RIPPLE-VT study: Multicenter prospective evaluation of ventricular tachycardia substrate ablation by targeting scar channels to eliminate latest scar potentials without direct ablation.

BACKGROUND: Recurrent ventricular tachycardia (VT) can be treated by substrate modification of the myocardial scar by catheter ablation during sinus rhythm without VT induction. Better defining this arrhythmic substrate could help improve outcome and reduce ablation burden. OBJECTIVE: The study aimed to limit ablation within postinfarction scar to conduction channels within the scar to reduce VT recurrence. METHODS: Patients undergoing catheter ablation for recurrent implantable cardioverter-defibrillator therapy for postinfarction VT were recruited at 5 centers. Left ventricular maps were collected on CARTO using a Pentaray catheter. Ripple mapping was used to categorize infarct scar potentials (SPs) by timing. Earliest SPs were ablated sequentially until there was loss of the terminal SPs without their direct ablation. The primary outcome measure was sustained VT episodes as documented by device interrogations at 1 year, which was compared with VT episodes in the year before ablation. RESULTS: The study recruited 50 patients (mean left ventricular ejection fraction, 33% ± 9%), and 37 patients (74%) met the channel ablation end point with successful loss of latest SPs without direct ablation. There were 16 recurrences during 1-year follow-up. There was a 90% reduction in VT burden from 30.2 ± 53.9 to 3.1 ± 7.5 (P < .01) per patient, with a concomitant 88% reduction in appropriate shocks from 2.1 ± 2.7 to 0.2 ± 0.9 (P < .01). There were 8 deaths during follow-up. Those who met the channel ablation end point had no significant difference in mortality, recurrence, or VT burden but had a significantly lower ablation burden of 25.7 ± 4.2 minutes vs 39.9 ± 6.1 minutes (P = .001). CONCLUSION: Scar channel ablation is feasible by ripple mapping and can be an alternative to more extensive substrate modification techniques.

Bariatric surgery partially reverses subclinical proarrhythmic structural, electrophysiological and autonomic changes in obesity.

BACKGROUND: Obesity confers higher risks of cardiac arrhythmias. The extent to which weight loss reverses subclinical proarrhythmic adaptations in arrhythmia-free obese individuals is unknown. OBJECTIVE: To study structural, electrophysiological and autonomic remodelling in arrhythmia-free obese patients, and their reversibility with bariatric surgery using electrocardiographic imaging (ECGi). METHODS: Sixteen arrhythmia-free obese patients (43+12years, 13 female, BMI 46.7+5.5kg/m2) had ECGi pre-bariatric surgery (PreSurg), of which twelve had ECGi post-surgery (PostSurg, 36.8+6.5kg/m2). Sixteen age- and sex-matched lean healthy individuals (42+11 years, BMI 22.8+2.6kg/m2) acted as controls and had ECGi once. RESULTS: Obesity was associated with structural (increased epicardial fat volumes and left ventricular mass), autonomic (blunted heart rate variability) and electrophysiological (slower atrial conduction and steeper ventricular repolarisation gradients) remodelling. Following bariatric surgery, there was partial structural reverse remodelling, with a reduction in epicardial fat volumes (68.7cm3 vs 64.5cm3, p=0.0010) and left ventricular mass (33g/m2.7 vs 25g/m2.7, p<0.0005). There was also partial electrophysiological reverse remodelling with a reduction in mean spatial ventricular repolarisation gradients (26mm/ms vs 19mm/ms, p=0.0009), although atrial activation remained prolonged. Heart rate variability, quantified by standard deviation of successive differences of RR intervals, was also partially improved following bariatric surgery (18.7ms vs 25.9ms, p=0.017). Computational modelling showed PreSurg obese hearts had a greater window of vulnerability to unidirectional block and had earlier spiral-wave break-up with more complex re-entry patterns than PostSurg counterparts. CONCLUSION: Obesity is associated with adverse electrophysiological, structural and autonomic remodelling that is partially reversed after bariatric surgery. These data have important implications for bariatric surgery weight thresholds and weight loss strategies.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Factors associated with non-fatal heart failure and atrial fibrillation or flutter within the first 30 days post COPD exacerbation: a nested case-control study.

BACKGROUND: An immediate, temporal risk of heart failure and arrhythmias after a Chronic Obstructive Pulmonary Disease (COPD) exacerbation has been demonstrated, particularly in the first month post-exacerbation. However, the clinical profile of patients who develop heart failure (HF) or atrial fibrillation/flutter (AF) following exacerbation is unclear. Therefore we examined factors associated with people being hospitalized for HF or AF, respectively, following a COPD exacerbation. METHODS: We conducted two nested case-control studies, using primary care electronic healthcare records from the Clinical Practice Research Datalink Aurum linked to Hospital Episode Statistics, Office for National Statistics for mortality, and socioeconomic data (2014-2020). Cases had hospitalization for HF or AF within 30 days of a COPD exacerbation, with controls matched by GP practice (HF 2:1;AF 3:1). We used conditional logistic regression to explore demographic and clinical factors associated with HF and AF hospitalization. RESULTS: Odds of HF hospitalization (1,569 cases, 3,138 controls) increased with age, type II diabetes, obesity, HF and arrhythmia history, exacerbation severity (hospitalization), most cardiovascular medications, GOLD airflow obstruction, MRC dyspnea score, and chronic kidney disease. Strongest associations were for severe exacerbations (adjusted odds ratio (aOR)=6.25, 95%CI 5.10-7.66), prior HF (aOR=2.57, 95%CI 1.73-3.83), age≥80 years (aOR=2.41, 95%CI 1.88-3.09), and prior diuretics prescription (aOR=2.81, 95%CI 2.29-3.45). Odds of AF hospitalization (841 cases, 2,523 controls) increased with age, male sex, severe exacerbation, arrhythmia and pulmonary hypertension history and most cardiovascular medications. Strongest associations were for severe exacerbations (aOR=5.78, 95%CI 4.45-7.50), age≥80 years (aOR=3.15, 95%CI 2.26-4.40), arrhythmia (aOR=3.55, 95%CI 2.53-4.98), pulmonary hypertension (aOR=3.05, 95%CI 1.21-7.68), and prescription of anticoagulants (aOR=3.81, 95%CI 2.57-5.64), positive inotropes (aOR=2.29, 95%CI 1.41-3.74) and anti-arrhythmic drugs (aOR=2.14, 95%CI 1.10-4.15). CONCLUSIONS: Cardiopulmonary factors were associated with hospitalization for HF in the 30 days following a COPD exacerbation, while only cardiovascular-related factors and exacerbation severity were associated with AF hospitalization. Understanding factors will help target people for prevention.

The Spatial Ventricular Gradient Is Associated With Inducibility of Ventricular Arrhythmias During Electrophysiology Study.

BACKGROUND: Myocardial electrical heterogeneity is critical for normal cardiac electromechanical function, but abnormal or excessive electrical heterogeneity is proarrhythmic. The spatial ventricular gradient (SVG), a vectorcardiographic measure of electrical heterogeneity, has been associated with arrhythmic events during long-term follow-up, but its relationship with short-term inducibility of ventricular arrhythmias (VAs) is unclear. OBJECTIVE: This study was designed to determine associations between SVG and inducible VAs during electrophysiology study. METHODS: A retrospective study was conducted of adults without prior sustained VA, cardiac arrest, or implantable cardioverter-defibrillator who underwent ventricular stimulation for evaluation of syncope and nonsustained ventricular tachycardia or for risk stratification before primary prevention implantable cardioverter-defibrillator implantation. The 12-lead electrocardiograms were converted into vectorcardiograms, and SVG magnitude (SVGmag) and direction (azimuth and elevation) were calculated. Odds of inducible VA were regressed by logistic models. RESULTS: Of 143 patients (median age, 69 years; 80% male; median left ventricular ejection fraction [LVEF], 47%; 52% myocardial infarction), 34 (23.8%) had inducible VAs. Inducible patients had lower median LVEF (38% vs 50%; P < .0001), smaller SVGmag (29.5 vs 39.4 mV·ms; P = .0099), and smaller cosine SVG azimuth (cosSVGaz; 0.64 vs 0.89; P = .0007). When LVEF, SVGmag, and cosSVGaz were dichotomized at their medians, there was a 39-fold increase in adjusted odds (P = .002) between patients with all low LVEF, SVGmag, and cosSVGaz (65% inducible) compared with patients with all high LVEF, SVGmag, and cosSVGaz (4% [n = 1] inducible). After multivariable adjustment, SVGmag, cosSVGaz, and sex but not LVEF or other characteristics remained associated with inducible VAs. CONCLUSION: Assessment of electrical heterogeneity by SVG, which reflects abnormal electrophysiologic substrate, adds to LVEF and identifies patients at high and low risk of inducible VA at electrophysiology study.

Therapeutic potential of conduction system pacing as a method for improving cardiac output during ventricular tachycardia.

BACKGROUND: Ventricular tachycardia (VT) reduces cardiac output through high heart rates, loss of atrioventricular synchrony, and loss of ventricular synchrony. We studied the contribution of each mechanism and explored the potential therapeutic utility of His bundle pacing to improve cardiac output during VT. METHODS: Study 1 aimed to improve the understanding of mechanisms of harm during VT (using pacing simulated VT). In 23 patients with left ventricular impairment, we recorded continuous ECG and beat-by-beat blood pressure measurements. We assessed the hemodynamic impact of heart rate and restoration of atrial and biventricular synchrony. Study 2 investigated novel pacing interventions during clinical VT by evaluating the hemodynamic effects of His bundle pacing at 5 bpm above the VT rate in 10 patients. RESULTS: In Study 1, at progressively higher rates of simulated VT, systolic blood pressure declined: at rates of 125, 160, and 190 bpm, -22.2%, -42.0%, and -58.7%, respectively (ANOVA p < 0.0001). Restoring atrial synchrony alone had only a modest beneficial effect on systolic blood pressure (+ 3.6% at 160 bpm, p = 0.2117), restoring biventricular synchrony alone had a greater effect (+ 9.1% at 160 bpm, p = 0.242), and simultaneously restoring both significantly increased systolic blood pressure (+ 31.6% at 160 bpm, p = 0.0003). In Study 2, the mean rate of clinical VT was 143 ± 21 bpm. His bundle pacing increased systolic blood pressure by + 14.2% (p = 0.0023). In 6 of 10 patients, VT terminated with His bundle pacing. CONCLUSIONS: Restoring atrial and biventricular synchrony improved hemodynamic function in simulated and clinical VT. Conduction system pacing could improve VT tolerability and treatment.

Randomized Trial of Remote Assessment of Patients After an Acute Coronary Syndrome.

BACKGROUND: Telemedicine programs can provide remote diagnostic information to aid clinical decisions that could optimize care and reduce unplanned readmissions post-acute coronary syndrome (ACS). OBJECTIVES: TELE-ACS (Remote Acute Assessment of Patients With High Cardiovascular Risk Post-Acute Coronary Syndrome) is a randomized controlled trial that aims to compare a telemedicine-based approach vs standard care in patients following ACS. METHODS: Patients were suitable for inclusion with at least 1 cardiovascular risk factor and presenting with ACS and were randomized (1:1) before discharge. The primary outcome was time to first readmission at 6 months. Secondary outcomes included emergency department (ED) visits, major adverse cardiovascular events, and patient-reported symptoms. The primary analysis was performed according to intention to treat. RESULTS: A total of 337 patients were randomized from January 2022 to April 2023, with a 3.6% drop-out rate. The mean age was 58.1 years. There was a reduced rate of readmission over 6 months (HR: 0.24; 95% CI: 0.13-0.44; P < 0.001) and ED attendance (HR: 0.59; 95% CI: 0.40-0.89) in the telemedicine arm, and fewer unplanned coronary revascularizations (3% in telemedicine arm vs 9% in standard therapy arm). The occurrence of chest pain (9% vs 24%), breathlessness (21% vs 39%), and dizziness (6% vs 18%) at 6 months was lower in the telemedicine group. CONCLUSIONS: The TELE-ACS study has shown that a telemedicine-based approach for the management of patients following ACS was associated with a reduction in hospital readmission, ED visits, unplanned coronary revascularization, and patient-reported symptoms. (Telemedicine in High-Risk Cardiovascular Patients Post-ACS [TELE-ACS]; NCT05015634).

Sinus rhythm activation signature indicates reentrant ventricular tachycardia inducibility and approximate isthmus location.

BACKGROUND: Sinus rhythm activation time is useful to assess infarct border zone substrate. OBJECTIVE: We sought to further investigate sinus activation in ventricular tachycardia (VT). METHODS: Canine postinfarction data were analyzed retrospectively. In each experiment, an infarct was created in the left ventricular wall by left anterior descending coronary artery ligation. At 3 to 5 days after ligation, 196-312 bipolar electrograms were recorded from the anterior left ventricular epicardium overlapping the infarct border zone. Sustained monomorphic VT was induced by premature electrical stimulation in 50 experiments and was noninducible in 43 experiments. Acquired sinus rhythm and VT electrograms were marked for electrical activation time, and activation maps of representative sinus rhythm and VT cycles were constructed. The sinus rhythm activation signature was defined as the cumulative number of multielectrode recording sites that had activated per time epoch, and its derivative was used to predict VT inducibility and to define the sinus rhythm slow/late activation sequence. RESULTS: Plotting mean activation signature derivative, a best cutoff value was useful to separate experiments with reentrant VT inducibility (sensitivity, 42/50) vs noninducibility (specificity, 39/43), with an accuracy of 81 of 93. For the 50 experiments with inducible VT, recording sites overlying a segment of isochrone encompassing the sinus rhythm slow/late activation sequence spanned the VT isthmus location in 32 cases (64%), partially spanned it in 15 cases (30%), but did not span it in 3 cases (6%). CONCLUSION: The sinus rhythm activation signature derivative is assistive to differentiate substrate supporting reentrant VT inducibility vs noninducibility and to identify slow/late activation for targeting isthmus location.

Physically Meaningful Surrogate Data for COPD.

The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are "data hungry" whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks. To ensure the faithfulness of our domain-aware COPD surrogates, the generated waveforms are examined through both flow waveforms and photoplethysmography (PPG) waveforms (as a proxy for intrathoracic pressure) in terms of duty cycle, sample entropy, FEV1/FVC ratios and flow-volume loops. The proposed simulator operates on healthy subjects and is able to generate FEV1/FVC obstruction ratios ranging from greater than 0.8 to less than 0.2, mirroring values that can observed in the full spectrum of real-world COPD. As a final stage of verification, a simple convolutional neural network is trained on surrogate data alone, and is used to accurately detect COPD in real-world patients. When training solely on surrogate data, and testing on real-world data, a comparison of true positive rate against false positive rate yields an area under the curve of 0.75, compared with 0.63 when training solely on real-world data.

Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization.

BACKGROUND: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward, but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artifact) or further acquisition of intersecting images, but this must take place before the scan finishes. Real-time quality assurance by humans is a complex task requiring training and experience, so being able to identify which images have an intermediate likelihood of LGE while the scan is ongoing, without the presence of an expert is of high value. This decision-support could prompt immediate image optimization or acquisition of supplementary images to confirm or refute the presence of genuine LGE. This could reduce ambiguity in reports. METHODS: Short-axis, phase-sensitive inversion recovery late gadolinium images were extracted from our clinical cardiac magnetic resonance (CMR) database and shuffled. Two, independent, blinded experts scored each individual slice for "LGE likelihood" on a visual analog scale, from 0 (absolute certainty of no LGE) to 100 (absolute certainty of LGE), with 50 representing clinical equipoise. The scored images were split into two classes-either "high certainty" of whether LGE was present or not, or "low certainty." The dataset was split into training, validation, and test sets (70:15:15). A deep learning binary classifier based on the EfficientNetV2 convolutional neural network architecture was trained to distinguish between these categories. Classifier performance on the test set was evaluated by calculating the accuracy, precision, recall, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Performance was also evaluated on an external test set of images from a different center. RESULTS: One thousand six hundred and forty-five images (from 272 patients) were labeled and split at the patient level into training (1151 images), validation (247 images), and test (247 images) sets for the deep learning binary classifier. Of these, 1208 images were "high certainty" (255 for LGE, 953 for no LGE), and 437 were "low certainty". An external test comprising 247 images from 41 patients from another center was also employed. After 100 epochs, the performance on the internal test set was accuracy = 0.94, recall = 0.80, precision = 0.97, F1-score = 0.87, and ROC AUC = 0.94. The classifier also performed robustly on the external test set (accuracy = 0.91, recall = 0.73, precision = 0.93, F1-score = 0.82, and ROC AUC = 0.91). These results were benchmarked against a reference inter-expert accuracy of 0.86. CONCLUSION: Deep learning shows potential to automate quality control of late gadolinium imaging in CMR. The ability to identify short-axis images with intermediate LGE likelihood in real-time may serve as a useful decision-support tool. This approach has the potential to guide immediate further imaging while the patient is still in the scanner, thereby reducing the frequency of recalls and inconclusive reports due to diagnostic indecision.

Entanglement-based quantum digital signatures over a deployed campus network.

The quantum digital signature protocol offers a replacement for most aspects of public-key digital signatures ubiquitous in today's digital world. A major advantage of a quantum-digital-signatures protocol is that it can have information-theoretic security, whereas public-key cryptography cannot. Here we demonstrate and characterize hardware to implement entanglement-based quantum digital signatures over our campus network. Over 25 hours, we collect measurements on our campus network, where we measure sufficiently low quantum bit error rates (<5% in most cases) which in principle enable quantum digital signatures at over 50 km as shown through rigorous simulation accompanied by a noise model developed specifically for our implementation. These results show quantum digital signatures can be successfully employed over deployed fiber. Moreover, our reported method provides great flexibility in the number of users, but with reduced entanglement rate per user. Finally, while the current implementation of our entanglement-based approach has a low signature rate, feasible upgrades would significantly increase the signature rate.

Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients.

Aims: Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mortality and should be minimized when safe to do so. We hypothesized that machine learning-derived ventricular tachycardia (VT) cycle length (CL) variability metrics could be used to discriminate between sustained and spontaneously terminating VT. Methods and results: In this single-centre retrospective study, we analysed data from 69 VT episodes stored on ICDs from 27 patients (36 spontaneously terminating VT, 33 sustained VT). Several VT CL parameters including heart rate variability metrics were calculated. Additionally, a first order auto-regression model was fitted using the first 10 CLs. Using features derived from the first 10 CLs, a random forest classifier was used to predict VT termination. Sustained VT episodes had more stable CLs. Using data from the first 10 CLs only, there was greater CL variability in the spontaneously terminating episodes (mean of standard deviation of first 10 CLs: 20.1 ± 8.9 vs. 11.5 ± 7.8 ms, P < 0.0001). The auto-regression coefficient was significantly greater in spontaneously terminating episodes (mean auto-regression coefficient 0.39 ± 0.32 vs. 0.14 ± 0.39, P < 0.005). A random forest classifier with six features yielded an accuracy of 0.77 (95% confidence interval 0.67 to 0.87) for prediction of VT termination. Conclusion: Ventricular tachycardia CL variability and instability are associated with spontaneously terminating VT and can be used to predict spontaneous VT termination. Given the harmful effects of unnecessary ICD shocks, this machine learning model could be incorporated into ICD algorithms to defer therapies for episodes of VT that are likely to self-terminate.

Characterization of conduction system activation in the postinfarct ventricle using ripple mapping.

BACKGROUND: Three-dimensional (3D) mapping of the ventricular conduction system is challenging. OBJECTIVE: The purpose of this study was to use ripple mapping to distinguish conduction system activation to that of adjacent myocardium in order to characterize the conduction system in the postinfarct left ventricle (LV). METHODS: High-density mapping (PentaRay, CARTO) was performed during normal rhythm in patients undergoing ventricular tachycardia ablation. Ripple maps were viewed from the end of the P wave to QRS onset in 1-ms increments. Clusters of >3 ripple bars were interrogated for the presence of Purkinje potentials, which were tagged on the 3D geometry. Repeating this process allowed conduction system delineation. RESULTS: Maps were reviewed in 24 patients (mean 3112 ± 613 points). There were 150.9 ± 24.5 Purkinje potentials per map, at the left posterior fascicle (LPF) in 22 patients (92%) and at the left anterior fascicle (LAF) in 15 patients (63%). The LAF was shorter (41.4 vs 68.8 mm; P = .0005) and activated for a shorter duration (40.6 vs 64.9 ms; P = .002) than the LPF. Fourteen of 24 patients had left bundle branch block (LBBB), with 11 of 14 (78%) having Purkinje potential-associated breakout. There were fewer breakouts from the conduction system during LBBB (1.8 vs 3.4; 1.6 ± 0.6; P = .039) and an inverse correlation between breakout sites and QRS duration (P = .0035). CONCLUSION: We applied ripple mapping to present a detailed electroanatomic characterization of the conduction system in the postinfarct LV. Patients with broader QRS had fewer LV breakout sites from the conduction system. However, there was 3D mapping evidence of LV breakout from an intact conduction system in the majority of patients with LBBB.

Role of activation signatures in re-entrant ventricular tachycardia circuits.

INTRODUCTION: Development of a rapid means to verify the ventricular tachycardia (VT) isthmus location from heart surface electrogram recordings would be a helpful tool for the electrophysiologist. METHOD: Myocardial infarction was induced in 22 canines by left anterior descending coronary artery ligation under general anesthesia. After 3-5 days, VT was inducible via programmed electrical stimulation at the anterior left ventricular epicardial surface. Bipolar VT electrograms were acquired from 196 to 312 recording sites using a multielectrode array. Electrograms were marked for activation time, and activation maps were constructed. The activation signal, or signature, is defined as the cumulative number of recording sites that have activated per millisecond, and it was utilized to segment each circuit into inner and outer circuit pathways, and as an estimate of best ablation lesion location to prevent VT. RESULTS: VT circuit components were differentiable by activation signals as: inner pathway (mean: 0.30 sites activating/ms) and outer pathway (mean: 2.68 sites activating/ms). These variables were linearly related (p < .001). Activation signal characteristics were dependent in part upon the isthmus exit site. The inner circuit pathway determined by the activation signal overlapped and often extended beyond the activation map isthmus location for each circuit. The best lesion location estimated by the activation signal would likely block an electrical impulse traveling through the isthmus, to prevent VT in all circuits. CONCLUSIONS: The activation signal algorithm, simple to implement for real-time computer display, approximates the VT isthmus location and shape as determined from activation marking, and best ablation lesion location to prevent reinduction.

Temporal Risk of Nonfatal Cardiovascular Events After Chronic Obstructive Pulmonary Disease Exacerbation: A Population-based Study.

Rationale: Cardiovascular events after chronic obstructive pulmonary disease (COPD) exacerbations are recognized. Studies to date have been post hoc analyses of trials, did not differentiate exacerbation severity, included death in the cardiovascular outcome, or had insufficient power to explore individual outcomes temporally.Objectives: We explore temporal relationships between moderate and severe exacerbations and incident, nonfatal hospitalized cardiovascular events in a primary care-derived COPD cohort.Methods: We included people with COPD in England from 2014 to 2020, from the Clinical Practice Research Datalink Aurum primary care database. The index date was the date of first COPD exacerbation or, for those without exacerbations, date upon eligibility. We determined composite and individual cardiovascular events (acute coronary syndrome, arrhythmia, heart failure, ischemic stroke, and pulmonary hypertension) from linked hospital data. Adjusted Cox regression models were used to estimate average and time-stratified adjusted hazard ratios (aHRs).Measurements and Main Results: Among 213,466 patients, 146,448 (68.6%) had any exacerbation; 119,124 (55.8%) had moderate exacerbations, and 27,324 (12.8%) had severe exacerbations. A total of 40,773 cardiovascular events were recorded. There was an immediate period of cardiovascular relative rate after any exacerbation (1-14 d; aHR, 3.19 [95% confidence interval (CI), 2.71-3.76]), followed by progressively declining yet maintained effects, elevated after one year (aHR, 1.84 [95% CI, 1.78-1.91]). Hazard ratios were highest 1-14 days after severe exacerbations (aHR, 14.5 [95% CI, 12.2-17.3]) but highest 14-30 days after moderate exacerbations (aHR, 1.94 [95% CI, 1.63-2.31]). Cardiovascular outcomes with the greatest two-week effects after a severe exacerbation were arrhythmia (aHR, 12.7 [95% CI, 10.3-15.7]) and heart failure (aHR, 8.31 [95% CI, 6.79-10.2]).Conclusions: Cardiovascular events after moderate COPD exacerbations occur slightly later than after severe exacerbations; heightened relative rates remain beyond one year irrespective of severity. The period immediately after an exacerbation presents a critical opportunity for clinical intervention and treatment optimization to prevent future cardiovascular events.

Quantum Key Distribution for Critical Infrastructures: Towards Cyber-Physical Security for Hydropower and Dams.

Hydropower facilities are often remotely monitored or controlled from a centralized remote control room. Additionally, major component manufacturers monitor the performance of installed components, increasingly via public communication infrastructures. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote control a facility's control systems, or it may involve sending control commands over a network from a control room to a machine. The content could be encrypted and decrypted using a public key to protect the communicated information. These cryptographic encoding and decoding schemes become vulnerable as more advances are made in computer technologies, such as quantum computing. In contrast, quantum key distribution (QKD) and other quantum cryptographic protocols are not based upon a computational problem, and offer an alternative to symmetric cryptography in some scenarios. Although the underlying mechanism of quantum cryptogrpahic protocols such as QKD ensure that any attempt by an adversary to observe the quantum part of the protocol will result in a detectable signature as an increased error rate, potentially even preventing key generation, it serves as a warning for further investigation. In QKD, when the error rate is low enough and enough photons have been detected, a shared private key can be generated known only to the sender and receiver. We describe how this novel technology and its several modalities could benefit the critical infrastructures of dams or hydropower facilities. The presented discussions may be viewed as a precursor to a quantum cybersecurity roadmap for the identification of relevant threats and mitigation.

Effects of novel Coronavirus (COVID-19) on presentation, management, and outcomes of acute cholecystitis at an academic tertiary care center cholecystitis management during COVID-19.

Background: The COVID-19 pandemic necessitated adjustments to nearly all aspects of healthcare, including surgical care. The effects of these adjustments have not been well studied on acute surgical problems conventionally managed non-electively in large, tertiary care centers. Methods: A retrospective analysis of admitted patients with acute cholecystitis at a US academic tertiary care center was performed. We compared the presentation, management, and 30-day outcomes of patients admitted during a 2-month time period during early COVID, to a pre-COVID control group of admitted cholecystitis patients over a 2-month span. Results: The study cohort captured 24 patients, while the control cohort encompassed 53 patients. A non-significant trend toward non-operative management in the COVID cohort is reported. There was no delay in time-to-surgery or complication rate. No surgically managed patient developed COVID within 30 days of operation. Conclusions: Operative management of acute cholecystitis during the COVID-19 pandemic, with pre-operative testing and personal protective equipment guidelines, remained safe and effective.

Generation and characterization of ultrabroadband polarization-frequency hyperentangled photons.

We generate ultrabroadband photon pairs entangled in both polarization and frequency bins through an all-waveguided Sagnac source covering the entire optical C- and L-bands (1530-1625 nm). We perform comprehensive characterization of high-fidelity states in multiple dense wavelength-division multiplexed channels, achieving full tomography of effective four-qubit systems. Additionally, leveraging the inherent high dimensionality of frequency encoding and our electro-optic measurement approach, we demonstrate the scalability of our system to higher dimensions, reconstructing states in a 36-dimensional Hilbert space consisting of two polarization qubits and two frequency-bin qutrits. Our findings hold potential significance for quantum networking, particularly dense coding and entanglement distillation in wavelength-multiplexed quantum networks.