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/excessive electrical heterogeneity is proarrhythmic. The spatial ventricular gradient (SVG), a vectorcardiographic measure of electrical heterogeneity, has been associated with arrhythmic events over long-term follow-up, but its relationship with short-term inducibility of ventricular arrhythmias (VAs) is unclear. OBJECTIVE: Determine associations between SVG and inducible VAs during electrophysiology study (EPS). METHODS: Retrospective study of adults without prior sustained VA, cardiac arrest, or implantable cardioverter-defibrillator (ICD), who underwent ventricular stimulation for evaluation of syncope, non-sustained VT, and/or risk-stratification prior to primary prevention ICD implantation. 12-lead ECGs were converted into vectorcardiograms and SVG magnitude (SVGmag) and direction (azimuth and elevation) were calculated. Odds of inducible VA were regressed using logistic models. RESULTS: Among 143 patients (median age 66, 80% male, median LVEF 47%, 52% myocardial infarction), 34 (23.8%) had inducible VAs. Inducible patients had lower median LVEF (38 vs 50%, p<0.0001), smaller SVGmag (29.5 vs 39.4mV*ms, p=0.0099), and smaller cosine SVG azimuth (cosSVGaz) (0.64 vs 0.89, p=0.0007). When LVEF, SVGmag, and cosSVGaz were dichotomized at their medians, there was a 39-fold increase in adjusted odds (p=0.002) between patients with all low LVEF, SVGmag, and cosSVGaz (65% inducible), compared to 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 via SVG, which reflects abnormal electrophysiological substrate, adds to LVEF and identifies patients at high and low risk of inducible VA at EPS.

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.

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: To further investigate sinus activation in ventricular tachycardia (VT). METHODS: Canine postinfarction data was analyzed retrospectively. In each experiment, an infarct was created in the left ventricular wall by LAD coronary artery ligation. Three-to-five days following ligation, 196-312 bipolar electrograms were recorded from the anterior left ventricular epicardium overlapping the infarct border zone. Sustained monomorphic VT was induced via premature electrical stimulation in 50 experiments and was non-inducible 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) versus non-inducibility (specificity: 39/43), with an accuracy of 81/93. 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 in 3 cases (6%). CONCLUSION: The sinus rhythm activation signature derivative is assistive to differentiate substrate supporting reentrant VT inducibility versus non-inducibility, and to identify slow/late activation for targeting isthmus location.

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.

Remote Acute Assessment of Cardiac Patients Post-Acute Coronary Syndrome (TELE-ACS): A Randomized Controlled Trial.

BACKGROUND: Telemedicine programmes can provide remote diagnostic information to aid clinical decision that could optimize care and reduce unplanned re-admissions post ACS. OBJECTIVES: TELE-ACS is a randomized controlled trial which aims to compare a telemedicine-based approach versus standard care in patients following ACS. METHODS: Patients were suitable for inclusion with at least one cardiovascular risk factor and presenting with ACS and were randomized (1:1) prior to 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. The trial was registered on ClinicalTrial.gov (NCT05015634). RESULTS: 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 (hazard ratio [HR] 0.24; 95% confidence interval [CI] 0.13 to 0.44; p < 0.001) and ED attendance (HR 0.59; 95% CI 0.59; 95% CI 0.40 to 0.89) in the telemedicine arm, and fewer unplanned coronary revascularizations (3% in telemedicine arm versus 9% in standard therapy arm). The occurrence of chest pain (9% versus 24%), breathlessness (21% versus 39%) and dizziness (6% versus 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.

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 MRI images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimisation.

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 artefact) 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 optimisation 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 (PSIR) late gadolinium images were extracted from our clinical CMR database and shuffled. Two, independent, blinded experts scored each individual slice for 'LGE likelihood' on a visual analogue 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 2 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 centre. RESULTS: 1645 images (from 272 patients) were labelled 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 centre was also employed. After 100 epochs the performance on the internal test set was: accuracy = 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 = 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 86%. CONCLUSIONS: 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.

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.

Left bundle branch pacing with and without anodal capture: impact on ventricular activation pattern and acute haemodynamics.

AIMS: Left bundle branch pacing (LBBP) can deliver physiological left ventricular activation, but typically at the cost of delayed right ventricular (RV) activation. Right ventricular activation can be advanced through anodal capture, but there is uncertainty regarding the mechanism by which this is achieved, and it is not known whether this produces haemodynamic benefit. METHODS AND RESULTS: We recruited patients with LBBP leads in whom anodal capture eliminated the terminal R-wave in lead V1. Ventricular activation pattern, timing, and high-precision acute haemodynamic response were studied during LBBP with and without anodal capture. We recruited 21 patients with a mean age of 67 years, of whom 14 were males. We measured electrocardiogram timings and haemodynamics in all patients, and in 16, we also performed non-invasive mapping. Ventricular epicardial propagation maps demonstrated that RV septal myocardial capture, rather than right bundle capture, was the mechanism for earlier RV activation. With anodal capture, QRS duration and total ventricular activation times were shorter (116 ± 12 vs. 129 ± 14 ms, P < 0.01 and 83 ± 18 vs. 90 ± 15 ms, P = 0.01). This required higher outputs (3.6 ± 1.9 vs. 0.6 ± 0.2 V, P < 0.01) but without additional haemodynamic benefit (mean difference -0.2 ± 3.8 mmHg compared with pacing without anodal capture, P = 0.2). CONCLUSION: Left bundle branch pacing with anodal capture advances RV activation by stimulating the RV septal myocardium. However, this requires higher outputs and does not improve acute haemodynamics. Aiming for anodal capture may therefore not be necessary.

Limits of the spatial ventricular gradient and QRST angles in patients with normal electrocardiograms and no known cardiovascular disease stratified by age, sex, and race.

INTRODUCTION: Measurement of the spatial ventricular gradient (SVG), spatial QRST angles, and other vectorcardiographic measures of myocardial electrical heterogeneity have emerged as novel risk stratification methods for sudden cardiac death and other adverse cardiovascular events. Prior studies of normal limits of these measurements included primarily young, healthy, White volunteers, but normal limits in older patients are unknown. The influence of race and body mass index (BMI) on these measurements is also unclear. METHODS: Normal 12-lead electrocardiograms (ECGs) from a single center were identified. Patients with abnormal cardiovascular, pulmonary, or renal history (assessed by International Classification of Disease [ICD-9/ICD-10] codes) or abnormal cardiovascular imaging were excluded. The SVG and QRST angles were measured and stratified by age, sex, and race. Multivariable linear regression was used to assess the influence of age, BMI, and heart rate (HR) on these measurements. RESULTS: Among 3292 patients, observed ranges of SVG and QRST angles (peak and mean) differed significantly based on sex, age, and race. Sex differences attenuated with increasing age. Men tended to have larger SVG magnitude (60.4 [46.1-77.8] vs. 52.5 [41.3-65.8] mv*ms, p < .0001) and elevation, and more anterior/negative SVG azimuth (-14.8 [-25.1 to -4.3] vs. 1.3 [-9.8 to 10.5] deg, p < .0001) compared to women. Men also had wider QRST angles. Observed ranges varied significantly with BMI and HR. SVG and QRST angle measurements were robust to different filtering bandwidths and moderate fiducial point annotation errors, but were heavily affected by changes in baseline correction. CONCLUSIONS: Age, sex, race, BMI, and HR significantly affect the range of SVG and QRST angles in patients with normal ECGs and no known cardiovascular disease, and should be accounted for in future studies. An online calculator for prediction of these "normal limits" given demographics is provided at https://bivectors.github.io/gehcalc/.

Temporal Trends and Lesion Sets for Persistent Atrial Fibrillation Ablation: A Meta-Analysis With Trial Sequential Analysis and Meta-Regression.

BACKGROUND: Ablation for persistent atrial fibrillation (PsAF) has been performed for over 20 years, although success rates have remained modest. Several adjunctive lesion sets have been studied but none have become standard of practice. We sought to describe how the efficacy of ablation for PsAF has evolved in this time period with a focus on the effect of adjunctive ablation strategies. METHODS: Databases were searched for prospective studies of PsAF ablation. We performed meta-regression and trial sequential analysis. RESULTS: A total of 99 studies (15 424 patients) were included. Ablation for PsAF achieved the primary outcome (freedom of atrial fibrillation/atrial tachycardia rate at 12 months follow-up) in 48.2% (5% CI, 44.0-52.3). Meta-regression showed freedom from atrial arrhythmia at 12 months has improved over time, while procedure time and fluoroscopy time have significantly reduced. Through the use of cumulative meta-analyses and trial sequential analysis, we show that some ablation strategies may initially seem promising, but after several randomized controlled trials may be found to be ineffective. Trial sequential analysis showed that complex fractionated atrial electrogram ablation is ineffective and further study of this treatment would be futile, while posterior wall isolation currently does not have sufficient evidence for routine use in PsAF ablation. CONCLUSIONS: Overall success rates from PsAF ablation and procedure/fluoroscopy times have improved over time. However, no adjunctive lesion set, in addition to pulmonary vein isolation, has been conclusively demonstrated to be beneficial. Through the use of trial sequential analysis, we highlight the importance of adequately powered randomized controlled trials, to avoid reaching premature conclusions, before widespread adoption of novel therapies.

Correlation relationships of the reentrant ventricular tachycardia circuit.

INTRODUCTION: A quantitative analysis of the components of reentrant ventricular tachycardia (VT) circuitry could improve understanding of its onset and perpetuation. METHOD: In 19 canine experiments, the left anterior descending coronary artery was ligated to generate a subepicardial infarct. The border zone resided at the epicardial surface of the anterior left ventricle and was mapped 3-5 days postinfarction with a 196-312 bipolar multielectrode array. Monomorphic VT was inducible by extrastimulation. Activation maps revealed an epicardial double-loop reentrant circuit and isthmus, causing VT. Several circuit parameters were analyzed: the coupling interval for VT induction, VT cycle length, the lateral isthmus boundary (LIB) lengths, and isthmus width and angle. RESULTS: The extrastimulus interval for VT induction and the VT cycle length were strongly correlated (p < 0.001). Both the extrastimulus interval and VT cycle length were correlated to the shortest LIB (p < 0.005). A derivation was developed to suggest that when conduction block at the shorter LIB is functional, the VT cycle length may depend on the local refractory period and the delay from wavefront pivot around the LIB. Isthmus width and angle were uncorrelated to other parameters. CONCLUSIONS: The shorter LIB is correlated to VT cycle length, hence its circuit loop may drive reentrant VT. The extrastimulation interval, VT cycle length, and shorter LIB are intertwined, and may depend upon the local refractory period. Isthmus width and angle are less correlated, perhaps being more related to electrical discontinuity caused by alterations in infarct shape at depth.

Septal scar as a barrier to left bundle branch area pacing.

BACKGROUND: The use of left bundle branch area pacing (LBBAP) for bradycardia pacing and cardiac resynchronization is increasing, but implants are not always successful. We prospectively studied consecutive patients to determine whether septal scar contributes to implant failure. METHODS: Patients scheduled for bradycardia pacing or cardiac resynchronization therapy were prospectively enrolled. Recruited patients underwent preprocedural scar assessment by cardiac MRI with late gadolinium enhancement imaging. LBBAP was attempted using a lumenless lead (Medtronic 3830) via a transeptal approach. RESULTS: Thirty-five patients were recruited: 29 male, mean age 68 years, 10 ischemic, and 16 non-ischemic cardiomyopathy. Pacing indication was bradycardia in 26% and cardiac resynchronization in 74%. The lead was successfully deployed to the left ventricular septum in 30/35 (86%) and unsuccessful in the remaining 5/35 (14%). Septal late gadolinium enhancement was significantly less extensive in patients where left septal lead deployment was successful, compared those where it was unsuccessful (median 8%, IQR 2%-18% vs. median 54%, IQR 53%-57%, p < .001). CONCLUSIONS: The presence of septal scar appears to make it more challenging to deploy a lead to the left ventricular septum via the transeptal route. Additional implant tools or alternative approaches may be required in patients with extensive septal scar.

Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.

Objective: To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases. Materials and Methods: We developed methods to generate drug codelists and tested this using the Clinical Practice Research Datalink (CPRD) Aurum database, accounting for missing data in the database. We generated codelists for: (1) cardiovascular disease and (2) inhaled Chronic Obstructive Pulmonary Disease (COPD) therapies, applying them to a sample cohort of 335 931 COPD patients. We compared searching all drug dictionary variables (A) against searching only (B) chemical or (C) ontological variables. Results: In Search A, we identified 165 150 patients prescribed cardiovascular drugs (49.2% of cohort), and 317 963 prescribed COPD inhalers (94.7% of cohort). Evaluating output per search strategy, Search C missed numerous prescriptions, including vasodilator anti-hypertensives (A and B:19 696 prescriptions; C:1145) and SAMA inhalers (A and B:35 310; C:564). Discussion: We recommend the full search (A) for comprehensiveness. There are special considerations when generating adaptable and generalizable drug codelists, including fluctuating status, cohort-specific drug indications, underlying hierarchical ontology, and statistical analyses. Conclusions: Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts.

Smartphone-Based Remote Monitoring for Chronic Heart Failure: Mixed Methods Analysis of User Experience From Patient and Nurse Perspectives.

BACKGROUND: Community-based management by heart failure specialist nurses (HFSNs) is key to improving self-care in heart failure with reduced ejection fraction. Remote monitoring (RM) can aid nurse-led management, but in the literature, user feedback evaluation is skewed in favor of the patient rather than nursing user experience. Furthermore, the ways in which different groups use the same RM platform at the same time are rarely directly compared in the literature. We present a balanced semantic analysis of user feedback from patient and nurse perspectives of Luscii, a smartphone-based RM strategy combining self-measurement of vital signs, instant messaging, and e-learning. OBJECTIVE: This study aims to (1) evaluate how patients and nurses use this type of RM (usage type), (2) evaluate patients' and nurses' user feedback on this type of RM (user experience), and (3) directly compare the usage type and user experience of patients and nurses using the same type of RM platform at the same time. METHODS: We performed a retrospective usage type and user experience evaluation of the RM platform from the perspective of both patients with heart failure with reduced ejection fraction and the HFSNs using the platform to manage them. We conducted semantic analysis of written patient feedback provided via the platform and a focus group of 6 HFSNs. Additionally, as an indirect measure of tablet adherence, self-measured vital signs (blood pressure, heart rate, and body mass) were extracted from the RM platform at onboarding and 3 months later. Paired 2-tailed t tests were used to evaluate differences between mean scores across the 2 timepoints. RESULTS: A total of 79 patients (mean age 62 years; 35%, 28/79 female) were included. Semantic analysis of usage type revealed extensive, bidirectional information exchange between patients and HFSNs using the platform. Semantic analysis of user experience demonstrates a range of positive and negative perspectives. Positive impacts included increased patient engagement, convenience for both user groups, and continuity of care. Negative impacts included information overload for patients and increased workload for nurses. After the patients used the platform for 3 months, they showed significant reductions in heart rate (P=.004) and blood pressure (P=.008) but not body mass (P=.97) compared with onboarding. CONCLUSIONS: Smartphone-based RM with messaging and e-learning facilitates bilateral information sharing between patients and nurses on a range of topics. Patient and nurse user experience is largely positive and symmetrical, but there are possible negative impacts on patient attention and nurse workload. We recommend RM providers involve patient and nurse users in platform development, including recognition of RM usage in nursing job plans.

Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI.

Background: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. Methods: Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Results: After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's κ=0.77). Conclusions: We present proof of concept that, given the same clinician labelling effort, comparing multiple images side-by-side using a 'multiple-image-ranking' strategy achieves ground truth labels for DL more accurately than by classifying images individually. We demonstrate a potential clinical application: the automatic removal of unrequired CMR images. This leads to increased efficiency by focussing human and machine attention on images which are needed to answer clinical questions.