Friedman, Paul A.

AbstractBackgroundCulture‐negative (CN) cardiovascular implantable electronic device (CIED) infections represent a significant management challenge for clinicians with no specific guidelines addressing this subgroup of patients. The aim of the current investigation is to report our institutional experience of CN CIED infections and propose a systematic approach to diagnostic evaluation and management of these complicated cases based on our observations.MethodsWe retrospectively screened all CIED infection cases at Mayo Clinic from 2005 through 2017. Using standardized criteria to define significant microbial growth, all patients with positive blood or pocket/device cultures were excluded.ResultsA total of 835 cases of CIED infection were screened, and of these, 47 (6%) met CN‐CIED infection criteria. Majority of patients (77%) in this cohort had received antimicrobial therapy prior to device cultures with a median duration of 8 days. The most common presentation was device pocket infection (81%). All patients underwent device removal. Route of antibiotics was switched from oral to parenteral and spectrum of activity expanded from initial therapy in 23% of patients despite negative cultures. Majority of patients (80%) were dismissed on parenteral therapy. Adverse events attributed to intravenous antibiotic therapy were documented in 63% of the cases. No recurrence was reported and 6‐month survival was 94.8%.ConclusionsPocket and device cultures in suspected CIED infections may be negative due to preextraction oral antibiotics. However, frequently these patients are managed with broad‐spectrum parenteral therapy postextraction.

AbstractBackgroundQuadripolar left ventricular (LV) leads provide a more optimal pacing configuration for cardiac resynchronization therapy (CRT) than conventional bipolar leads. The objective of this study is to determine the clinical outcomes of CRT using quadripolar leads.MethodsThis study included 516 CRT patients who received bipolar LV leads (n = 278) or quadripolar LV leads (n = 238) from January 2013 to June 2016. Data were retrospectively collected from a prospective CRT database and electronic health records. CRT response to CRT was defined as >5% improvement in the LV ejection fraction (LVEF) from baseline. Baseline characteristics and outcomes were compared between groups.ResultsNew York Heart Association Functional Classification and LVEF significantly improved in the quadripolar and bipolar groups after CRT. There was no difference in the all‐cause mortality rate. The implant success rate was significantly higher in the quadripolar group (100% vs 97.8%; P = 0.02). Quadripolar lead placement was an independent predictor of CRT response at 12 months (hazard ratio, 0.76; 95% confidence interval, 0.58–0.98; P = 0.04). The rate of LV lead–related complications requiring invasive lead revision or abandonment was significantly higher in the bipolar group (11.2% vs 4.6%; P = 0.007).ConclusionsQuadripolar leads achieve similar CRT outcomes as bipolar LV leads but with a higher implant success rate and fewer procedure‐related complications.

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.