Loriano Galeotti

Differentiating drug-induced multichannel block on the electrocardiogram: randomized study of dofetilide, quinidine, ranolazine, and verapamil.

Block of the hERG potassium channel and prolongation of the QT interval are predictors of drug‐induced torsade de pointes. However, drugs that block the hERG potassium channel may also block other channels that mitigate torsade risk. We hypothesized that the electrocardiogram can differentiate the effects of multichannel drug block by separate analysis of early repolarization (global J–Tpeak) and late repolarization (global Tpeak–Tend). In this prospective randomized controlled clinical trial, 22 subjects received a pure hERG potassium channel blocker (dofetilide) and three drugs that block hERG and either calcium or late sodium currents (quinidine, ranolazine, and verapamil). The results show that hERG potassium channel block equally prolongs early and late repolarization, whereas additional inward current block (calcium or late sodium) preferentially shortens early repolarization. Characterization of multichannel drug effects on human cardiac repolarization is possible and may improve the utility of the electrocardiogram in the assessment of drug‐related cardiac electrophysiology.

Improving the Assessment of Heart Toxicity for All New Drugs Through Translational Regulatory Science

Fourteen drugs have been removed from the market worldwide because they cause torsade de pointes. Most drugs that cause torsade can be identified by assessing whether they block the human ether à gogo related gene (hERG) potassium channel and prolong the QT interval on the electrocardiogram. In response, regulatory agencies require new drugs to undergo “thorough QT” studies. However, some drugs block hERG potassium channels and prolong QT with minimal torsade risk because they also block calcium and/or sodium channels. Through analysis of clinical and preclinical data from 34 studies submitted to the US Food and Drug Administration and by computer simulations, we demonstrate that by dividing the QT interval into its components of depolarization (QRS), early repolarization (J−Tpeak), and late repolarization (Tpeak−Tend), along with atrioventricular conduction delay (PR), it may be possible to determine which hERG potassium channel blockers also have calcium and/or sodium channel blocking activity. This translational regulatory science approach may enable innovative drugs that otherwise would have been labeled unsafe to come to market.

Comprehensive Translational Assessment of Human Induced Pluripotent Stem Cell Derived Cardiomyocytes for Evaluating Drug-Induced Arrhythmias

Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) hold promise for assessment of drug-induced arrhythmias and are being considered for use under the comprehensive in vitro proarrhythmia assay (CiPA). We studied the effects of 26 drugs and 3 drug combinations on 2 commercially available iPSC-CM types using high-throughput voltage-sensitive dye and microelectrode-array assays being studied for the CiPA initiative and compared the results with clinical QT prolongation and torsade de pointes (TdP) risk. Concentration-dependent analysis comparing iPSC-CMs to clinical trial results demonstrated good correlation between drug-induced rate-corrected action potential duration and field potential duration (APDc and FPDc) prolongation and clinical trial QTc prolongation. Of 20 drugs studied that exhibit clinical QTc prolongation, 17 caused APDc prolongation (16 in Cor.4U and 13 in iCell cardiomyocytes) and 16 caused FPDc prolongation (16 in Cor.4U and 10 in iCell cardiomyocytes). Of 14 drugs that cause TdP, arrhythmias occurred with 10 drugs. Lack of arrhythmic beating in iPSC-CMs for the four remaining drugs could be due to differences in relative levels of expression of individual ion channels. iPSC-CMs responded consistently to human ether-a-go-go potassium channel blocking drugs (APD prolongation and arrhythmias) and calcium channel blocking drugs (APD shortening and prevention of arrhythmias), with a more variable response to late sodium current blocking drugs. Current results confirm the potential of iPSC-CMs for proarrhythmia prediction under CiPA, where iPSC-CM results would serve as a check to ion channel and in silico modeling prediction of proarrhythmic risk. A multi-site validation study is warranted.

Evaluating strict and conventional left bundle branch block criteria using electrocardiographic simulations

AIMS Left bundle branch block (LBBB) is a critical predictor of patient benefit from cardiac resynchronization therapy (CRT), but recent studies suggest that one-third of patients diagnosed with LBBB by conventional electrocardiographic (ECG) criteria may have a false-positive diagnosis. In this study, we tested the hypothesis that recently proposed strict LBBB ECG criteria improve specificity in cases of left ventricular hypertrophy (LVH) /dilatation and incomplete LBBB. METHODS AND RESULTS We developed five heart models based on a healthy male with increasing degrees of LV hypertrophy and/or dilation. With each model, we simulated six conduction types: normal conduction, four increments of delayed initiation of LV activation (incomplete LBBB), and complete LBBB. Simulated ECGs were evaluated for the presence of LBBB by conventional (LV conduction delay and QRSd ≥120 ms) and strict ECG criteria (LV conduction delay, QRSd ≥140 ms men or ≥130 ms women, and mid-QRS notching in at least two of the leads I, aVL, V1, V2, V5, and/or V6). Both conventional and strict LBBB criteria had 100% sensitivity. However, conventional criteria falsely diagnosed LBBB in cases with LVH + LV dilated 10 mm, LVH or LV dilated 10 mm combined with LV initiation ≥6 ms after the right ventricle (RV), and with LV dilated 5 mm combined with LV initiation ≥12 ms after RV (48% specificity). Strict LBBB criteria resulted in no false positives (100% specificity). CONCLUSIONS New strict LBBB criteria increase the specificity of complete LBBB diagnosis in the presence of LV hypertrophy/dilatation and incomplete LBBB, which is critical for selecting CRT patients.

Automatic ECG quality scoring methodology: mimicking human annotators

An algorithm to determine the quality of electrocardiograms (ECGs) can enable inexperienced nurses and paramedics to record ECGs of sufficient diagnostic quality. Previously, we proposed an algorithm for determining if ECG recordings are of acceptable quality, which was entered in the PhysioNet Challenge 2011. In the present work, we propose an improved two-step algorithm, which first rejects ECGs with macroscopic errors (signal absent, large voltage shifts or saturation) and subsequently quantifies the noise (baseline, powerline or muscular noise) on a continuous scale. The performance of the improved algorithm was evaluated using the PhysioNet Challenge database (1500 ECGs rated by humans for signal quality). We achieved a classification accuracy of 92.3% on the training set and 90.0% on the test set. The improved algorithm is capable of detecting ECGs with macroscopic errors and giving the user a score of the overall quality. This allows the user to assess the degree of noise and decide if it is acceptable depending on the purpose of the recording.

Mechanisms of sex and age differences in ventricular repolarization in humans

INTRODUCTION Corrected QT interval (QTc) is shorter in postpubertal men than in women; however, QTc lengthens as men age and testosterone levels decrease. Animal studies have demonstrated that testosterone decreases L-type calcium current and increases slow delayed rectifier potassium current; however, it is not known how these contribute to QTc differences by sex and age in humans. We separately analyzed early versus late repolarization duration and performed simulations of the effect of testosterone on the electrocardiogram (ECG) to examine the mechanism of sex and age differences in QTc in humans. METHODS Twelve-lead ECGs from 2,235 healthy subjects (41% women) in Thorough QT studies were analyzed to characterize sex- and age-dependent differences in depolarization (QRS), early repolarization (J-T(peak)), and late repolarization (T(peak)-T(end)). In addition, we simulated the effects of testosterone on calcium current, slow delayed rectifier potassium current, and surface ECG intervals. RESULTS QTc was shorter in men than in women (394 ± 16 vs 408 ± 15 milliseconds, P < .001), which was due to shorter early repolarization (213 ± 16 vs 242 ± 16 milliseconds, P < .001), as men had longer depolarization (94 ± 7 vs 89 ± 7 milliseconds, P < .001) and longer late repolarization (87 ± 10 vs 78 ± 9 milliseconds, P < .001). Sex difference in QTc decreased with age and was due to changes in early repolarization. Simulations showed that the early repolarization changes were most influenced by testosterones effect on calcium current. CONCLUSION Shorter QTc in men compared to women is explained by shorter early repolarization, and this difference decreases with age. These sex and age differences in repolarization appear to be caused by testosterone effects on calcium current.

Transformation of the Mason-Likar 12-lead electrocardiogram to the Frank vectorcardiogram

Vectorcardiograpic (VCG) parameters can supplement the diagnostic information of the 12-lead electrocardiogram (ECG). Nevertheless, the VCG is seldom recorded in modern-day practice. A common approach today is to derive the Frank VCG from the standard 12-lead ECG (distal limb electrode positions). There is, to date no direct method that allows for a transformation from 12-lead ECGs with proximal limb electrode positions (Mason-Likar (ML) 12-lead ECG), to Frank VCGs. In this research, we develop such a transformation (ML2VCG) by means of multivariate linear regression on a training data set of 545 ML 12-lead ECGs and corresponding Frank VCGs that were both extracted surface potential maps (BSPMs). We compare the performance of the ML2VCG method against an alternative approach (2step method) that utilizes two existing transformations that are applied consecutively (ML 12-lead ECG to standard 12-lead ECG and subsequently to Frank VCG). We quantify the performance of ML2VCG and 2 step on an unseen test dataset (181 ML 12-lead ECGs and corresponding Frank VCGs again extracted from BSPMs) through root mean squared error (RMSE) values, calculated over the QRST, between actual and transformed Frank leads. The ML2VCG transformation achieved a reduction of the median RMSE values for leads X (13.9μV; p<;.001), Y (15.1μV; p<;.001) and Z (2.6μV; p=.001) when compared to the 2 step transformation. Our results show that the 2step method may not be optimal when transforming ML 12-lead ECGs to Frank VCGs. The utilization of the herein developed ML2VCG transformation should thus be considered when transforming ML 12-lead ECGs to Frank VCGs.

Comparison of the Relation Between Left Ventricular Anatomy and QRS Duration in Patients With Cardiomyopathy With Versus Without Left Bundle Branch Block

QRS duration (QRSd) is used to diagnose left bundle branch block (LBBB) and is important to determine cardiac resynchronization therapy eligibility. The same QRSd thresholds established decades ago are used for all patients. However, significant interpatient variability of normal QRSd exists, and individualized QRSd thresholds might improve diagnosis and intervention strategies. Previous work reported left ventricular (LV) mass and papillary muscle location predicted QRSd in healthy subjects, but the relation in diseased ventricles is unknown. The aim of the present study was to determine the association between LV anatomy and QRSd in patients with cardiomyopathy. Patients referred for primary prevention implantable defibrillators (n = 166) received cardiac magnetic resonance imaging, and those with normal conduction (without bundle branch or fascicular block) and LBBB were studied. The LV mass, length, internal diameter, LV end-diastolic volume, septal and lateral wall thicknesses, and papillary muscle location were measured. In patients with normal conduction, LV length (r = 0.35, p <0.001), mass (r = 0.32, p <0.001), diameter (r = 0.20, p = 0.03), and septal wall thickness (r = 0.20, p = 0.03) had positive correlations with QRSd. In patients with LBBB, LV length (r = 0.32, p = 0.03), mass (r = 0.39, p = 0.01), diameter (r = 0.34, p = 0.02), and LV end-diastolic volume (r = 0.32, p = 0.04) had positive correlations with QRSd. Contrary to previous studies in healthy subjects, papillary muscle angle (location) was not associated with QRSd in cardiomyopathy patients with normal conduction or LBBB. In conclusion, increasing LV anatomical measurements were associated with increasing QRSd in patients with cardiomyopathy. Future work should investigate the use of LV anatomical measurements in developing individualized QRSd thresholds for diagnosing conduction abnormalities such as LBBB and identifying candidates for cardiac resynchronization therapy.

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs.

False and non-actionable alarms in critical care can be reduced by developing algorithms which assess the trueness of an arrhythmia alarm from a bedside monitor. Computational approaches that automatically identify artefacts in ECG signals are an important branch of physiological signal processing which tries to address this issue. Signal quality indices (SQIs) derived considering differences between artefacts which occur in ECG signals and normal QRS morphology have the potential to discriminate pathologically different arrhythmic ECG segments as artefacts. Using ECG signals from the PhysioNet/Computing in Cardiology Challenge 2015 training set, we studied previously reported ECG SQIs in the scientific literature to differentiate ECG segments with artefacts from arrhythmic ECG segments. We found that the ability of SQIs to discriminate between ECG artefacts and arrhythmic ECG varies based on arrhythmia type since the pathology of each arrhythmic ECG waveform is different. Therefore, to reduce the risk of SQIs classifying arrhythmic events as noise it is important to validate and test SQIs with databases that include arrhythmias. Arrhythmia specific SQIs may also minimize the risk of misclassifying arrhythmic events as noise.

Robust algorithm to locate heart beats from multiple physiological waveforms by individual signal detector voting.

Alarm fatigue is a major issue in patient monitoring that could be reduced by merging physiological information from multiple sensors, minimizing the impact of a single sensor failing. We developed a heart beat detection algorithm that utilizes multi-modal physiological waveforms (e.g. ECG, blood pressure, stroke volume, photoplethysmogram and electroencephalogram). The 100 record training set from the Physionet challenge 2014 was used for development. The algorithm was evaluated at three testing phases during the 2014 challenge consisting of 100 (phase I), 200 (phase II) and 300 (phase III) hidden records, respectively. A true positive was declared if a beat was detected within 150 ms of a reference annotation. The algorithm had a sensitivity of >99.9%, Positive Predictive Value of 99.7%, and an overall score (average of sensitivity and Positive Predictive Value) of 99.8% when applied to the training set. The best overall performance on the test sets were: 88.9%, 76.3% and 84.4% for phases I, II and III, respectively. We developed a robust heart beat detector that fuses annotations from multiple individual detectors. The algorithm improves the training results compared to ECG detections alone, and performs well on the test sets. Data fusion approaches like this one can improve patient monitoring and reduce false alarms.