance. In 135 patients with conduction disturbances, ROC curves for TWRabsMedian as indicator of mortality exhibited areas under a curve of 0.66, 0.65, and 0.56 at 6‐month, 24‐month, and 35‐month follow‐up. Conduction disturbances were associated with increased nondipolar PCA component and, thus, with increased repolarization inhomogeneity. The nondipolar PCA component contained a moderate amount of prognostic information not present in a simple ECG diagnosis of a conduction disturbance.

Enhanced rhythm analysis for resting ECG using spectral and time-domain techniques

Interpretation of cardiac rhythms is highly dependent on accurate detection of atrial activity. Several new enhancements were made to the previously described MAC-RHYTHM atrial analysis program, including spectral analysis for the detection of atrial flutter; optimal lead selection for P wave detection; and T wave alignment to reduce subtraction artifact in the residual signals used to create a P wave detection function. Performance was assessed using a test set of 69957 confirmed ECGs from four hospitals. The rhythm interpretation in the confirmed ECG was compared to the rhythm interpretations from the previous and new versions of the program. The rate of disagreements between the confirmed rhythm and the computerized interpretation decreased from 6.9% to 4.1%. Sensitivity improved for sinus, atrial fibrillation, atrial flutter, and junctional rhythms, while specificity and positive predictive value improved for all arrhythmias.

Optimizing Fuzzy Clinical Decision Support Rules Using Genetic Algorithms

In this paper, we present a technique for optimizing a fuzzy system using a genetic algorithm that works for patient status monitoring in the operating room. The genetic algorithm adjusts rule weights, outputs, and input membership functions to maximize the area under a receiver operator curve (ROC) for final classification. Compared to pre-optimization, the optimized fuzzy inference system increased ROC area from 0.68 to 0.77, which can be translated to an increase in specificity from 74% to 82%, at a fixed sensitivity of 58%