Branislav Vajdic

Vectorcardiographic determinants of cardiac memory during normal ventricular activation and continuous ventricular pacing

BACKGROUND Cardiac memory (CM) refers to persistent T-wave changes on resumption of normal conduction after a period of abnormal ventricular activation. Traditionally, to observe CM, normal ventricular activation had to be restored, limiting the exploration of this phenomenon in clinical practice. OBJECTIVE This study sought to prove that CM can be detected during continuous aberrant activation and to establish factors affecting its magnitude using a vectorcardiographic technique. METHODS Sixteen nonpacemaker-dependent patients (11 male, age 72 +/- 8 years, mean +/- SD) undergoing pacemaker/internal cardioverter-defibrillator implantation were paced in DDD mode with a short atrioventricular (AV) delay for 7 days to induce CM. Electrocardiograms were acquired during AAI and DDD pacing at a constant rate before and after CM induction. Dower transform-derived vectorcardiograms were reconstructed and analyzed. RESULTS T vector during AAI pacing changed in both magnitude (baseline, 0.26 +/- 0.10 mV; Day 7, 0.39 +/- 0.13 mV, P < .01) and direction aligning with the paced QRS vector (baseline DDD QRS - AAI T angle 125 degrees +/- 36 degrees; Day 7, 39 degrees +/- 21 degrees, P < .01). During DDD pacing, there was no change in T-vector direction, but T amplitude decreased (baseline, 1.06 +/- 0.32 mV; Day 7, 0.71 +/- 0.26 mV, P < .01). CM measured as T-vector peak displacement (TPD) was identical in AAI and DDD mode (TPD 0.46 +/- .0.17 mV and 0.46 +/- 0.17 mV, respectively). Individual CM magnitude correlated with QRS/T-vector amplitude ratio during DDD pacing at baseline (r = 0.90). CONCLUSION CM can be reliably shown during continuous ventricular pacing, expanding its application to situations in which abnormal ventricular activation persists. Its magnitude is determined by the QRS/T-amplitude ratio of the ventricular paced beat.

Comparison of QTinno, a fully automated electrocardiographic analysis program, to semiautomated electrocardiographic analysis methods in a drug safety study in healthy subjects.

BACKGROUND Improved automated methods for electrocardiographic (ECG) analysis are needed, particularly for drug development purposes. OBJECTIVES This study compared a novel fully automated method for ECG analysis (QTinno; NewCardio, Santa Clara, CA) to 2 semiautomated digital methods: global measurement from the earliest QRS onset to the latest T-wave offset on representative superimposed beats (global) and tangent measurement on 3 consecutive beats in one lead (tangent). METHODS All 3 methods were used to determine uncorrected and rate-corrected QT interval duration (QT and QTcF) and related metrics in 1422 digital 12-lead ECGs from a phase 1 drug study. Global and tangent annotations were manually adjusted by the same 3 cardiologists wherever necessary. No adjustments were made in QTinno determinations. RESULTS QTinno returned QTcF change from time-matched baseline (DeltaQTcF) that differed minimally from both global and tangent methods (mean pairwise difference: 0.1 millisecond between QTinno and global, 1.1 milliseconds between QTinno and tangent). The average absolute QT and QTcF intervals by QTinno were approximately 5 milliseconds longer than global and 25 milliseconds longer than by tangent. QTinno had lower intrinsic variability for DeltaQTcF than either global or tangent (between-subject SD: QTinno 4.0 milliseconds, global 5.6 milliseconds, tangent 6.4 milliseconds; within-subject SD: QTinno 4.8 milliseconds, global 7.4 milliseconds, tangent 10.6 milliseconds). All methods were robust in detecting the largest placebo-adjusted mean time-matched DeltaQTcF (15-25 milliseconds) induced by study drug. CONCLUSIONS The methods show good agreement for drug-induced QTc prolongation. Lower intrinsic variability of DeltaQTcF by QTinno could facilitate smaller sample sizes or increase study power in thorough QTc studies.

Vectorcardiographic and electrocardiographic criteria to distinguish new and old left bundle branch block

BACKGROUND There are no established criteria to differentiate new from old left bundle branch block (LBBB). This complicates management of patients with LBBB and suspected acute coronary syndrome. OBJECTIVES The purpose of this study was to develop electrocardiographic (ECG) criteria to differentiate new and old LBBB. METHODS All LBBB tracings (n = 3,706) in a hospital ECG database were retrieved. New (<24 hours, n = 39) and old (>24 hours, n = 1,760) LBBB tracings were identified. QRS and T-wave amplitudes, directions, and durations were measured digitally. Vectorcardiograms were reconstructed from 12-lead ECGs using inverse Dower transform and analyzed with Cardio3KG software. Receiver operator characteristic (ROC) curves were used to develop decision rules to distinguish new and old LBBB. RESULTS The new LBBB group had larger T-vector magnitude (1.20 +/- 0.07 vs. 0.71 +/- 0.01 mV), smaller QRS vector magnitude (2.13 +/- 0.12 vs. 2.47 +/- 0.02 mV), and a lower QRS/T vector magnitude ratio (QRS/T; 1.79 +/- 0.03 vs. 3.92 +/- 0.04) compared with the old LBBB group (mean +/- standard error of the mean, P <.001). The ratio of deepest S to largest T wave in precordial leads (Max S/T) was significantly smaller in the new compared with in the old LBBB group (1.66 +/- 0.05 vs. 3.54 +/- 0.08; P <.001). A decision rule using QRS/T <2.25 and Max S/T <2.5 had 100% sensitivity and 96%-68% specificity in diagnosing new LBBB, including subsets of patients with tachycardia and ischemia. CONCLUSIONS QRS/T and Max S/T allow accurate discrimination between new and old LBBB suitable for both computerized and manual analysis. If confirmed in prospective studies, this finding can improve management of patients with chest pain and LBBB.

Visual 3Dx: Algorithms for quantitative 3-dimensional analysis of ECG signals

Introduction: The 12-lead ECG is useful for cardiac diagnosis but has limited sensitivity and specificity. To address this, we developed the Visual3Dx, a comprehensive method for describing cardiac electrical activity in time and space. The Visual3Dx transforms the ECG input into a time-variable heart vector, and normalizes each lead input to assure equal representation from all cardiac regions. Methods: We compared the Visual3Dx to the standard 12-lead ECG for detection of acute myocardial ischemia (AMI) in 2 clinical models. Model 1 was AMI after 90 s of balloon coronary occlusion in 117 cases. Model 2 was 122 consecutive patients who: (1) presented to an urban emergency department with chest pain; (2) were admitted to coronary care and developed elevated cardiac troponin levels; and (3) had coronary arteriography within 6 hrs. Results: In Model 1, the 12 lead ECG developed ST segment deviation diagnostic of AMI in 78/117 occlusions (67%), whereas using the same input ECG data, the Visual3Dx was diagnostic of AMI in 105/117 occlusions (90%; p<0.001). In Model 2, the first 12 lead ECG was diagnostic of AMI in 80/122 (66%), whereas the Visual3Dx was diagnostic in 103/122 (84%). In both Models, the largest sensitivity gains were seen in left circumflex and right coronary artery occlusions. Conclusions: The Visual3Dx is a promising tool for 3D quantitative analysis of cardiac electrical activity that may improve diagnosis of AMI, especially in electrically remote regions of the heart. Additional studies will define diagnostic specificity and further improve 3D biomarkers of AMI.

Computerized Extraction of Electrocardiograms From Continuous 12‐Lead Holter Recordings Reduces Measurement Variability in a Thorough QT Study

Continuous Holter recordings are often used in thorough QT studies (TQTS), with multiple 10‐second electrocardiograms (ECGs) visually selected around predesignated time points. The authors hypothesized that computer‐automated ECG selection would reduce within‐subject variability, improve study data precision, and increase study power. Using the moxifloxacin and placebo arms of a Holter‐based crossover TQTS, the authors compared interval duration measurements (IDMs) from manually selected to computer‐selected ECGs. All IDMs were made with a fully automated computer algorithm. Moxifloxacin‐induced changes in baseline‐ and placebo‐subtracted QT intervals were similar for manual and computer ECG selection. Mean 90% confidence intervals were narrower, and within‐subject variability by mixed‐model covariance was lower for computer‐selected than for manual‐selected ECGs. Computer ECG selection reduced the number of subjects needed to achieve 80% power by 40% to 50% over manual. Computer ECG selection returns accurate ddQTcF values with less measurement variability than manual ECG selection by a variety of metrics. This results in increased study power and reduces the number of subjects needed to achieve desired power, which represents a significant potential source cost savings in clinical drug trials.