Křemen, Václav

Background: Although the hallmark of long-QT syndrome (LQTS) is abnormal cardiac repolarization, there are varying degrees of phenotypic expression and arrhythmic risk. Our aim was to evaluate the performance of a morphological T-wave analysis program in defining breakthrough LQTS arrhythmic risk beyond the QTc value. Methods and Results: We analyzed 407 genetically confirmed patients with LQT1 (n=246; 43% men) and LQT2 (n=161; 41% men) over the mean follow-up period of 6.4±3.9 years. ECG analysis was conducted using a novel, proprietary T-wave analysis program. Time to a LQTS-associated cardiac event was analyzed using Cox proportional hazards regression methods. Twenty-three patients experienced ≥1 defined breakthrough cardiac arrhythmic events with 5- and 10-year event rates of 4% and 7%. Two independent predictors of future LQTS-associated cardiac events from the surface ECG were identified: left slope of T wave in lead V6 (hazard ratio=0.40 [0.24–0.69]; P <0.001) and T-wave center of gravity x axis (last 25% of wave) in lead I (hazard ratio=1.90 [1.21–2.99]; P =0.005), C statistic of 0.77 (0.65–0.89). When added to the QTc (C statistic 0.68 for QTc alone), discrimination improved to 0.78. Genotype analysis showed weaker association between these T-wave variables and LQT1-triggered events while these features were stronger in patients with LQT2 and significantly outperformed the QTc (C statistic, 0.82 [0.71–0.93]). Conclusion: Detailed morphological analysis of the T wave provides novel insights into risk of breakthrough arrhythmic events in LQTS, particularly LQT2. This observation has the potential to guide clinical decision making and further refine risk stratification.

BackgroundProlongation of the QT on the surface electrocardiogram can be due to either genetic or acquired causes. Distinguishing congenital long QT syndrome (LQTS) from acquired QT prolongation has important prognostic and management implications. We aimed to investigate if quantitative T‐wave analysis could provide a tool for the physician to differentiate between congenital and acquired QT prolongation.MethodsPatients were identified through an institution‐wide computer‐based QT screening system which alerts the physician if the QTc ≥ 500 ms. ECGs were retrospectively analyzed with an automated T‐wave analysis program. Congenital LQTS was compared in a 1:3 ratio to those with an identified acquired etiology for QT prolongation (electrolyte abnormality and/or prescription of known QT prolongation medications). Linear discriminant analysis was performed using 10‐fold cross‐validation to statistically test the selected features.ResultsThe 12‐lead ECG of 38 patients with congenital LQTS and 114 patients with drug‐induced and/or electrolyte‐mediated QT prolongation were analyzed. In lead V5, patients with acquired QT prolongation had a shallower T wave right slope (−2,322 vs. −3,593 mV/s), greater T‐peak‐Tend interval (109 vs. 92 ms), and smaller T wave center of gravity on the x axis (290 ms vs. 310 ms; p < .001). These features could distinguish congenital from acquired causes in 77% of cases (sensitivity 90%, specificity 58%).ConclusionT‐wave morphological analysis on lead V5 of the surface ECG could successfully differentiate congenital from acquired causes of QT prolongation.

Objective Sudden cardiac arrest (SCA) is the most devastating outcome in hypertrophic cardiomyopathy (HCM). We evaluated repolarisation features on the surface electrocardiogram (ECG) to identify the potential risk factors for SCA. Methods Data was collected from 52 patients with HCM who underwent implantable cardioverter defibrillator (ICD) implantation. Leads V2 and V5 from the ECG closest to the time of ICD implant were utilised for measuring the Tpeak-Tend interval (Tpe), QTc, Tpe/QTc, T-wave duration and T-wave amplitude. The presence of the five traditional SCA-associated risk factors was assessed, as well as the HCM risk-SCD score. Results 16 (30%) patients experienced aborted cardiac arrest over 8.5±4.1 years, with 9 receiving an ICD shock and 7 receiving ATP. On univariate analysis, T-wave amplitude was associated with appropriate ICD therapy (HR per 0.1 mV 0.79, 95% CI 0.56 to 0.96, p=0.02). Aborted SCA was not associated with a greater mean QTc duration, Tpeak-Tend interval, T-wave duration, or Tpe/QT ratio. Multivariate analysis (adjusting for cardinal HCM SCA-risk factors) showed T-wave amplitude in Lead V2 was an independent predictor of risk (adjusted HR per 0.1 mV 0.74, 95% CI 0.57 to 0.97, p=0.03). Addition of T-wave amplitude in Lead V2 to the traditional risk factors resulted in significant improvement in risk stratification (C-statistic from 0.65 to 0.75) but did not improve the performance of the HCM SCD-risk score. Conclusions T-wave amplitude is a novel marker of SCA in this high risk HCM population and may provide incremental predictive value to established risk factors. Further work is needed to define the role of repolarisation abnormalities in predicting SCA in HCM.

Abstract Objective . Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach . Data from seven patients (age 34 ± 12 , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1–600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results . Classification accuracy of 97.8 ± 0.3% (normal tissue) and 89.4 ± 0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8 ± 0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1 ± 1.6%). Spectral power in high frequency band features (Ripple (80–250 Hz), Fast Ripple (250–600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy ⩾90% using a single electrode contact and single spectral feature. Significance . Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

Cardiac output (CO) assessment as a basic hemodynamic parameter has been of interest in exercise physiology, cardiology, and anesthesiology. Noninvasive techniques available are technically challenging, and thus difficult to use outside of a clinical or laboratory setting. We propose a novel method of noninvasive CO assessment using a single, upper-arm cuff. The method uses the arterial pressure pulse wave signal acquired from the brachial artery during 20-s intervals of suprasystolic occlusion. This method was evaluated in a cohort of 12 healthy individuals (age, 27.7 ± 5.4 yr, 50% men) and compared with an established method for noninvasive CO assessment, the open-circuit acetylene method (OpCirc) at rest, and during low- to moderate-intensity exercise. CO increased from rest to exercise (rest, 7.4 ± 0.8 vs. 7.2 ± 0.8; low, 9.8 ± 1.8 vs. 9.9 ± 2.0; moderate, 14.1 ± 2.8 vs. 14.8 ± 3.2 l/min) as assessed by the cuff-occlusion and OpCirc techniques, respectively. The average error of experimental technique compared with OpCirc was −0.25 ± 1.02 l/min, Pearson’s correlation coefficient of 0.96 (rest + exercise), and 0.21 ± 0.42 l/min with Pearson’s correlation coefficient of 0.87 (rest only). Bland-Altman analysis demonstrated good agreement between methods (within 95% boundaries); the reproducibility coefficient (RPC) = 0.84 l/min with R2= 0.75 at rest and RPC = 2 l/min with R2= 0.92 at rest and during exercise, respectively. In comparison with an established method to quantify CO, the cuff-occlusion method provides similar measures at rest and with light to moderate exercise. Thus, we believe this method has the potential to be used as a new, noninvasive method for assessing CO during exercise.

Background— Congenital long QT syndrome (LQTS) is characterized by QT prolongation. However, the QT interval itself is insufficient for diagnosis, unless the corrected QT interval is repeatedly ≥500 ms without an acquired explanation. Further, the majority of LQTS patients have a corrected QT interval below this threshold, and a significant minority has normal resting corrected QT interval values. Here, we aimed to develop and validate a novel, quantitative T wave morphological analysis program to differentiate LQTS patients from healthy controls. Methods and Results— We analyzed a genotyped cohort of 420 patients (22±16 years, 43% male) with either LQT1 (61%) or LQT2 (39%). ECG analysis was conducted using a novel, proprietary T wave analysis program that quantitates subtle changes in T wave morphology. The top 3 discriminating features in each ECG lead were determined and the lead with the best discrimination selected. Classification was performed using a linear discriminant classifier and validated on an untouched cohort. The top 3 features were Tpeak–Tend interval, T wave left slope, and T wave center of gravity x axis (last 25% of the T wave). Lead V6 had the best discrimination. It could distinguish 86.8% of LQTS patients from healthy controls. Moreover, it distinguished 83.33% of patients with concealed LQTS from controls, despite having essentially identical resting corrected QT interval values. Conclusions— T wave quantitative analysis on the 12-lead surface ECG provides an effective, novel tool to distinguish patients with either LQT1/LQT2 from healthy matched controls. It can provide guidance while mutation-specific genetic testing is in motion for family members.