Ultra-high-frequency ECG assessment of QRS fragmentation predicts sudden cardiac death risk in inherited arrhythmia syndromes

Background

Fragmentation of the QRS complex, as a surrogate for scar or functionally disrupted ventricular activation, has been postulated as a risk factor for malignant ventricular arrhythmias across a range of cardiac diagnoses including cardiomyopathies, channelopathies and myocardial infarction. Fragmentation is subtle on the conventionally filtered 12-lead ECG and can easily be missed or over-diagnosed. Isolation of high-frequency (HF) QRS components could overcome this to demonstrate easily identifiable fragmentation but this has previously been limited by technological constraints resulting in a limited range of measured frequencies (150–300Hz). Ultra-high-frequency ECG (UHF-ECG) is a novel technology that utilises amplification and signal-averaging techniques to reliably measure frequencies up to 1000Hz.

Purpose

We explored the use of UHF-ECG in arrhythmia risk stratification.

Methods

We recruited 60 participants to undergo UHF-ECG recordings, including 23 healthy volunteers and 37 patients with inherited arrhythmia syndromes: 25 hypertrophic cardiomyopathy (HCM), 5 Brugada syndrome, 4 arrhythmogenic cardiomyopathy, 3 idiopathic ventricular fibrillation, 2 long QT syndrome and 1 non-ischaemic dilated cardiomyopathy.

The arrhythmia risk status of patients with inherited disease was classified, by two independent researchers, into high or low risk based on their history of cardiac arrest, sustained ventricular arrhythmia, appropriate therapy, syncope and programmed ventricular stimulation result. A third researcher adjudicated disagreement. Two further researchers, blinded to aforementioned risk status, independently assessed the UHF-ECG recordings of all participants.

Results

40 patients were classified as low risk, and 20 as high. Healthy volunteer UHF-ECGs showed uniform ventricular activation with single HF peaks in each lead. High-risk patients' UHF-ECGs showed multiple HF peaks, representing QRS fragmentation. The maximum number of HF peaks in any lead was used to measure severity of fragmentation. Example UHF-ECGs are shown in Figure 1. Fragmentation severity (number of peaks) correlated with arrhythmia risk status (chi-square statistic = 8.95, p-value = 0.03) across all participants (Figure 2) and when comparing high to low risk patients with inherited disease.

UHF-ECG fragmentation could be observed even when the 12-lead ECG did not show any observable fragmentation. Among patients with inherited disease, patients with HCM showed the largest difference in UHF-ECG fragmentation between high and low risk. UHF-ECG fragmentation analysis showed excellent reproducibility with no difference in number of peaks identified between two independent assessors.

Conclusion

We demonstrate proof-of-concept that a novel ultra-high-frequency tool for measuring a broad range of high frequency QRS components can be used for sudden death risk stratification in patients with inherited cardiac conditions.

Funding Acknowledgement

Type of funding sources: None.

A systematic review and meta-analysis of upgrade to biventricular or conduction system pacing approaches

Introduction

Chronic RV pacing has been recognised as being harmful to cardiac function. Patients undergoing a de novo pacemaker implant with even mild LV impairment are recommended to instead receive a physiological pacing strategy (biventricular or conduction system pacing [CSP]). No corresponding guideline recommendation exists for patients who already have a pacemaker.

Methods

We undertook a random-effects meta-analysis of all RCTs and observational studies covering device upgrade to biventricular pacing or conduction system pacing.

Results

6 RCTs assessing effect of upgrade to BiV pacing randomising 161 patients were eligible for analysis. Eligible observational studies included 46 of BiV upgrade and 7 of CSP upgrade totalling 2795 patients.

Mean LVEF improved by +8.3% from 34.4% in BiV upgrade RCTs (p=0.001) and +8.3% from 25.7% in BiV upgrade observational studies (p<0.001).

In observational studies of upgrade to CSP, LVEF increased by +10.1% from 38.4% (p=0.001) despite less severe LV impairment at baseline (p=0.004 vs mean EF in BiV RCTs and p<0.0001 vs mean EF in BiV observational studies).

LVESV decreased significantly by −25.4 ml, −23.7 ml, and −19.8 ml in BiV RCTs, BiV observational studies and CSP observational studies. Significant changes were also seen in NYHA class (decreased by −0.4, −0.8 and −1.0 respectively).

Minnesota Heart Failure Score (−6.9 points) and peak oxygen uptake (+1.1 ml/kg/min) increased significantly in RCTs of BiV upgrade. This was also seen in observational studies of BiV upgrade (−21.0 points and +2.63 ml/kg/min respectively).

Conclusions

RCTs and observational studies of upgrade to BiV pacing show significant physiological and symptomatic benefit. Observational studies of CSP upgrade show similar benefit with significant improvements in LVEF, LVESV and NYHA class in patients with an even milder degree of baseline LV impairment.

Funding Acknowledgement

Type of funding sources: None.

Explanation-visualised deep learning model for accessory pathway localisation using 12-lead electrocardiography

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): British Heart Foundation Imperial Centre of Research Excellence

Background/Introduction

ECG algorithms for identifying accessory pathway (AP) locations are inaccurate and difficult to use. Human expert interpretation is poorly reproducible. Artificial intelligence (AI) techniques such as machine learning can improve accuracy in classification tasks by eschewing theory-driven predictions. More reproducible and accurate AP localisation could shorten procedure time and personalise ablation consent.

Purpose

We developed a neural network to perform AP localisation using 12-lead ECGs. Its decision-making process was analysed to enable explainability of the neural network.

Methods

A convolutional neural network was trained on raw, digital, intra-procedural 12-lead ECGs of patients with manifest APs who underwent successful ablation. ECGs were labelled with AP locations as left-sided, septal or right-sided using procedure reports, fluoroscopy and electro-anatomical maps. Accuracy of the neural network was assessed via 4-fold cross-validation and was compared to the Arruda algorithm. Five cardiologists were also assessed for their accuracy in determining locations in sub-groups of cases. The neural network was retrospectively analysed to identify areas of ECGs most influential to its predictions using importance mapping.

Results 

In 156 cases, accuracy of the neural network (92.9%) was significantly higher than the Arruda algorithm (76.9%; p < 0.0001) and all five cardiologists (37.5% to 65.9%; p = 0.0001 to 0.0290). Importance mapping demonstrated that the QRS complexes of leads aVL and V1 were perceived as most influential, indicating interrogation of the lateral and anterior-posterior axes respectively.

The figure shows (A) architecture of the neural network, (B) accuracy of the neural network, Arruda algorithm and five cardiologists, (*, p = 0.05 – 0.01; **, p = 0.01 – 0.001; ***, p = 0.001 - 0.0001; ****, p < 0.0001; as compared to the neural network) and (C) example importance maps for 12-lead ECGs of left-sided, septal and right-sided APs (in order from left to right), with darker regions corresponding to greater relative importance. 

Conclusion 

AI ECG interpretation allows accurate, reproducible prediction of AP locations, superior to conventional algorithms and human interpretation. Although AI decision-making is thought of as a ‘black box’, explanation visualisation techniques such as importance mapping allow humans to understand aspects of how a neural network make decisions. A prospectively validated neural network could be integrated into clinical practice to improve pre-procedural AP localisation. Abstract Figure. Summary of results

Laser doppler derived peripheral perfusion can distinguish haemodynamically tolerated VT from haemodynamically compromised VT

Funding Acknowledgements

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): NIHR Imperial Biomedical Research Centre

Introduction

Implantable Cardioverter-Defibrillators (ICDs) cannot distinguish between ventricular tachycardia (VT) with haemodynamic compromise from haemodynamically tolerated VT to ensure that therapies are delivered only when necessary. Currently, unnecessary therapies are reduced by longer duration thresholds and higher rate thresholds. This can result in ICDs withholding or delaying therapies during haemodynamically compromising VT while potentially still providing therapies during rapid or prolonged VT that is haemodynamically well tolerated.

Laser doppler perfusion monitoring (LDPM) allows assessment of peripheral blood flow as a surrogate for haemodynamic status. We have previously demonstrated that laser doppler perfusion signals, analysed using an electro-mechanical coupling algorithm (SafeShock), can reliably identify loss of perfusion during ventricular fibrillation (VF), as well as discriminate VF from simulated lead fractures and T wave over-sensing. The utility of LDPM signals in VT, however, has not been established.

Purpose

In this study we assessed the utility of LDPM using the SafeShock algorithm to discriminate haemodynamically tolerated VT from VT with haemodynamic compromise.

Methods

Recruited participants underwent a rapid ventricular pacing protocol to simulate VT at different rates. Pacing was performed using the right ventricular lead of an implanted pacing device or a temporary pacing wire in the right ventricular apex. 3-lead ECG, blood pressure (either invasively using a radial artery catheter or non-invasively using beat-by-beat finometry) and LDPM signal were continuously recorded during the protocol. LDPM signals during simulated VT were analysed using the SafeShock electro-mechanical algorithm and compared to blood pressure change from baseline intrinsic rhythm to simulated VT.

Results

We obtained 588 recordings of simulated VT in 56 patients at rates of 100 bpm, 120 bpm, 140 bpm, 160 bpm, 180 bpm and 200 bpm. Percentage change in systolic blood pressure from baseline to VT correlated with LDPM-derived perfusion value during VT (Spearman’s Rho = 0.7786, p < 0.0001).

Using a cut-off of 5 units, perfusion value predicted a 20% drop in systolic blood pressure in VT with an accuracy of 89.4% (sensitivity 94.8%, specificity 83.6%, p value <0.0001).

Conclusions

Peripheral perfusion measurements, analysed using an electro-mechanical algorithm, can accurately discriminate haemodynamically tolerated VT from VT with haemodynamic compromise. ICDs with integrated LDPM sensors and algorithms could make therapy decisions based on the circulatory status of patients with arrhythmias not just rate and duration parameters. This could reduce unnecessary therapies while facilitating prompt treatment of compromising arrhythmias. Abstract Figure 1

Non-selective and selective His bundle pacing both preserve left ventricular activation time and pattern

Funding Acknowledgements

Type of funding sources: Public Institution(s). Main funding source(s): British Heart Foundation

Background: His bundle pacing can be achieved in two ways

selective His bundle pacing, where the His bundle is captured alone, and non-selective His bundle pacing, where local myocardium is also captured resulting a pre-excited ECG appearance. We assessed the impact of this ventricular pre-excitation on left and right ventricular dys-synchrony.

Methods

We recruited patients who displayed both selective and non-selective His bundle pacing. We performed non-invasive epicardial electrical mapping to determine left and right ventricular activation times and patterns.

Results

In the primary analysis (n = 20, all patients), non-selective His bundle pacing did not prolong LVAT compared to select His bundle pacing by a pre-specified non-inferiority margin of 10ms (LVAT prolongation: -5.5ms, 95% confidence interval (CI): -0.6 to -10.4, non-inferiority p < 0.0001). Non-selective His bundle pacing did not prolong right ventricular activation time (4.3ms, 95%CI: -4.0 to 12.8, p = 0.296) but did prolong QRS duration (22.1ms, 95%CI: 11.8 to 32.4, p = 0.0003).

In patients with narrow intrinsic QRS (n = 6), non-selective His bundle pacing preserved left ventricular activation time (-2.9ms, 95%CI: -9.7 to 4.0, p = 0.331) but prolonged QRS duration (31.4ms, 95%CI: 22.0 to 40.7, p = 0.0003) and mean right ventricular activation time (16.8ms, 95%CI: -5.3 to 38.9, p = 0.108) compared to selective His bundle pacing.

Activation pattern of the left ventricular surface was unchanged between selective and non-selective His bundle pacing. Non-selective His bundle pacing produced early basal right ventricular activation, which was not observed with selective His bundle pacing.

Conclusions

Compared to selective His bundle pacing, local myocardial capture during non-selective His bundle pacing produces right ventricular pre-excitation resulting in prolongation of QRS duration. However, non-selective His bundle pacing preserves the left ventricular activation time and pattern of selective His bundle pacing. When choosing between selective and non-selective His bundle pacing, left ventricular dyssynchrony is not an important factor. Abstract Figure: Selective vs Non-Selective HBP

A method for accurately and dynamically optimising pacemaker atrio-ventricular delay timing using implantable physiological biomarkers

Funding Acknowledgements

Type of funding sources: Other. Main funding source(s): BRAVO trial: BHF SP/10/002/28189, FS/10/038, FS/11/92/29122, FS/13/44/30291) National Institute for Health Research Imperial Biomedical Research Centre. HOPE-HF trial: British Heart Foundation (CS/15/3/31405, FS/13/44/30291, FS/15/53/31615, FS/14/27/30752, FS/10/038).

Introduction

The optimal atrioventricular (AV) delay for implantable cardiac devices can be derived by echocardiography or  beat-by-beat blood pressure measurements. However, both of these approaches are labour intensive and neither could be incorporated into an implantable cardiac device for frequent repeated optimisations. Laser Doppler perfusion monitoring (LDPM) measures blood flow through tissue. LDPM has been miniaturised ready to be incorporated into future implantable cardiac devices.

Purpose

We studied if LDPM is a clinically reliable alternative method to blood-pressure measurements to determine optimal AV delay.

Methods

Data from  58 patients undergoing 94 clinical AVD optimisations using LDPM and simultaneous non-invasive beat-by-beat blood pressure was obtained. The optimal AV delay for each method and for each optimisation was determined using a curve of haemodynamic response to switching from AAI (reference state) to DDD (test state) at a series of AV delays (40, 80, 120, 160, 200, 240 ms). We then compared the derived optimal AV delays between the two measurement approaches. We also assessed the impact of the paced heart-rate on agreement between laser Doppler and Blood-Pressure derived optimal AV delays.

Results

The AV delay derived using LDPM was not clinically significant different from that derived by blood pressure changes. The median difference was -9ms (IQR -26 to 7, p = 0.05). Variability between the two methods was low (median absolute deviation 17ms). Optimisations performed at higher heart-rates resulted in a non-significant smaller difference between the LDPM and blood-pressure derived AV delays (median absolute deviation 12 vs 22 ms, p = 0.11).

Conclusions

Optimal AVDs derived from non-invasive blood-pressure or laser Doppler perfusion methods are clinically equivalent. The addition of laser Doppler to future implantable cardiac devices may enable devices to dynamically and reliably optimise AV delays. Abstract Figure 1