Citations

Analysis of biomedical signals can yield invaluable information for prognosis, diagnosis, therapy evaluation, risk assessment, and disease prevention which is often recorded as short time series data that challenges existing complexity classification algorithms such as Shannon entropy (SE) and other techniques. The purpose of this study was to improve previously developed multiscale entropy (MSE) technique by incorporating nearest-neighbor moving-average kernel, which can be used for analysis of nonlinear and non-stationary short time series physiological data. The approach was tested for robustness with respect to noise analysis using simulated sinusoidal and ECG waveforms. Feasibility of MSE to discriminate between normal sinus rhythm (NSR) and atrial fibrillation (AF) was tested on a single-lead ECG. In addition, the MSE algorithm was applied to identify pivot points of rotors that were induced in ex vivo isolated rabbit hearts. The improved MSE technique robustly estimated the complexity of the signal compared to that of SE with various noises, discriminated NSR and AF on single-lead ECG, and precisely identified the pivot points of ex vivo rotors by providing better contrast between the rotor core and the peripheral region. The improved MSE technique can provide efficient complexity analysis of variety of nonlinear and nonstationary short-time biomedical signals.

Background: Use of magnetic resonance imaging (MRI) brain is restricted in patients with cardiovascular implantable electronic devices (CIEDs). We proposed to determine whether mortality difference exists for patients with non–MRI-conditional CIEDs undergoing brain MRI compared with controls. Methods and Results: Retrospective analysis of prospectively collected data was performed to compare all-cause mortality in patients with CIEDs undergoing brain MRI (CIED-MRI) with 3 control groups matched for age, sex, imaging year, and type of CIED: (1) no CIED, brain MRI (no–CIED-MRI); (2) CIED, brain computerized tomography (CT) scan (CIED-CT); and (3) no CIED, brain CT (no–CIED-CT). The primary outcome was a significant difference ( P <0.05) between estimated mortality rates. Secondary outcomes were changes in device function before and after brain MRI. The estimated all-cause mortality at 5 years for the CIED-MRI group [mean age (±SD), 68.2 years (15.3 years); 61.2% men] was not significantly different from patients who underwent CT with or without a device (CIED-CT group: hazard ratio, 0.814; 95% confidence interval, 0.593–1.117; P =0.2; no–CIED-CT group: hazard ratio, 1.149; 95% confidence interval, 0.818–1.613; P =0.4). There was a significant increase in mortality between CIED-MRI and no–CIED-MRI groups (hazard ratio, 1.463; 95% confidence interval, 1.019–2.099; P =0.04). Conclusions: Brain MRI in patients with CIEDs does not carry an increased mortality risk compared with brain CT and can be performed with adherence to appropriate procedural protocols.

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.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia affecting approximately 3 million Americans, and is a prognostic marker for stroke, heart failure and even death [1]. 12-lead electrocardiogram (ECG) is used to monitor normal sinus rhythm (NSR) and also detect AF. Although the persistent form of AF can be detected relatively easy, detecting paroxysmal AF is often a challenge since requiring continuous monitoring, which becomes expensive and cumbersome to collect lot of ECG data [1]. Several researchers have attempted to develop new methods to discriminate NSR and AF which are based on R-R interval analysis, linear methods, filtering, spectral analysis, statistical approaches such as entropy etc. which faces limitation of successfully detecting AF of all types with high sensitivity and specificity using short time ECG data [1–3]. The major issues with these approaches is that they often distort the ECG by several pre-processing steps with filters, do not provide reliable discrimination using short ECG time series data and many of them lack real-time capability that makes it difficult to trust the data for diagnosis and treatment. Both clinical and scientific communities recognize these difficulties and the necessity to develop novel methods that can enable accurate monitoring and detection of AF [2]. In addition, robust detection and classification algorithms are essential for delivering appropriate therapy for implantable cardioverter defibrillators (ICD) to provide lifesaving timely action. In this work, the authors propose and demonstrate the application of a multiscale frequency (MSF) approach [4] for accurate detection and discrimination between AF and NSR ECG traces taken from publically available Physionet database. The MSF approach takes into account the contribution from various frequencies in ECG and thus yield valuable information regarding the chaotic nature of AF. Therefore, we demonstrate that MSF can capture the complexity of AF which is associated with higher MSF value compared with NSR thus enabling robust discrimination e AF manifests itself with numerous chaotic frequencies within the body surface ECG,. We validate the feasibility of this technique to discriminate NSR from AF.

Background: Recognition of rates and causes of hard, patient-centered outcomes of death and cerebrovascular events (CVEs) after heart rhythm disorder management (HRDM) procedures is an essential step for the development of quality improvement programs in electrophysiology laboratories. Our primary aim was to assess and characterize death and CVEs (stroke or transient ischemic attack) after HRDM procedures over a 17-year period. Methods: We performed a retrospective cohort study of all patients undergoing HRDM procedures between January 2000 and November 2016 at the Mayo Clinic. Patients from all 3 tertiary academic centers (Rochester, Phoenix, and Jacksonville) were included in the study. All in-hospital deaths and CVEs after HRDM procedures were identified and were further characterized as directly or indirectly related to the HRDM procedure. Subgroup analysis of death and CVE rates was performed for ablation, device implantation, electrophysiology study, lead extraction, and defibrillation threshold testing procedures. Results: A total of 48 913 patients (age, 65.7±6.6 years; 64% male) who underwent a total of 62 065 HRDM procedures were included in the study. The overall mortality and CVE rates in the cohort were 0.36% (95% confidence interval [CI], 0.31–0.42) and 0.12% (95% CI, 0.09–0.16), respectively. Patients undergoing lead extraction had the highest overall mortality rate at 1.9% (95% CI, 1.34–2.61) and CVE rate at 0.62% (95% CI, 0.32–1.07). Among patients undergoing HRDM procedures, 48% of deaths directly related to the HDRM procedure were among patients undergoing device implantation procedures. Overall, cardiac tamponade was the most frequent direct cause of death (40%), and infection was the most common indirect cause of death (29%). The overall 30-day mortality rate was 0.76%, with the highest being in lead extraction procedures (3.08%), followed by device implantation procedures (0.94%). Conclusions: Half of the deaths directly related to an HRDM procedure were among the patients undergoing device implantation procedures, with cardiac tamponade being the most common cause of death. This highlights the importance of the development of protocols for the quick identification and management of cardiac tamponade even in procedures typically believed to be lower risk such as device implantation.

AbstractBackgroundCardiovascular implantable electronic devices (CIEDs) can be life‐saving. However, complications from CIED infection can be life‐threatening, often requiring device removal. Despite publication of CIED infection management guidelines, there remains marked variation in clinical practice.ObjectiveTo better understand and quantify these differences, we conducted a multinational survey of practitioners of CIED management.MethodsAn electronic survey was sent to Heart Rhythm Society members, spanning 70 countries across six continents. All responses were collected anonymously.Results227 out of 3,600 (6.3%) responded to the survey. The majority of surveys were completed by practitioners from the United States (168; 68.3%) and 53.8% of these practiced in academic medical centers. The large majority (92.7%) of sites had protocols to ensure appropriate timing of prophylactic antibiotics. Superficial (incisional) site infections were treated with antibiotics alone 52.5% of the time (consistent with guidelines); in contrast, deep pocket infections were treated with antibiotics (with device removal) in accordance to guidelines only 37.4% of the time. Almost all providers (98.7%) were inclined to perform complete hardware removal in cases of CIED‐related endocarditis. In contrast, 82.2% of survey participants suggested complete CIED system removal in patients with an occult Gram‐positive bacteremia, 65.5% with occult Gram‐negative bacteremia, and 59.3% with prolonged bacteremia due to a source other than CIED.ConclusionsThese data suggest wide variability in clinical practice in managing CIED infection with significant deviations from published guidelines. There is critical need to increase awareness and develop institutional protocols to ensure adherence with evidence‐based guidelines to optimize outcomes.

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.