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.

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.

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.

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.

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.

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.

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.

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.

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/.

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.

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.

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.

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.

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.

Smartphone-Based Remote Monitoring in Heart Failure With Reduced Ejection Fraction: Retrospective Cohort Study of Secondary Care Use and Costs

BACKGROUND

Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospitalizations. Treatment optimization and admission avoidance rely on frequent symptom reviews and monitoring of vital signs. Remote monitoring (RM) aims to prevent admissions by facilitating early intervention, but the impact of noninvasive, smartphone-based RM of vital signs on secondary health care use and costs in the months after a new diagnosis of HFrEF is unknown.

OBJECTIVE

The purpose of this study is to conduct a secondary care health use and health-economic evaluation for patients with HFrEF using smartphone-based noninvasive RM and compare it with matched controls receiving usual care without RM.

METHODS

We conducted a retrospective study of 2 cohorts of newly diagnosed HFrEF patients, matched 1:1 for demographics, socioeconomic status, comorbidities, and HFrEF severity. They are (1) the RM group, with patients using the RM platform for >3 months and (2) the control group, with patients referred before RM was available who received usual heart failure care without RM. Emergency department (ED) attendance, hospital admissions, outpatient use, and the associated costs of this secondary care activity were extracted from the Discover data set for a 3-month period after diagnosis. Platform costs were added for the RM group. Secondary health care use and costs were analyzed using Kaplan-Meier event analysis and Cox proportional hazards modeling.

RESULTS

A total of 146 patients (mean age 63 years; 42/146, 29% female) were included (73 in each group). The groups were well-matched for all baseline characteristics except hypertension (P=.03). RM was associated with a lower hazard of ED attendance (hazard ratio [HR] 0.43; P=.02) and unplanned admissions (HR 0.26; P=.02). There were no differences in elective admissions (HR 1.03, P=.96) or outpatient use (HR 1.40; P=.18) between the 2 groups. These differences were sustained by a univariate model controlling for hypertension. Over a 3-month period, secondary health care costs were approximately 4-fold lower in the RM group than the control group, despite the additional cost of RM itself (mean cost per patient GBP £465, US $581 vs GBP £1850, US $2313, respectively; P=.04).

CONCLUSIONS

This retrospective cohort study shows that smartphone-based RM of vital signs is feasible for HFrEF. This type of RM was associated with an approximately 2-fold reduction in ED attendance and a 4-fold reduction in emergency admissions over just 3 months after a new diagnosis with HFrEF. Costs were significantly lower in the RM group without increasing outpatient demand. This type of RM could be adjunctive to standard care to reduce admissions, enabling other resources to help patients unable to use RM.

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.

Lateral Boundaries of the Ventricular Tachycardia Circuit Align With Sinus Rhythm Discontinuities

BACKGROUND

Sinus rhythm electrical activation mapping can provide information regarding the ischemic re-entrant ventricular tachycardia (VT) circuit. The information gleaned may include the localization of sinus rhythm electrical discontinuities, which can be defined as arcs of disrupted electrical conduction with large activation time differences across the arc.

OBJECTIVES

This study sought to detect and localize sinus rhythm electrical discontinuities that might be present in activation maps constructed from infarct border zone electrograms.

METHODS

Monomorphic re-entrant VT with a double-loop circuit and central isthmus was repeatedly inducible by programmed electrical stimulation in the epicardial border zone of 23 postinfarction canine hearts. Sinus rhythm and VT activation maps were constructed from 196 to 312 bipolar electrograms acquired surgically at the epicardial surface and analyzed computationally. A complete re-entrant circuit was mappable from the epicardial electrograms of VT, and isthmus lateral boundary (ILB) locations were ascertained. The difference in sinus rhythm activation time across ILB locations, vs the central isthmus and vs the circuit periphery, was determined.

RESULTS

Sinus rhythm activation time differences averaged 14.4 milliseconds across the ILB vs 6.5 milliseconds at the central isthmus and 6.4 milliseconds at the periphery (ie, the outer circuit loop) (P ≤ 0.001). Locations with large sinus rhythm activation difference tended to overlap ILB (60.3% ± 23.2%) compared with their overlap with the entire grid (27.5% ± 18.5%) (P < 0.001).

CONCLUSIONS

Disrupted electrical conduction is evident as discontinuity in sinus rhythm activation maps, particularly at ILB locations. These areas may represent permanent fixtures relating to spatial differences in border zone electrical properties, caused in part by alterations in underlying infarct depth. The tissue properties producing sinus rhythm discontinuity at ILB may contribute to functional conduction block formation at VT onset.

Should lethal arrhythmias in hypertrophic cardiomyopathy be predicted using non-electrophysiological methods?

While sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HCM) is due to arrhythmias, the guidelines for prediction of SCD are based solely on non-electrophysiological methods. This study aims to stimulate thinking about whether the interests of patients with HCM are better served by using current, 'risk factor', methods of prediction or by further development of electrophysiological methods to determine arrhythmic risk. Five published predictive studies of SCD in HCM, which contain sufficient data to permit analysis, were analysed to compute receiver operating characteristics together with their confidence bounds to compare their formal prediction either by bootstrapping or Monte Carlo analysis. Four are based on clinical risk factors, one with additional MRI analysis, and were regarded as exemplars of the risk factor approach. The other used an electrophysiological method and directly compared this method to risk factors in the same patients. Prediction methods that use conventional clinical risk factors and MRI have low predictive capacities that will only detect 50-60% of patients at risk with a 15-30% false positive rate [area under the curve (AUC) = ∼0.7], while the electrophysiological method detects 90% of events with a 20% false positive rate (AUC = ∼0.89). Given improved understanding of complex arrhythmogenesis, arrhythmic SCD is likely to be more accurately predictable using electrophysiologically based approaches as opposed to current guidelines and should drive further development of electrophysiologically based methods.

Ventricular Conduction Stability Noninvasively Identifies an Arrhythmic Substrate in Survivors of Idiopathic Ventricular Fibrillation

Background Idiopathic ventricular fibrillation (VF) is a diagnosis of exclusion following normal cardiac investigations. We sought to determine if exercise-induced changes in electrical substrate could distinguish patient groups with various ventricular arrhythmic pathophysiological conditions and identify patients susceptible to VF. Methods and Results Computed tomography and exercise testing in patients wearing a 252-electrode vest were combined to determine ventricular conduction stability between rest and peak exercise, as previously described. Using ventricular conduction stability, conduction heterogeneity in idiopathic VF survivors (n=14) was compared with those surviving VF during acute ischemia with preserved ventricular function following full revascularization (n=10), patients with benign ventricular ectopy (n=11), and patients with normal hearts, no arrhythmic history, and negative Ajmaline challenge during Brugada family screening (Brugada syndrome relatives; n=11). Activation patterns in normal subjects (Brugada syndrome relatives) are preserved following exercise, with mean ventricular conduction stability of 99.2±0.9%. Increased heterogeneity of activation occurred in the idiopathic VF survivors (ventricular conduction stability: 96.9±2.3%) compared with the other groups combined (versus 98.8±1.6%; P=0.001). All groups demonstrated periodic variation in activation heterogeneity (frequency, 0.3-1 Hz), but magnitude was greater in idiopathic VF survivors than Brugada syndrome relatives or patients with ventricular ectopy (7.6±4.1%, 2.9±2.9%, and 2.8±1.2%, respectively). The cause of this periodicity is unknown and was not replicable by introducing exercise-induced noise at comparable frequencies. Conclusions In normal subjects, ventricular activation patterns change little with exercise. In contrast, patients with susceptibility to VF experience activation heterogeneity following exercise that requires further investigation as a testable manifestation of underlying myocardial abnormalities otherwise silent during routine testing.

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.

Artificial intelligence-enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia

Background

Accurately determining arrhythmia mechanism from a 12-lead electrocardiogram (ECG) of supraventricular tachycardia can be challenging. We hypothesized a convolutional neural network (CNN) can be trained to classify atrioventricular re-entrant tachycardia (AVRT) vs atrioventricular nodal re-entrant tachycardia (AVNRT) from the 12-lead ECG, when using findings from the invasive electrophysiology (EP) study as the gold standard.

Methods

We trained a CNN on data from 124 patients undergoing EP studies with a final diagnosis of AVRT or AVNRT. A total of 4962 5-second 12-lead ECG segments were used for training. Each case was labeled AVRT or AVNRT based on the findings of the EP study. The model performance was evaluated against a hold-out test set of 31 patients and compared to an existing manual algorithm.

Results

The model had an accuracy of 77.4% in distinguishing between AVRT and AVNRT. The area under the receiver operating characteristic curve was 0.80. In comparison, the existing manual algorithm achieved an accuracy of 67.7% on the same test set. Saliency mapping demonstrated the network used the expected sections of the ECGs for diagnoses; these were the QRS complexes that may contain retrograde P waves.

Conclusion

We describe the first neural network trained to differentiate AVRT from AVNRT. Accurate diagnosis of arrhythmia mechanism from a 12-lead ECG could aid preprocedural counseling, consent, and procedure planning. The current accuracy from our neural network is modest but may be improved with a larger training dataset.

Optical mapping of contracting hearts

Optical mapping is a widely used tool to record and visualize the electrophysiological properties in a variety of myocardial preparations such as Langendorff-perfused isolated hearts, coronary-perfused wedge preparations, and cell culture monolayers. Motion artifact originating from the mechanical contraction of the myocardium creates a significant challenge to performing optical mapping of contracting hearts. Hence, to minimize the motion artifact, cardiac optical mapping studies are mostly performed on non-contracting hearts, where the mechanical contraction is removed using pharmacological excitation-contraction uncouplers. However, such experimental preparations eliminate the possibility of electromechanical interaction, and effects such as mechano-electric feedback cannot be studied. Recent developments in computer vision algorithms and ratiometric techniques have opened the possibility of performing optical mapping studies on isolated contracting hearts. In this review, we discuss the existing techniques and challenges of optical mapping of contracting hearts.

Comparison of methods for delivering cardiac resynchronization therapy: an acute electrical and haemodynamic within-patient comparison of left bundle branch area, His bundle, and biventricular pacing

AIMS

Left bundle branch area pacing (LBBAP) is a promising method for delivering cardiac resynchronization therapy (CRT), but its relative physiological effectiveness compared with His bundle pacing (HBP) is unknown. We conducted a within-patient comparison of HBP, LBBAP, and biventricular pacing (BVP).

METHODS AND RESULTS

Patients referred for CRT were recruited. We assessed electrical response using non-invasive mapping, and acute haemodynamic response using a high-precision haemodynamic protocol. Nineteen patients were recruited: 14 male, mean LVEF of 30%. Twelve had time for BVP measurements. All three modalities reduced total ventricular activation time (TVAT), (ΔTVATHBP -43 ± 14 ms and ΔTVATLBBAP -35 ± 20 ms vs. ΔTVATBVP -19 ± 30 ms, P = 0.03 and P = 0.1, respectively). HBP produced a significantly greater reduction in TVAT compared with LBBAP in all 19 patients (-46 ± 15 ms, -36 ± 17 ms, P = 0.03). His bundle pacing and LBBAP reduced left ventricular activation time (LVAT) more than BVP (ΔLVATHBP -43 ± 16 ms, P < 0.01 vs. BVP, ΔLVATLBBAP -45 ± 17 ms, P < 0.01 vs. BVP, ΔLVATBVP -13 ± 36 ms), with no difference between HBP and LBBAP (P = 0.65). Acute systolic blood pressure was increased by all three modalities. In the 12 with BVP, greater improvement was seen with HBP and LBBAP (6.4 ± 3.8 mmHg BVP, 8.1 ± 3.8 mmHg HBP, P = 0.02 vs. BVP and 8.4 ± 8.2 mmHg for LBBAP, P = 0.3 vs. BVP), with no difference between HBP and LBBAP (P = 0.8).

CONCLUSION

HBP delivered better ventricular resynchronization than LBBAP because right ventricular activation was slower during LBBAP. But LBBAP was not inferior to HBP with respect to LV electrical resynchronization and acute haemodynamic response.

Feasibility of mapping and ablating ectopy-triggering ganglionated plexus reproducibly in persistent atrial fibrillation

BACKGROUND

Ablation of autonomic ectopy-triggering ganglionated plexuses (ET-GP) has been used to treat paroxysmal atrial fibrillation (AF). It is not known if ET-GP localisation is reproducible between different stimulators or whether ET-GP can be mapped and ablated in persistent AF. We tested the reproducibility of the left atrial ET-GP location using different high-frequency high-output stimulators in AF. In addition, we tested the feasibility of identifying ET-GP locations in persistent atrial fibrillation.

METHODS

Nine patients undergoing clinically-indicated paroxysmal AF ablation received pacing-synchronised high-frequency stimulation (HFS), delivered in SR during the left atrial refractory period, to compare ET-GP localisation between a custom-built current-controlled stimulator (Tau20) and a voltage-controlled stimulator (Grass S88, SIU5). Two patients with persistent AF underwent cardioversion, left atrial ET-GP mapping with the Tau20 and ablation (Precision™, Tacticath™ [n = 1] or Carto™, SmartTouch™ [n = 1]). Pulmonary vein isolation (PVI) was not performed. Efficacy of ablation at ET-GP sites alone without PVI was assessed at 1 year.

RESULTS

The mean output to identify ET-GP was 34 mA (n = 5). Reproducibility of response to synchronised HFS was 100% (Tau20 vs Grass S88; [n = 16] [kappa = 1, SE = 0.00, 95% CI 1 to 1)][Tau20 v Tau20; [n = 13] [kappa = 1, SE = 0, 95% CI 1 to 1]). Two patients with persistent AF had 10 and 7 ET-GP sites identified requiring 6 and 3 min of radiofrequency ablation respectively to abolish ET-GP response. Both patients were free from AF for > 365 days without anti-arrhythmics.

CONCLUSIONS

ET-GP sites are identified at the same location by different stimulators. ET-GP ablation alone was able to prevent AF recurrence in persistent AF, and further studies would be warranted.

Survival and health economic outcomes in heart failure diagnosed at hospital admission versus community settings: a propensity-matched analysis

BACKGROUND AND AIMS

Most patients with heart failure (HF) are diagnosed following a hospital admission. The clinical and health economic impacts of index HF diagnosis made on admission to hospital versus community settings are not known.

METHODS

We used the North West London Discover database to examine 34 208 patients receiving an index diagnosis of HF between January 2015 and December 2020. A propensity score-matched (PSM) cohort was identified to adjust for differences in socioeconomic status, cardiovascular risk and pre-diagnosis health resource utilisation cost. Outcomes were stratified by two pathways to index HF diagnosis: a 'hospital pathway' was defined by diagnosis following hospital admission; and a 'community pathway' by diagnosis via a general practitioner or outpatient services. The primary clinical and health economic endpoints were all-cause mortality and cost-consequence differential, respectively.

RESULTS

The diagnosis of HF was via hospital pathway in 68% (23 273) of patients. The PSM cohort included 17 174 patients (8582 per group) and was matched across all selected confounders (p>0.05). The ratio of deaths per person-months at 24 months comparing community versus hospital diagnosis was 0.780 (95% CI 0.722 to 0.841, p<0.0001). By 72 months, the ratio of deaths was 0.960 (0.905 to 1.020, p=0.18). Diagnosis via hospital pathway incurred an overall extra longitudinal cost of £2485 per patient.

CONCLUSIONS

Index diagnosis of HF through hospital admission continues to dominate and is associated with a significantly greater short-term risk of mortality and substantially increased long-term costs than if first diagnosed in the community. This study highlights the potential for community diagnosis-early, before symptoms necessitate hospitalisation-to improve both clinical and health economic outcomes.

Immunohistochemical characteristics of local sites that trigger atrial arrhythmias in response to high-frequency stimulation

AIMS

The response to high frequency stimulation (HFS) is used to locate putative sites of ganglionated plexuses (GPs), which are implicated in triggering atrial fibrillation (AF). To identify topological and immunohistochemical characteristics of presumed GP sites functionally identified by HFS.

METHODS AND RESULTS

Sixty-three atrial sites were tested with HFS in four Langendorff-perfused porcine hearts. A 3.5 mm tip quadripolar ablation catheter was used to stimulate and deliver HFS to the left and right atrial epicardium, within the local atrial refractory period. Tissue samples from sites triggering atrial ectopy/AF (ET) sites and non-ET sites were stained with choline acetyltransferase (ChAT) and tyrosine hydroxylase (TH), for quantification of parasympathetic and sympathetic nerves, respectively. The average cross-sectional area (CSA) of nerves was also calculated. Histomorphometry of six ET sites (9.5%) identified by HFS evoking at least a single atrial ectopic was compared with non-ET sites. All ET sites contained ChAT-immunoreactive (ChAT-IR) and/or TH-immunoreactive nerves (TH-IR). Nerve density was greater in ET sites compared to non-ET sites (nerves/cm2: 162.3 ± 110.9 vs. 69.65 ± 72.48; P = 0.047). Overall, TH-IR nerves had a larger CSA than ChAT-IR nerves (µm2: 11 196 ± 35 141 vs. 2070 ± 5841; P < 0.0001), but in ET sites, TH-IR nerves were smaller than in non-ET sites (µm2: 6021 ± 14 586 vs. 25 254 ± 61 499; P < 0.001).

CONCLUSIONS

ET sites identified by HFS contained a higher density of smaller nerves than non-ET sites. The majority of these nerves were within the atrial myocardium. This has important clinical implications for devising an effective therapeutic strategy for targeting autonomic triggers of AF.

Ablation versus anti-arrhythmic therapy for reducing all hospital episodes from recurrent atrial fibrillation: a prospective, randomized, multi-centre, open label trial

AIMS

There is rising healthcare utilization related to the increasing incidence and prevalence of atrial fibrillation (AF) worldwide. Simplifying therapy and reducing hospital episodes would be a valuable development. The efficacy of a streamlined AF ablation approach was compared to drug therapy and a conventional catheter ablation technique for symptom control in paroxysmal AF.

METHODS AND RESULTS

We recruited 321 patients with symptomatic paroxysmal AF to a prospective randomized, multi-centre, open label trial at 13 UK hospitals. Patients were randomized 1:1:1 to cryo-balloon ablation without electrical mapping with patients discharged same day [Ablation Versus Anti-arrhythmic Therapy for Reducing All Hospital Episodes from Recurrent (AVATAR) protocol]; optimization of drug therapy; or cryo-balloon ablation with confirmation of pulmonary vein isolation and overnight hospitalization. The primary endpoint was time to any hospital episode related to treatment for atrial arrhythmia. Secondary endpoints included complications of treatment and quality-of-life measures. The hazard ratio (HR) for a primary endpoint event occurring when comparing AVATAR protocol arm to drug therapy was 0.156 (95% CI, 0.097-0.250; P < 0.0001 by Cox regression). Twenty-three patients (21%) recorded an endpoint event in the AVATAR arm compared to 76 patients (74%) within the drug therapy arm. Comparing AVATAR and conventional ablation arms resulted in a non-significant HR of 1.173 (95% CI, 0.639-2.154; P = 0.61 by Cox regression) with 23 patients (21%) and 19 patients (18%), respectively, recording primary endpoint events (P = 0.61 by log-rank test).

CONCLUSION

The AVATAR protocol was superior to drug therapy for avoiding hospital episodes related to AF treatment, but conventional cryoablation was not superior to the AVATAR protocol. This could have wide-ranging implications on how demand for AF symptom control is met.

TRIAL REGISTRATION

Clinical Trials Registration: NCT02459574.

A fully-automated paper ECG digitisation algorithm using deep learning

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60-70% and the average correlation of 3-by-1 ECGs achieved 80-90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects.

Bariatric surgery reverses ventricular repolarisation heterogeneity in obesity: mechanistic insights into fat-related arrhythmic risk

Background

Obesity is a growing global health problem that confers higher risks of atrial arrhythmias and sudden cardiac death. Despite this, the proarrhythmic substrate in obesity and its reversibility with weight loss has not been studied in-depth.

Purpose

To characterise the proarrhythmic substrate in obese patients, and its reversibility with bariatric surgery, using electrocardiographic imaging (ECGi).

Methods

ECGi was performed in 16 obese patients pre-bariatric surgery (PreSurg; mean age 43±12 years, 13 female) and 16 age- and sex-matched non-obese (lean) individuals (42±11 years). 12 of the 16 obese patients also underwent ECGi after bariatric surgery (PostSurg). Over 2000 atrial and ventricular epicardial electrograms were computed using high density body surface mapping (256-lead ECG) and heart-torso geometries from cardiac magnetic resonance imaging, by solving the inverse problem of electrocardiography. Local atrial and ventricular epicardial activation times (AT) were calculated as the steepest downslope of their respective activation complexes, and local ventricular repolarisation times (RT) as the steepest upslope of the T-wave. Atrial activation gradients (ATG) and ventricular repolarisation gradients (RTG) were calculated as the maximum difference within 10 mm radius divided by the corresponding distance.

Results

Body mass index was greater in PreSurg vs lean (46.7±5.5 vs 22.8±2.6 kg/m2, p&lt;0.0001) and decreased with surgery (PostSurg 36.8±6.5 kg/m2, p&lt;0.0001). Epicardial adipose tissue (EAT) was greater in PreSurg vs lean (83±56 vs 28±13 ml, p&lt;0.0001) and decreased post-surgery (PostSurg 69±45 ml, p=0.0010). Total atrial AT was prolonged in PreSurg vs lean (62±15 vs 46±12 ms, p=0.0028), which persisted post-surgery (PostSurg 67±15 ms, p=0.86). Atrial ATG were also greater in PreSurg vs lean (26±11 vs 14±8 ms, p=0.0007) and did not change with weight loss (PostSurg 25±12, p=0.44). Ventricular RTG were greater in PreSurg vs lean (26±11 vs 15±7 ms/mm, p=0.0024) and decreased with weight loss (PostSurg 19±8, p=0.0009). Ventricular RTG were similar between PostSurg and lean (p=0.20). EAT from lean and PreSurg individuals correlated with atrial ATG (r=0.36, p=0.044) and ventricular RTG (r=0.54, p=0.0014). Ventricular AT were similar between lean (31±6 ms), PreSurg (34±5 ms) and PostSurg (35±9 ms); all p&gt;0.05.

Conclusion

Steep ventricular repolarisation gradients and prolonged atrial activation contribute to the proarrhythmic substrate in obesity. Ventricular repolarisation gradients correlate with epicardial adiposity and both regress post-bariatric surgery. By contrast, atrial activation remains prolonged after weight loss. These results provide mechanistic insights into obesity-related arrhythmic risks and their reversibility with weight loss following bariatric surgery.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): British Heart FoundationNational Institute for Health Research (NIHR) Imperial Biomedical Research Centre (BRC).

Smartphone-based remote monitoring in chronic heart failure: patient & clinician user experience, impact on patient engagement and quality of life

Background

Heart failure with reduced ejection fraction (HFrEF) lowers patients' quality of life (QoL) [1]. Digital interventions such as ESC's “Heart Failure Matters” website aim to encourage patient-engagement & self-management [2], which remain major challenges in HFrEF care. Although remote monitoring (RM) has been tested in HFrEF with inconclusive impact on prognosis [3], its impact on patients' experience and engagement is unclear [4]. Furthermore, the perspective of clinicians using RM technologies remains unknown. We present users' experience of Luscii, a novel smartphone-based RM platform enabling HFrEF patients to submit clinical measurements, symptoms, complete educational modules, & communicate with HF specialist nurses (HFSNs).

Purpose

(I) To evaluate the usage-type & user experience of patients and HFSNs.

(II) To assess the impact of using the RM platform on self-reported QoL

Methods

A two-part retrospective analysis of HFrEF patients from our regional service using the RM platform: Part A: Thematic analysis of patient feedback provided via the platform and a focus group of six HFSNs. Part B: Scores for a locally-devised HF questionnaire (HFQ), depression (PHQ-9) & anxiety (GAD-7) questionnaires were extracted from the RM platform at two timepoints: at on-boarding and 3 months after. Paired non-parametric tests were used to evaluate difference between median scores across the two time points.

Results

83 patients (mean age 62 years; 27% female) used the RM platform between April and November 2021. 2 dropped out & 2 died before 3 months. Part A: Patients and HFSNs exchanged information on many topics via the platform, including patient educational modules (Figure 1). Thematic analysis revealed positive and negative impacts with many overlapping subthemes between the two user groups (Figure 2). Part B: At 3 months there was no difference in HFQ score (19 vs. 18, p=0.57, maximum possible score = 50). PHQ-9 (3 vs. 3, p=0.48, maximum possible score = 27) and GAD-7 (5 vs. 3, p=0.54. maximum possible score = 21) scores were low at onboarding and follow-up.

Conclusions

This evaluation shows smartphone-based RM is feasible in HFrEF with good retention (2% drop-out rate over 3 months, albeit in a cohort with low baseline depression and anxiety levels). The platform serves as an integrated solution for symptom reporting, patient-clinician communication & education. Positive impacts include patient engagement, convenience, admission avoidance & medication optimisation, but there was no corresponding change in QoL scores in the short-term. We find potential pitfalls: information overload for patients & increased workload for clinicians.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): Sameer Zaman is supported by UK Research and Innovation [UKRI Centre for Doctoral Training in AI for Healthcare grant number EP/S023283/1].

Smartphone-based remote monitoring (RM) in chronic heart failure reduces emergency hospital attendances, unplanned admissions and secondary care costs: a retrospective cohort study

Background

Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospital attendances [1]. Treatment optimisation and admission avoidance relies on frequent symptom review and monitoring of vital signs [2]. RM programmes aim to prevent admissions and improve system efficiency by enabling self-management [3]. Few studies evaluate the economic impact of RM in HFrEF, compared to real-world matched controls [4]. We compare hospital attendances and costs between patients using Luscii, a novel smartphone-based RM platform, and matched controls receiving usual care for 3 months.

Purpose

To assess the impact of RM on emergency department (ED) attendances, unplanned admissions and associated healthcare costs over 3 months.

Methods

A retrospective cohort study of new HFrEF referrals to our service was undertaken using the Discover dataset [5] for two cohorts (i) “RM group”: patients who used the RM platform for at least 3 months and (ii) “control group”: consecutive patients referred before the RM platform was available. The groups were matched 1:1 for age, sex, ethnicity, New York Heart Association grade and left ventricular ejection fraction. Medical co-morbidities, ED attendances, unplanned admissions and costs were extracted over 3 months from platform onboarding (RM group) or accepted referral (control group). Platform costs were added for the RM group. Differences between outcomes were analysed using t-tests, Kaplan-Meier event analysis and Cox's proportional hazard modelling.

Results

146 patients (mean age 63 years; 23% female) were included in the analyses (73 “RM group”; 73 “Control group”). The groups were well-matched for all baseline characteristics except hypertension (p=0.03). Compared to the control group, after 3 months follow-up the RM group had significantly fewer ED attendances (p&lt;0.01) and unplanned admissions (p&lt;0.01). Accounting for RM platform costs, there was no difference between ED costs (p=0.42), but significantly lower unplanned admissions costs in the RM group (p=0.02) (Table 1). RM was protective against ED attendances (HR=0.43, p=0.02) and unplanned admissions (HR=0.26, p=0.02), which was sustained after controlling for hypertension (Table 1). Kaplan-Meier analyses found significantly lower probability of ED attendances (p=0.02) and unplanned admissions (p=0.01) in the RM group (Figure 1).

Conclusions

HFrEF patients with RM were half as likely to attend ED and approximately four times less likely to need short-term unplanned admissions. The economic benefit of RM is driven by lower unplanned admission costs; the cost benefit is equivocal at the ED stage. Participants were younger than the typical HFrEF cohort. RM use could free up valuable resources to enhance standard care for older patients who decline or are unable to use RM. Further evaluation is required of the long-term impact of RM and its effect on outpatient encounters and costs.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Discover data extraction and analyst time were funded by Astra Zeneca. Astra Zeneca did not have any input to study design, analyses or reporting.

Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms. Eur Heart J Digit Health 2022;3:405-414. [PMID: 36712163 PMCID: PMC9708023 DOI: 10.1093/ehjdh/ztac042]

Aims

Accurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.

Methods and results

We trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13 500 five-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI-dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists in Europe was undertaken on the same 57 ECGs. The model had an accuracy of 86% (95% CI 0.77-0.95) compared to median expert electrophysiologist accuracy of 79% (range 70-84%). In the two thirds of test set cases (38/57) where both the model and electrophysiologist consensus were in agreement, the prediction accuracy was 100%. Saliency mapping demonstrated atrial activation was the most important segment of the ECG for determining model output.

Conclusion

We describe the first CNN trained to differentiate CTI-dependent AFL from other AT using the ECG. Our model matched and complemented expert electrophysiologist performance. Automated artificial intelligence-enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organized atrial arrhythmias.

Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps

Micro-anatomical reentry has been identified as a potential driver of atrial fibrillation (AF). In this paper, we introduce a novel computational method which aims to identify which atrial regions are most susceptible to micro-reentry. The approach, which considers the structural basis for micro-reentry only, is based on the premise that the accumulation of electrically insulating interstitial fibrosis can be modelled by simulating percolation-like phenomena on spatial networks. Our results suggest that at high coupling, where micro-reentry is rare, the micro-reentrant substrate is highly clustered in areas where the atrial walls are thin and have convex wall morphology, likely facilitating localised treatment via ablation. However, as transverse connections between fibres are removed, mimicking the accumulation of interstitial fibrosis, the substrate becomes less spatially clustered, and the bias to forming in thin, convex regions of the atria is reduced, possibly restricting the efficacy of localised ablation. Comparing our algorithm on image-based models with and without atrial fibre structure, we find that strong longitudinal fibre coupling can suppress the micro-reentrant substrate, whereas regions with disordered fibre orientations have an enhanced risk of micro-reentry. With further development, these methods may be useful for modelling the temporal development of the fibrotic substrate on an individualised basis.

Bariatric surgery reduces ventricular repolarisation gradients in obese patients - results from an electrocardiographic imaging study

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): National Institute for Health Research (NIHR) British Heart Foundation

Background

Obesity confers higher risks of atrial arrhythmias and is associated with abnormal ventricular repolarisation. Despite this, the proarrhythmic substrate in obesity and its reversibility with weight loss has not been studied in-depth.

Purpose

To characterise the proarrhythmic substrate in obese patients, and its reversibility with bariatric surgery, using electrocardiographic imaging (ECGi).

Methods

ECGi was performed in 8 obese patients before (PreOb) and after (PostOb) bariatric surgery (mean age 39+/-11years, 7 female), and in 8 age- and sex-matched non-obese controls (NOb) (40+/-11 years). ECGi recordings were made at rest, on exercise, and during recovery from exercise. For ECGi analysis, &gt;2000 atrial and ventricular epicardial electrograms were calculated from body surface potential recordings from 256 sites and information from cardiac magnetic resonance imaging, by solving the inverse problem. Local atrial and ventricular epicardial activation times (AT) were calculated as the steepest downslope of their respective activation complexes, and local ventricular repolarisation times (RT) as the steepest upslope of the T-wave. Atrial activation gradients (ATG) and ventricular repolarisation gradients (RTG) were calculated as the maximum difference within 10mm radius divided by the corresponding Euclidean distance.

Results

BMI was greater in PreOb vs NOb (46.6+/-4.8 vs 23.8+/-2.6kg/m2, p&lt;0.0001) and decreased with surgery (PostOb 35.3+/-4.2kg/m2, p&lt;0.0001). Total atrial AT was prolonged in PreOb vs NOb (68+/-12 vs 45+/-10ms, p=0.016) and did not change post-surgery (PreOb vs PostOb: 68+/-12 vs 67+/-16ms, p=0.81). Atrial ATG were also greater in PreOb vs NOb: max 254+/-111 vs 106+/-58ms, p=0.035; mean 24+/-6 vs 12+/-6ms, p=0.0087) and did not change with weight loss (PreOb vs PostOb: max 254+/-111 vs 222+/-69ms/mm, p=0.61; mean 24+/-6 vs 21+/-7ms/mm, p=0.52). Ventricular RTG were greater in PreOb vs NOb (max: 287+/-73 vs 131+/-89ms/mm, p=0.012; mean: 33+/-10 vs 17+/-9ms/mm, p=0.0052). Ventricular RTG decreased with weight loss (PreOb vs PostOb: max 287+/-73 vs 151+/-54ms/mm, p=0.0070; mean: 33+/-10 vs 21+/-8ms/mm, p=0.018), and were similar between PostOb and NOb (max, p=0.81; mean p=0.58). Ventricular AT and RT were non-different in NOb, PreOb and PostOb.

Conclusion

Obesity is associated with pro-arrhythmic electrophysiological remodelling, including steeper ventricular repolarisation gradients and slower atrial activation. At 6 months post-bariatric surgery, there was a reduction in ventricular repolarisation gradients though atrial conduction abnormalities persisted. These findings provide a mechanistic insight into obesity-related arrhythmic risks and its potential reversibility with weight loss surgery.

Classification of organised atrial arrythmias using explainable artificial intelligence

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): BHF

NIHR

Background

Accurately determining atrial arrhythmia mechanisms from a 12-lead ECG can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, accurate identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. Machine learning, with convolutional neural networks (CNNs) in particular, has been used to classify arrhythmias using the 12-lead ECG with great accuracy. However, most studies use human interpretation of the ECG as the ground truth to label the arrhythmia ECGs. Therefore, these neural networks can only ever be as good as expert human interpretation. We hypothesised a convolutional neural network could be trained to match or even exceed expert human performance in classifying CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), when using findings from the invasive electrophysiology (EP) study as the gold standard.

Methods

Figure 1 summarises the study methodology. We trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13500 5-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists and cardiologists in Europe was undertaken on the same 57 ECGs.

Results

The model had an accuracy of 86% (95% CI 0.77-0.95). The F1 score was 0.87.The AT/AFL network correctly identified AT 82% and AFL 90% of the time.

A saliency map can be used to help understand why a CNN predicted a particular outcome. This is achieved by mapping the outcome back to key areas of the input that most influenced the network in producing the classification result. Figure 2 presents the saliency mappings of an example 12-lead ECG for each class of AFL and AT. The network used the expected sections of the ECGs for diagnoses; these were the P-wave segments and not the QRS or other unexpected segments.

There were twelve respondents in the clinician survey. These respondents included nine electrophysiologists. The median accuracy was 78% (range 70-86%). The electrophysiologists had a median accuracy of 79%, (range 70-84%). Humans were more likely to incorrectly diagnose AFL as AT (on average incorrect diagnoses: 9 AFL, 1 AT). In comparison, the neural network most often incorrectly diagnosed AT as AFL (incorrect diagnoses: 5 AT, 3 AFL).

Conclusion

We describe the first neural network trained to differentiate CTI-dependent AFL from other atrial tachycardias. We found that our model surpassed expert human performance. Automated artificial intelligence enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organised atrial arrhythmias.

OCT2013, an ischaemia-activated antiarrhythmic prodrug, devoid of the systemic side effects of lidocaine

BACKGROUND AND PURPOSE

Sudden cardiac death (SCD) caused by acute myocardial ischaemia and ventricular fibrillation (VF) is an unmet therapeutic need. Lidocaine suppresses ischaemia-induced VF, but its utility is limited by side effects and a narrow therapeutic index. Here, we characterise OCT2013, a putative ischaemia-activated prodrug of lidocaine.

EXPERIMENTAL APPROACH

The rat Langendorff-perfused isolated heart, anaesthetised rat and rat ventricular myocyte preparations were utilised in a series of blinded and randomised studies to investigate the antiarrhythmic effectiveness, adverse effects and mechanism of action of OCT2013, compared with lidocaine.

KEY RESULTS

In isolated hearts, OCT2013 and lidocaine prevented ischaemia-induced VF equi-effectively, but OCT2013 did not share lidocaine's adverse effects (PR widening, bradycardia and negative inotropy). In anaesthetised rats, i.v. OCT2013 and lidocaine suppressed VF and increased survival equi-effectively; OCT2013 had no effect on cardiac output even at 64 mg·kg^-1 i.v., whereas lidocaine reduced it even at 1 mg·kg^-1 . In adult rat ventricular myocytes, OCT2013 had no effect on Ca²⁺ handling, whereas lidocaine impaired it. In paced isolated hearts, lidocaine caused rate-dependent conduction slowing and block, whereas OCT2013 was inactive. However, during regional ischaemia, OCT2013 and lidocaine equi-effectively hastened conduction block. Chromatography and MS analysis revealed that OCT2013, detectable in normoxic OCT2013-perfused hearts, became undetectable during global ischaemia, with lidocaine becoming detectable.

CONCLUSIONS AND IMPLICATIONS

OCT2013 is inactive but is bio-reduced locally in ischaemic myocardium to lidocaine, acting as an ischaemia-activated and ischaemia-selective antiarrhythmic prodrug with a large therapeutic index, mimicking lidocaine's benefit without adversity.

Ectopy-triggering ganglionated plexuses ablation to prevent atrial fibrillation: GANGLIA-AF study

BACKGROUND

The ganglionated plexuses (GPs) of the intrinsic cardiac autonomic system may play a role in atrial fibrillation (AF).

OBJECTIVE

We hypothesized that ablating the ectopy-triggering GPs (ET-GPs) prevents AF.

METHODS

GANGLIA-AF (ClinicalTrials.gov identifier NCT02487654) was a prospective, randomized, controlled, 3-center trial. ET-GPs were mapped using high frequency stimulation, delivered within the atrial refractory period and ablated until nonfunctional. If triggered AF became incessant, atrioventricular dissociating GPs were ablated. We compared GP ablation (GPA) without pulmonary vein isolation (PVI) against PVI in patients with paroxysmal AF. Follow-up was for 12 months including 3-monthly 48-hour Holter monitors. The primary end point was documented ≥30 seconds of atrial arrhythmia after a 3-month blanking period.

RESULTS

A total of 102 randomized patients were analyzed on a per-protocol basis after GPA (n = 52; 51%) or PVI (n = 50; 49%). Patients who underwent GPA had 89 ± 26 high frequency stimulation sites tested, identifying a median of 18.5% (interquartile range 16%-21%) of GPs. The radiofrequency ablation time was 22.9 ± 9.8 minutes in GPA and 38 ± 14.4 minutes in PVI (P < .0001). The freedom from ≥30 seconds of atrial arrhythmia at 12-month follow-up was 50% (26 of 52) with GPA vs 64% (32 of 50) with PVI (log-rank, P = .09). ET-GPA without atrioventricular dissociating GPA achieved 58% (22 of 38) freedom from the primary end point. There was a significantly higher reduction in antiarrhythmic drug usage postablation after GPA than after PVI (55.5% vs 36%; P = .05). Patients were referred for redo ablation procedures in 31% (16 of 52) after GPA and 24% (12 of 50) after PVI (P = .53).

CONCLUSION

GPA did not prevent atrial arrhythmias more than PVI. However, less radiofrequency ablation was delivered to achieve a higher reduction in antiarrhythmic drug usage with GPA than with PVI.

Prognostic Significance of Ventricular Arrhythmias in 13 444 Patients With Acute Coronary Syndrome: A Retrospective Cohort Study Based on Routine Clinical Data (NIHR Health Informatics Collaborative VA-ACS Study)

Background A minority of acute coronary syndrome (ACS) cases are associated with ventricular arrhythmias (VA) and/or cardiac arrest (CA). We investigated the effect of VA/CA at the time of ACS on long-term outcomes. Methods and Results We analyzed routine clinical data from 5 National Health Service trusts in the United Kingdom, collected between 2010 and 2017 by the National Institute for Health Research Health Informatics Collaborative. A total of 13 444 patients with ACS, 376 (2.8%) of whom had concurrent VA, survived to hospital discharge and were followed up for a median of 3.42 years. Patients with VA or CA at index presentation had significantly increased risks of subsequent VA during follow-up (VA group: adjusted hazard ratio [HR], 4.15 [95% CI, 2.42-7.09]; CA group: adjusted HR, 2.60 [95% CI, 1.23-5.48]). Patients who suffered a CA in the context of ACS and survived to discharge also had a 36% increase in long-term mortality (adjusted HR, 1.36 [95% CI, 1.04-1.78]), although the concurrent diagnosis of VA alone during ACS did not affect all-cause mortality (adjusted HR, 1.03 [95% CI, 0.80-1.33]). Conclusions Patients who develop VA or CA during ACS who survive to discharge have increased risks of subsequent VA, whereas those who have CA during ACS also have an increase in long-term mortality. These individuals may represent a subgroup at greater risk of subsequent arrhythmic events as a result of intrinsically lower thresholds for developing VA.

EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks

Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation. It is, however, notoriously difficult to perform. We present EP-PINNs (Physics Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation from sparse amounts of EP data. We demonstrate, using 1D and 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporal evolution of action potentials, whilst predicting parameters related to action potential duration (APD), excitability and diffusion coefficients. EP-PINNs are additionally able to identify heterogeneities in EP properties, making them potentially useful for the detection of fibrosis and other localised pathology linked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological in vitro preparations, by characterising the effect of anti-arrhythmic drugs on APD using optical mapping data. EP-PINNs are a promising clinical tool for the characterisation and potential treatment guidance of arrhythmias.

Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study

Background

Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower.

Methods

We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0–1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov, NCT04601415.

Findings

Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93·3%] of 1050). Quality was lowest for the aortic position (846 [80·6%]). AI-ECG performed best at the pulmonary valve position (p=0·02), with an AUROC of 0·85 (95% CI 0·81–0·89), sensitivity of 84·8% (76·2–91·3), and specificity of 69·5% (66·4–72·6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity. Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0·85 (0·81–0·89), sensitivity of 82·7% (72·7–90·2), and specificity of 79·9% (77·0–82·6). Using AI-ECG outputs from these two positions, a weighted logistic regression with l2 regularisation resulted in an AUROC of 0·91 (0·88–0·95), sensitivity of 91·9% (78·1–98·3), and specificity of 80·2% (75·5–84·3).

Interpretation

A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG-enabled stethoscope can detect LVEF of 40% or lower. These findings highlight the potential for inexpensive, non-invasive, workflow-adapted, point-of-care screening, for earlier diagnosis and prognostically beneficial treatment.

Funding

NHS Accelerated Access Collaborative, NHSX, and the National Institute for Health Research.

Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD

An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.

Structure and function of the ventricular tachycardia isthmus

Catheter ablation of postinfarction reentrant ventricular tachycardia (VT) has received renewed interest owing to the increased availability of high-resolution electroanatomic mapping systems that can describe the VT circuits in greater detail, and the emergence and need to target noninvasive external beam radioablation. These recent advancements provide optimism for improving the clinical outcome of VT ablation in patients with postinfarction and potentially other scar-related VTs. The combination of analyses gleaned from studies in swine and canine models of postinfarction reentrant VT, and in human studies, suggests the existence of common electroanatomic properties for reentrant VT circuits. Characterizing these properties may be useful for increasing the specificity of substrate mapping techniques and for noninvasive identification to guide ablation. Herein, we describe properties of reentrant VT circuits that may assist in elucidating the mechanisms of onset and maintenance, as well as a means to localize and delineate optimal catheter ablation targets.

Increasing Adiposity Is Associated With QTc Interval Prolongation and Increased Ventricular Arrhythmic Risk in the Context of Metabolic Dysfunction: Results From the UK Biobank

Background

Small-scale studies have linked obesity (Ob) and metabolic ill-health with proarrhythmic repolarisation abnormalities. Whether these are observed at a population scale, modulated by individuals' genetics, and confer higher risks of ventricular arrhythmias (VA) are not known.

Methods and Results

Firstly, using the UK Biobank, the association between adiposity and QTc interval was assessed in participants with a resting 12-lead ECG (n = 23,683), and a polygenic risk score (PRS) was developed to investigate any modulatory effect of genetics. Participants were also categorised into four phenotypes according to the presence (+) or absence (-) of Ob, and if they were metabolically unhealthy (MU+) or not (MU-). QTc was positively associated with body mass index (BMI), body fat (BF), waist:hip ratio (WHR), and hip and waist girths. Individuals' genetics had no significant modulatory effect on QTc-prolonging effects of increasing adiposity. QTc interval was comparably longer in those with metabolic perturbation without obesity (Ob-MU+) and obesity alone (Ob+MU-) compared with individuals with neither (Ob-MU-), and their co-existence (Ob+MU+) had an additive effect on QTc interval. Secondly, for 502,536 participants in the UK Biobank, odds ratios (ORs) for VA were computed for the four clinical phenotypes above using their past medical records. Referenced to Ob-MU-, ORs for VA in Ob-MU+ men and women were 5.96 (95% CI: 4.70-7.55) and 5.10 (95% CI: 3.34-7.80), respectively. ORs for Ob+MU+ were 6.99 (95% CI: 5.72-8.54) and 3.56 (95% CI: 2.66-4.77) in men and women, respectively.

Conclusion

Adiposity and metabolic perturbation increase QTc to a similar degree, and their co-existence exerts an additive effect. These effects are not modulated by individuals' genetics. Metabolic ill-health is associated with a higher OR for VA than obesity.

Neural networks applied to 12-lead electrocardiograms predict body mass index, visceral adiposity and concurrent cardiometabolic ill-health

Background

Obesity is associated with electrophysiological remodeling, which manifests as detectable changes on the surface electrocardiogram (ECG).

Objective

To develop neural networks (NN) to predict body mass index (BMI) from ECGs and test the hypothesis that discrepancies between NN-predicted BMI and measured BMI are indicative of underlying adiposity and/or concurrent cardiometabolic ill-health.

Methods

NN models were developed using 36,856 12-lead resting ECGs from the UK Biobank. Two architectures were developed for continuous and categorical BMI estimation (normal weight [BMI <25 kg/m²] vs overweight/obese [BMI ≥25 kg/m²]). Models for male and female participants were trained and tested separately. For each sex, data were randomly divided into 4 folds, and models were evaluated in a leave-1-fold-out manner.

Results

ECGs were available for 17,807 male and 19,049 female participants (mean ages: 61 ± 7 and 63 ± 8 years; mean BMI 26 ± 5 kg/m² and 27 ± 4 kg/m², respectively). NN models detected overweight/obese individuals with average accuracies of 75% and 73% for male and female subjects, respectively. The magnitudes of difference between NN-predicted BMI and actual BMI were significantly correlated with visceral adipose tissue volumes. Concurrent hypertension, diabetes, dyslipidemia, and/or coronary heart disease explained false-positive classifications (ie, calculated BMI <25 kg/m² misclassified as ≥25 kg/m² by NN model, P < .001).

Conclusion

NN models applied to 12-lead ECGs predict BMI with a reasonable degree of accuracy. Discrepancies between NN-predicted and calculated BMI may be indicative of underlying visceral adiposity and concomitant cardiometabolic perturbation, which could be used to identify individuals at risk of cardiometabolic disease.