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<0.0001) and decreased with surgery (PostSurg 36.8±6.5 kg/m2, p<0.0001). Epicardial adipose tissue (EAT) was greater in PreSurg vs lean (83±56 vs 28±13 ml, p<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>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<0.01) and unplanned admissions (p<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.

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, >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<0.0001) and decreased with surgery (PostOb 35.3+/-4.2kg/m2, p<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.

Multicentre validation of point-of-care screening tool for heart failure: single-lead ECG recorded by smart stethoscope predicts low ejection fraction using artificial intelligence

Background/Introduction

Artificial intelligence (AI) applied to 12-lead ECG can identify left ventricular ejection fraction (EF) ≤35% with a sensitivity and specificity of 86.3% and 85.7%, respectively. Whether AI algorithms trained on 12-lead can accurately predict EF from single-lead ECGs (recorded by a smart stethoscope) remains unknown. This could facilitate point-of-care screening for low EF during routine clinical examination.

Purpose

First independent multicentre real-world UK National Health Service (NHS) prospective validation of 12-lead-ECG-trained AI algorithm applied to single-lead ECG recorded by a smart stethoscope, with AI algorithm tuned to detect EF ≤40%.

Methods

Prospective recruitment of unselected patients attending for echocardiography across six urban NHS hospital sites (UK). In addition to transthoracic echocardiogram (routine care), all participants had 15 seconds of supine, single-lead ECG recorded at six different positions (figure), encompassing standard anatomical positions for cardiac auscultation. A convolutional neural network (CNN) previously trained on 35,970 independent pairings of 12-lead-ECG and echocardiograms was retrained to use the single-lead ECG as input. Accuracy of CNN detection of low EF (binary ≤40%) is reported at a threshold of 0.5 against gold-standard; echo-determined percentage EF.

Results

Among 353 patients recruited (mean age 63±17; 58% male, 43.1% non-white), 309 (87.5%) had an EF >40%, and 44 (12.5%) had EF ≤40%. The best single recording position in isolation was position 3 (sensitivity 57.9% [42.2–73.6], specificity 86.3% [82.2–90.3]). Taking any of the six positions performed during the examination as predicting EF ≤40%, this achieved a sensitivity of 81.2% and specificity of 61.5%.

Conclusion(s)

In this first prospective multicentre validation study the retrained AI algorithm reliably detected low EF from single-lead ECGs acquired using a novel ECG-enabled stethoscope in standard auscultation positions. The ability to identify patients with possible low EF during routine physical examination addresses a significant unmet clinical need in point-of-care ruling in/out of heart failure, and has potential to provide broader population-level screening for asymptomatic cardiovascular disease.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health Research, Accelerated Access Collaborative & NHSX: Artificial Intelligence in Health & Social Care Award

Triage-HF plus: 12-month study of remote monitoring pathway for triage of heart failure risk initiated during the Covid-19 pandemic

Background

The Covid-19 pandemic necessitated rapid adoption of remote monitoring across cardiovascular patient cohorts. Most patients with cardiac implantable electronic devices (CIEDs) are now able to be remotely monitored using either scheduled, patient- or threshold-triggered transmissions. The validated “Triage Heart Failure Risk Score” (Triage-HFRS) is a medical algorithm within company-specific CIEDs that can risk-stratify patients as low-, medium- or high-risk of worsening heart failure (WHF) in the next 30 days based on integrated monitoring of physiological parameters. Building on a previous proof-of-concept of the Triage-HF Plus pathway, we integrated remote data with simple 5-question telephone triage within a clinical pathway to identify WHF during the first year of the Covid-19 pandemic.

Purpose

Prospective evaluation of clinical remote monitoring pathway integrating Triage-HFRS with protocolised telephone triage (Triage-HF Plus pathway).

Methods

Prospective, real-world evaluation of clinical pathway serving a large urban region over a 12-month period, using data from April 2020 to April 2021 (initiated during the first wave of Covid-19 pandemic in the UK). From a population of 435 patients with CIEDs, 87 “high” Triage-HFRS alerts were received and patients contacted for telephone triage assessment. Screening questions were designed to identify episodes of WHF and non-HF events. Intervention was at discretion of the clinical practitioner and in line with guideline-directed practice. A consecutive sample of 115 “medium” risk scores received the same triage.

Results

Successful contact was made with 72 (82.8%) high-risk patients. Classification for high scoring patients confirmed on triage included isolated heart failure (18.3%), heart failure concurrent to medical problem (5.7%), alternative medical problem (10.3%), and recent hospital admission (8.0%); triage reassured absence of acute cause of high score in 40.2%. The sensitivity and specificity for detection of WHF was 87.9% (0.77–0.99) and 59.4% (0.50–0.69) respectively. Positive and negative predictive values were 40.3% and 94.0%, respectively. Overall accuracy was 66.2%.

Conclusions

The Triage-HF Plus pathway served as a useful remote monitoring tool for identifying patients with WHF whose care had been otherwise disrupted by the Covid-19 pandemic, allowing timely intervention and cementing the longer-term role for such models of care delivery. Crucially, in this multimorbid, high-cost population, relevant non-HF issues were also identified. The high negative predictive value further highlights the potential of proactive surveillance over conventional, periodic follow up.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): Imperial Health Charity

Septal late gadolinium enhancement on Cardiac MRI predicts failure to achieve left bundle pacing

Funding Acknowledgements

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

Background; Left bundle area pacing is a novel technique that provides direct stimulation of cardiac conduction tissue in order to deliver physiological ventricular activation. The approach for left bundle area pacing is transseptal lead implantation, where the lead is advanced from the right ventricular side of the septum to the left ventricular side to capture the proximal left bundle. Observational data suggests that whilst this is a safe and feasible method, implant success rate is not 100%, and appears to be lower in patients with a cardiac resynchronization therapy (CRT) indication rather than a bradycardia indication for pacing. The mechanisms for failure to advance the lead through the ventricular septum are not well understood.

Purpose; We used pre-procedural CMR to determine whether there are features which can help identify patients where lead implantation may be challenging.  We assessed whether the extent and location of septal late gadolinium enhancement identified patients in whom left bundle area pacing will be challenging.  We hypothesized that the presence of extensive scar in the septum impedes advancing the lead to the left ventricular septum and prevents capture of the left bundle.

Methods; Patients underwent cardiac MRI including motion corrected free-breathing late gadolinium enhancement imaging1 before implantation.  Scar was quantified using the full height half maximum method and expressed as the overall proportion of myocardial mass in the basal anteroseptal and basal inferoseptal segments, as shown in Figure 1.  Left bundle area pacing was then attempted in patients with a CRT indication for pacing.  We compared the extent of septal scar between patients in whom left bundle area pacing was achieved and those where there was failure to advance the lead deep into the septum.

Results; 12 patients (11 male, 1 female), with average age 72 (IQR 63 to 78) and LVEF 30% (IQR 26 to 33) were studied.  There was failure to advance the lead deep into the septum in 4 patients.  There was a significantly higher basal septal scar burden in those patients where there was failure to advance the left bundle lead compared to those in which left bundle capture was achieved as shown in Figure 2 (median 55% and 5% respectively, p-value 0.02 by Wilcoxon signed rank test).

Conclusion; The presence and extent of late gadolinium enhancement in the basal septum appears to be an important determinant of successful implantation of left bundle pacing lead using current implant technology. This may be because extensive septal scar prevents advancement of the pacing lead through the septum. Cardiac MRI before left bundle area pacing is likely to be useful in procedural planning.

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

Investigation of the safety and feasibility of AAV1/SERCA2a gene transfer in patients with chronic heart failure supported with a left ventricular assist device - the SERCA-LVAD TRIAL

The SERCA-LVAD trial was a phase 2a trial assessing the safety and feasibility of delivering an adeno-associated vector 1 carrying the cardiac isoform of the sarcoplasmic reticulum calcium ATPase (AAV1/SERCA2a) to adult chronic heart failure patients implanted with a left ventricular assist device. The SERCA-LVAD trial was one of a program of AAV1/SERCA2a cardiac gene therapy trials including CUPID1, CUPID 2 and AGENT trials. Enroled subjects were randomised to receive a single intracoronary infusion of 1 × 10¹³ DNase-resistant AAV1/SERCA2a particles or a placebo solution in a double-blinded design, stratified by presence of neutralising antibodies to AAV. Elective endomyocardial biopsy was performed at 6 months unless the subject had undergone cardiac transplantation, with myocardial samples assessed for the presence of exogenous viral DNA from the treatment vector. Safety assessments including ELISPOT were serially performed. Although designed as a 24 subject trial, recruitment was stopped after five subjects had been randomised and received infusion due to the neutral result from the CUPID 2 trial. Here we describe the results from the 5 patients at 3 years follow up, which confirmed that viral DNA was delivered to the failing human heart in 2 patients receiving gene therapy with vector detectable at follow up endomyocardial biopsy or cardiac transplantation. Absolute levels of detectable transgene DNA were low, and no functional benefit was observed. There were no safety concerns in this small cohort. This trial identified some of the challenges of performing gene therapy trials in this LVAD patient cohort which may help guide future trial design.

P975Composite electroanatomical maps locate rapid activity within low voltage zones in persistent AF

Funding Acknowledgements

Our research group receives an educational grant from Abbott Inc.

Introduction. Outcomes from catheter ablation of persistent AF (psAF) are not favourable. The two prevailing major directions to improve success are left atrial (LA) substrate ablation, and non pulmonary vein driver ablation. In LA substrate ablation guided by intracardiac voltage, there is debate on the most fitting mapping rhythm and the appropriate cut offs for low voltage zones (LVZ). Non pulmonary vein driver ablation requires extensive experience and relies on complex pattern recognition by the operator, introducing subjectivity, that may lead to reduced reproducibility. AF drivers have been shown to localise to LVZs. We propose an objective, patient-tailored method of identifying rapid activity within LVZs to locate drivers of psAF. 

Methods. Eleven patients (61 ± 10.8 years of age, 9 male) undergoing first time catheter ablation for psAF were included. 3D maps were collected with a double spiral 20 pole catheter, in non-cardiac triggered mode, recording 8s segments at each bipole. Mean AF voltage (AFV) a AF cycle length (AFCL) was calculated for each 8s segment using automated algorithms. Grades of rapid activity and low voltage were defined as the 10th 20th and 30th percentile of all collected points within a patient. Percentile-matched composite LVZ-ARA maps were created on a research platform. 

Results. Mean LVZ percentage of the total mapped area was 4.67 ± 2.4%, 13.95 ± 3.8%, 23.81 ± 5.7% for the 10th, 20th and 30th percentiles respectively (Table 1). Mean, percentile matched LVZ-ARA overlap area percentage of the total mapped area was 0.3 ± 0.25% (10th-10th), 0.86 ± 0.58 (20th-20th), 3.1 ± 1.9% (30th-30th). ARAs represented a small proportion of all LVZs. Location of overlap areas differed significantly between patients and were marked with colours. Multi-colour areas including purple represent LVZ, multi-colour areas excluding purple, show LVZ-ARA overlap (examples in Fig 1). 

Conclusion. Analysis of LVZ-ARA overlap by mean AFV and AFCL provides an objective method of identifying potential drivers that localise to LVZs. The identified overlap areas constituted small, occasionally disparate areas within the LVZ of the LA. By adjusting the AFCL and AFV percentiles, the overlap areas can be tailored at the operator’s discretion, maintaining reproducible, objective decision making, without the need for complex pattern recognition. If ablation is planned, established techniques can be used to target the overlap areas, such as homogenisation or transection and connection to anatomical or ablative non-conductive tissues.

AFCL 10th AFCL 20th AFCL 30th AFV 10th AFV 20th AFV 30th All patients 128 ± 13 ms 144 ± 10 ms 150 ± 9 ms 0.15 ± 0.02 mV 0.19 ± 0.03 mV 0.24 ± 0.04 mV Mean values of percentile cut offs. AFCL: AF cycle length; AFV: AF voltage

Abstract Figure. Fig 1

Achieving good-quality consent: review of literature, case law and guidance

Background

Informed consent is an integral part of clinical practice. There is widespread agreement amongst health professionals that obtaining procedural consent needs to move away from a unidirectional transfer of information to a process of supporting patients in making informed, self‐determined decisions. This review aimed to identify processes and measures that warrant consideration when engaging in consent‐based discussions with competent patients undergoing elective procedures.

Methods

Formal written guidance from the General Medical Council and Royal College of Surgeons of England, in addition to peer‐reviewed literature and case law, was considered in the formulation of this review.

Results

A framework for obtaining consent is presented that is informed by the key tenets of shared decision‐making (SDM), a model that advocates the contribution of both the clinician and patient to the decision‐making process through emphasis on patient participation, analysis of empirical evidence, and effective information exchange. Moreover, areas of contention are highlighted in which further guidance and research are necessary for improved enhancement of the consent process.

Conclusion

This SDM‐centric framework provides structure, detail and suggestions for achieving meaningful consent.

P967Role of low voltage ablation in catheter ablation of patients with persistent AF- a single centre experience

Funding Acknowledgements

Research grant from Abbott

Introduction

We have recently described a novel evaluation of AF voltage which correlates better with MRI-DE defined scar than sinus rhythm voltage. We evaluated the clinical efficacy of additional voltage-based substrate modification in the Persistent AF patient cohort in a single centre case series.

Methods

22 PsAF patients undergoing catheter ablation were recruited. Left atrial electroanatomical maps were created in AF before any ablation was performed in all patients. Mean peak to peak AF voltage mapping was undertaken using 8s segments of AF (<0.35mV).  PVI was then performed in all patients after which, further ablation lesions were delivered on the underlying scar tissue (transection, box formation or homogenisation).

Results

Of the 22 patients currently under follow up, 16 patients are more than 12 months after their initial procedure. 11/16 patients have had no recurrence and no patient is currently on anti-arrhythmic medication.

Conclusion

From our series, 69% of PsAF patients remain arrhythmia free at one year follow up post blanking period with a single procedure. Ablation of low voltage areas appears to infer incremental benefit in the Persistent AF population.

Table 1 Mean Age, yrs 64 ± 9 Male 19 (86.3) Diabetes mellitus 1 (4.5) Hypertension 7 (31.8) TIA/CVA 2 (9) Left ventricular EF ≥55% 22 (100.0) LA size (diameter, according to British Society of Echocardiography Guidelines) Normal -Mild 12 (54.5) Moderate - Severe 10 (45.5) Mean AF duration, months 24.2 ± 20.8 Current antiarrhythmic strategy Amiodarone 3 (13.6) Sotalol 1 (4.5) Current anticoagulation Warfarin 3 (13.6) Direct oral anticoagulants 19 (86.3) Values are mean ± SD or N (%) or duration in months ± SD AF = atrial fibrillation; CVA= cerebrovascular accident; EF = ejection fraction; LA = left atrium; TIA = transient ischaemic attack. Baseline characteristics of patients (n = 22)

Abstract Figure. Ablation sets and AF Voltage

P991Pattern of rapid activity is preserved in persistent AF in selected locations after pulmonary vein isolation

Funding Acknowledgements

Our research group receives an educational grant from Abbott Inc.

Introduction. There is evidence to suggest that structural remodelling in psAF potentially gives rise to areas of rapid cycle length activity that may act as driving mechanisms. We describe a new method to compare rapid activity (RA) in psAF prior to and after pulmonary vein isolation, in extended AF segments (EAFS). We focus on patterns of RA, based on the hypothesis that AF drivers are transient but recur in the same locations. 

Methods. Five patients (61 ± 8 years of age, 3 male) for catheter ablation of psAF were included. 3D maps were collected with a double spiral 20 pole catheter. In stable locations, pre and post PVI, 37s EAFS were recorded using 8s segments, automatically every 1s, creating a 7s overlap between segments. Dominant cycle length (DCL) was determined for every 8s segment by a fully automated algorithm. RA was defined as the rapidest 20th percentile for each patient. RA episodes consisted of continuous segments with rapid DCL (black lines in Fig 1) and terminated with a non-rapid segment (red lines on Fig 1). Episodes were truncated where overlap occurred (Box 1 and Box 2 in Fig 1). The pattern of RA was assessed by the number, cumulative duration and mean duration of RA episodes within an EAFS pre and post PVI.

Results. Mean DCL of EAFS increased significantly in 4/5 patients after PVI, the number of EAFS with rapid activity showed a reduction in all patients.  The percentage of new sites with RA post PVI was 27%. The number of sites that retained RA post PVI was 14 ± 11.3 (58.3%; Table 1).  Of these, number and cumulative duration of RA did not change in 4/5 patients, and mean duration of RA remained stable in 5/5.

Conclusion. An automated DCL algorithm shows that, in most cases, global AFCL prolongs significantly with PVI overall, but selected foci retain RA and RA patterns. These may represent active drivers, as their activity appears to be independent of their surroundings.

Table 1 Patient ID Number of segments Mean AFCL ± SD of all segments Number of EAFS with rapid activity Pre-PVI Post-PVI P Pre-PVI Post-PVI New sites 1 145 135 ± 8.9 141 ± 9.8 <0.001 94 62 15 2 121 154 ± 12.9 162 ± 15.0 <0.001 94 72 11 3 172 148 ± 13.7 160 ± 16.6 <0.001 108 82 25 4 301 172 ± 22.9 174 ± 21.5 0.418 198 189 58 5 200 177 ± 9.9 215 ± 18.1 <0.001 87 43 14 Pre and Post PVI cycle length and EAFS with rapid activity. (AFCL: AF cycle length; EAFS: Extended AF segments; PVI: Pulmonary vein isolation)

Abstract Figure.

P6594Granger Causality-based analysis to accurately identify specific electrophenotypes of myocardial fibrillation

Background

Clinical identification of fibrillation drivers remains challenging in both atrial and ventricular fibrillation (VF). In this study, we developed novel tools using granger causality (GC) analysis for quantifying the causal relationship between neighbouring fibrillatory signals. We tested whether it was adaptable to low resolution, limited coverage and sequentially acquired data for quantifying global organisation of VF and mapping regions with stable rotational drivers (RDs).

Methods

Eighteen Sprague-Dawley rat hearts were perfused ex vivo for optical mapping studies. VF with differing degrees of organisation was induced with carbenoxolone (10–50μM, n=8), or prior maturation of patchy ventricular fibrosis (n=10) generated by ischaemia-reperfusion. After phase mapping, the data was downsampled to 25% of full resolution to develop validated GC-based tools. The causality pairing index (CPI), a global measure of organisation, quantified propagational effects between all neighboring signals. Low-resolution GC-vector maps localized areas harboring RDs and quantified the prevalence of RDs over time using a novel index called circular interdependence value (CIV). These GC-based tools were then adapted to analyze low-resolution multi-electrode electrograms of sixteen persistent-AF (psAF) patients presenting for a first ablation procedure.

Results

A spectrum of fibrillatory organisation and mechanisms in VF was observed. In rat VF there was a positive correlation between CPI and the number of stable RDs (R2=0.41, p=0.004), and CIV showed a significant difference in driver vs non-driver regions (0.91±0.05 vs 0.35±0.06, p=0.0002). Similarly, in psAF patients, there was a positive correlation between CPI and the number of stable RDs (R2=0.56, p≤0.001). GC vector mapping showed that 8/16 of patients had at least one RD area, and 8/16 had chaotic activity with no RDs.

Conclusion

Mechanisms of myocardial fibrillation occurs along a spectrum between organized activity with discrete areas harboring RDs and disorganised myocardial activation with no RDs. GC maps can be utilised for identifying regions localising RDs with sequential mapping in limited spatial resolution and coverage. In psAF GC-based analysis accurately identified specific fibrillatory mechanisms from low-resolution mapping. GC vector mapping holds potential for development with human fibrillation data as a mapping tool for driver guided ablation.

Acknowledgement/Funding

BHF Programme Grant PG/16/17/32069

P1594Ventricular fibrosis spatial distribution and quantity is a key mechanistic determinant of ventricular fibrillation mechanisms

Background

Ventricular fibrosis is known to play a critical role in initiation and maintenance of ventricular fibrillation (VF). Post myocardial infarction the quantity of fibrosis negatively correlates with survival. There is a lack of data on how the quantity and degree of fibrosis influences the mechanisms of VF itself. VF mechanisms remain debated, there are data to support both critical areas sustaining rotational drivers (RDs) and the contrary hypothesis of disorganized myocardial activation driving VF.

Purpose

We hypothesized that the underlying mechanism of VF is influenced by the spatial distribution and quantity of ventricular fibrosis.

Methods

Thirty-five Sprague-Dawley rats underwent permanent left anterior descending (LAD) ligation (n=11), 20mins LAD territory ischaemia-reperfusion (n=13) or in-vivo angiotensin infusion (500ng/kg/min, n=11) to generate compact (CF), patchy (PF) and diffuse fibrosis (DF) models respectively. After a 4-week maturation period, the hearts were explanted, Langendorff perfused and VF induced with burst pacing and 30μM pinacidil. Fibrillation dynamics were quantified using phase analysis, phase singularity (PS) tracking and our novel method of global fibrillation organisation quantification, frequency dominance index (FDI), which is a power ratio of highest amplitude dominant frequency in the frequency spectrum.

Results

Ventricular fibrosis for each group was characterized and quantified (CF: 22.3±3.2%, PF: 18.4±4.2%, DF: 5.8±1.3%, p=0.046). VF was driven predominantly by disorganised activity in CF, PSs were detected 26±7% of time comparative to 51.2±4% in DF and 69.5±8% in PF group (p=0.001). PF stabilised RDs, average maximum rotations for a single RD in PF were 31.6±7.1 comparative to 12.5±1.7 in DF and 6.4±1.1 in CF, p<0.001. The average maximum duration for a single RDs was significantly longer in PF (PF: 1231±365ms, DF: 568±68ms, CF: 363±41ms, p=0.014). Similarly, average rotations per RD were greater in the PF group (PF: 4.5±0.7, DF: 3.3±0.2, CF: 2.41±0.3 rotations, p=0.013). Total number of RDs/second were much greater with PF (PF: 12.4±2.0, DF: 5.4±0.8, CF: 3.1±1.1, p<0.001). VF organisation measured by FDI was higher in PF (PF: 0.61±0.07, DF: 0.47±0.04, CF: 0.33±0.03, p=0.004). RDs in DF showed a greater degree of meander comparative to PF (DF: 12.6±1.4 vs PF: 9.3±0.8 pixels, p=0.024).

Conclusion

VF mechanisms occur along a spectrum between organised activity sustained by discrete drivers and disorganised myocardial activation. The underlying VF mechanism can differ significantly dependent on the quantity and pattern of fibrosis. Patchy fibrosis stabilises RDs with localization to discrete areas and sustains an organised form of VF comparative to CF where VF is largely disorganised. Characterising the degree and pattern of fibrosis in patient groups vulnerable to VF might be beneficial in identifying patients with suitable targets for ablation.

Acknowledgement/Funding

BHF Programme Grant PG/16/17/32069

Development of an automaton model of rotational activity driving atrial fibrillation

BACKGROUND

Atrial fibrillation (AF) is difficult to treat effectively, owing to uncertainty in where to best ablate to eliminate arrhythmogenic substrate. A model providing insight into the electrical activation events would be useful to guide catheter ablation strategy. Method A two-dimensional, 576×576 node automaton was developed to simulate atrial electrical activity. The substrate field was altered by the presence of differing refractory period at varying locations. Fibrosis was added in the form of short, randomly positioned lines of conduction block. Larger areas of block were used to simulate ablation lesions. Anisotropy was imposed in a 2:1 ratio. A premature electrical impulse from one of four grid corners was utilized to initiate activation.

RESULTS

Rotational activity was uninducible when refractory patch dimensions were less than 20×20mm. For larger refractory regions, a single premature stimulus was capable of inducing an average of 1.19±1.10 rotors, which often formed near the patch edges. A maximum of 5 rotors formed when refractory patch dimensions approached the size of the entire left atrial virtual field. Rotors formed along a refractory patch edge, after wavefront arrival was delayed at turning points or due to the presence of a fiber cluster of sufficient size. However, rotational activity could also occur around a large fiber cluster without the need of spatially variable refractoriness. When obstacles to conduction were lacking in size, nascent rotors drifted and either extinguished, or stabilized upon anchoring at a sufficiently large fiber cluster elsewhere in the field. Transient rotors terminated when traversing a region with differing refractory periods, if no obstacle to conduction was present to sufficiently delay wavefront arrival beyond the longest refractory period. Other rotors were annihilated when a nearby rotor with faster spin rate gradually interrupted the activation pathway. Elimination of anchors by removal, or by simulated ablation over a sufficient region, prevented rotor onset at a particular location where it would otherwise form.

CONCLUSIONS

The presence of obstacles to conduction and spatial differences in refractory period are important parameters for initiating and maintaining rotational activity in this simulation of an atrial substrate.

Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping

Measurements of cardiac conduction velocity provide valuable functional and structural insight into the initiation and perpetuation of cardiac arrhythmias, in both a clinical and laboratory context. The interpretation of activation wavefronts and their propagation can identify mechanistic properties of a broad range of electrophysiological pathologies. However, the sparsity, distribution and uncertainty of recorded data make accurate conduction velocity calculation difficult. A wide range of mathematical approaches have been proposed for addressing this challenge, often targeted towards specific data modalities, species or recording environments. Many of these algorithms require identification of activation times from electrogram recordings which themselves may have complex morphology or low signal-to-noise ratio. This paper surveys algorithms designed for identifying local activation times and computing conduction direction and speed. Their suitability for use in different recording contexts and applications is assessed.

Haemorrhagic cerebral air embolism from an atrio-oesophageal fistula following atrial fibrillation ablation

We report the case of a man found unconscious three weeks following atrial fibrillation (AF) ablation. Cranial and thoracic imaging demonstrated multiple areas of pneumo-embolic infarction secondary to an atrio-oesophageal fistula (AEF). AEF is a recognised, but rare, complication of AF ablation.(1-8) Early recognition is critical as the mortality is 100% without surgical intervention. We consider the postulated mechanisms of AEF formation, the spectrum of clinical presentation, investigations and treatment.

Automated analysis of atrial late gadolinium enhancement imaging that correlates with endocardial voltage and clinical outcomes: a 2-center study

BACKGROUND

For late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) assessment of atrial scar to guide management and targeting of ablation in atrial fibrillation (AF), an objective, reproducible method of identifying atrial scar is required.

OBJECTIVE

To describe an automated method for operator-independent quantification of LGE that correlates with colocated endocardial voltage and clinical outcomes.

METHODS

LGE CMR imaging was performed at 2 centers, before and 3 months after pulmonary vein isolation for paroxysmal AF (n = 50). A left atrial (LA) surface scar map was constructed by using automated software, expressing intensity as multiples of standard deviation (SD) above blood pool mean. Twenty-one patients underwent endocardial voltage mapping at the time of pulmonary vein isolation (11 were redo procedures). Scar maps and voltage maps were spatially registered to the same magnetic resonance angiography (MRA) segmentation.

RESULTS

The LGE levels of 3, 4, and 5SDs above blood pool mean were associated with progressively lower bipolar voltages compared to the preceding enhancement level (0.85 ± 0.33, 0.50 ± 0.22, and 0.38 ± 0.28 mV; P = .002, P < .001, and P = .048, respectively). The proportion of atrial surface area classified as scar (ie, >3 SD above blood pool mean) on preablation scans was greater in patients with postablation AF recurrence than those without recurrence (6.6% ± 6.7% vs 3.5% ± 3.0%, P = .032). The LA volume >102 mL was associated with a significantly greater proportion of LA scar (6.4% ± 5.9% vs 3.4% ± 2.2%; P = .007).

CONCLUSIONS

LA scar quantified automatically by a simple objective method correlates with colocated endocardial voltage. Greater preablation scar is associated with LA dilatation and AF recurrence.