Ladejobi, Adetola

Background: Previous animal studies have shown no significant vascular injury from pulsed electrical field (PEF) ablation. We sought to assess the effect of PEF on swine coronary arteries. Methods: We performed intracoronary and epicardial (near the coronary artery) PEF ablations in swine pretreated with dual antiplatelet and antiarrhythmic therapy. Intracoronary PEF was delivered using MapiT catheters (Biotronik, Berlin), whereas epicardial PEF was delivered using EPT catheters (Boston Scientific, MA). PEF pulse duration was microseconds (Nanoknife 3.0, Angio Dynamics, NY) or nanoseconds (CellFX, Pulse Biosciences, CA). Results: We performed 39 intracoronary ablations in 10 swine and 20 epicardial-pericoronary ablations in 4 separate swine. Intracoronary PEF was delivered at higher energy compared with epicardial PEF (46 [interquartile range, IQR 20–85] J versus 10 [IQR 10–11] J, P < 0.01). Reversible coronary spasm occurred in 49% intracoronary ablations and 45% epicardial ablations ( P =0.80). At the end study, fixed coronary stenosis was demonstrated in 44% intracoronary ablations (80% for microsecond PEF and 18% for nanosecond PEF) and 0% epicardial ablations. Visible hemorrhagic and/or fibrotic myocardial lesions were observed at necropsy with similar frequency between intracoronary and epicardial PEF (45% versus 50%, P =0.70). Nanosecond PEF (49 ablations in 11 swine), when compared with microsecond PEF (10 intracoronary ablations in 3 swine), resulted in lower energy delivery (21 [IQR 10–46] J versus 129 [IQR 24–143] J, P =0.03) and less incidence of fixed coronary stenosis (18% versus 80%, P =0.04). Conclusions: In the swine model, intracoronary PEF resulted both in significant coronary spasm and fixed coronary stenosis. Epicardial PEF, delivered at lower energy, resulted in reversible spasm but no fixed coronary stenosis.

Abstract Background We have demonstrated that a neural network is able to predict a person’s age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes. Methods and results We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether Age-Gap was correlated with total and cardiovascular mortality. Of 25 144 subjects tested (54% females, 95% Caucasian) followed for 12.4 ± 5.3 years, the mean chronological age was 53.7 ± 11.6 years and ECG-derived age was 54.6 ± 11 years (R2 = 0.79, P &lt; 0.0001). The mean Age-Gap was small at 0.88 ± 7.4 years. Compared to those whose ECG-derived age was within 1 standard deviation (SD) of their chronological age, patients with Age-Gap ≥1 SD had higher all-cause and cardiovascular disease (CVD) mortality. Conversely, subjects whose Age-Gap was ≤1 SD had lower all-cause and CVD mortality. Results were unchanged after adjusting for CVD risk factors and other survival influencing factors. Conclusion The difference between AI ECG and chronological age is an independent predictor of all-cause and cardiovascular mortality. Discrepancies between these possibly reflect disease independent biological ageing.

Abstract Background We have demonstrated that artificial intelligence interpretation of ECGs (AI-ECG) can estimate an individual's physiologic age and that the gap between AI-ECG and chronologic age (Age-Gap) is associated with increased mortality. We hypothesized that Age-Gap would predict long-term atherosclerotic cardiovascular disease (ASCVD) and that Age-Gap would refine the ACC/AHA Pooled Cohort Equations' (PCE) predictive abilities. Methods Using the Rochester Epidemiology Project (REP) we evaluated a community-based cohort of consecutive patients seeking primary care between 1998–2000 and followed through March 2016. Inclusion criteria were age 40–79 and complete data to calculate PCE. We excluded those with known ASCVD, AF, HF or an event within 30 days of baseline.A neural network, trained, validated, and tested in an independent cohort of ∼ 500,000 independent patients, using 10-second digital samples of raw, 12 lead ECGs. PCE was categorized as low&lt;5%, intermediate 5–9.9%, high 10–19.9%, and very high≥20%. The primary endpoint was ASCVD and included fatal and non-fatal myocardial infarction and ischemic stroke; the secondary endpoint also included coronary revascularization [Percutaneous Coronary Intervention (PCI) or Coronary Artery Bypass Graft (CABG)], TIA and Cardiovascular mortality. Events were validated in duplicate. Follow-up was truncated at 10 years for PCE analysis. The association between Age-Gap with ASCVD and expanded ASCVD was assessed with cox proportional hazard models that adjusted for chronological age, sex and risk factors. Models were stratified by PCE risk categories to evaluate the effect of PCE predicted risk. Results We included 24,793 patients (54% women, 95% Caucasian) with mean follow up of 12.6±5.1 years. 2,366 (9.5%) developed ASCVD events and 3,401 (13.7%) the expanded ASCVD. Mean chronologic age was 53.6±11.6 years and the AI-ECG age was 54.5±10.9 years, R2=0.7865, p&lt;0.0001. The mean Age-Gap was 0.87±7.38 years. After adjusting for age and sex, those considered older by ECG, compared to their chronologic age had a higher risk for ASCVD when compared to those with &lt;−2 SD age gap (considered younger by ECG). (Figure 1A), with similar results when using the expanded definition of ASCVD (data not shown). Furthermore, Age-Gap enhanced predicted capabilities of the PCE among those with low 10-year predicted risk (&lt;5%): Age and sex adjusted HR 4.73, 95% CI 1.42–15.74, p-value=0.01 and among those with high predicted risk (&gt;20%) age and sex adjusted HR 6.90, 95% CI 1.98–24.08, p-value=0.0006, when comparing those older to younger by ECG respectively (Figure 1B). Conclusion The difference between physiologic AI-ECG age and chronologic age is associated with long-term ASCVD, and enhances current risk calculators (PCE) ability to identify high and low risk individuals. This may help identify individuals who should or should not be treated with newer, expensive risk-reducing therapies. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Mayo Clinic

Abstract Background Slowing electrical conduction by cooling the myocardium can be used for defibrillation. We previously demonstrated the efficacy of a small cold device placed in oblique sinus (OS) in terminating atrial fibrillation (AF). However, the parameters needed to achieve effective atrial defibrillation are unknown. Purpose Assess effect of the size of cooled myocardium on frequency of AF termination in acute canine animal models. Methods Sternotomy was performed under general anesthesia in 10 acute canine experiments. AF was induced using rapid atrial pacing and intra-myocardial epinephrine and acetylcholine injections. Once AF sustained for at least 30s, either a cool (7–9°C) or placebo (body temperature) device was placed in the OS. Four device sizes were tested; ½X½, ¾X¾, and 1X1 inch devices and two ¾X¾ inch devices placed side by side simultaneously. Time to AF termination was recorded. Chi-squared or Fisher's exact test were used to compare the frequency of arrhythmia termination with cooling versus placebo. Results A total of 166 applications were performed (89 cool vs 77 placebo) in 10 animal experiments. Overall, AF terminated in 82% of the cooling applications vs. 67.5% of placebo (P=0.03, Figure 1). For the ½X½ inch device 88% of cold applications restored sinus rhythm vs. 63.6% for placebo (P=0.05). The frequency of sinus restoration for cold ¾X¾, 1X1 and two ¾X¾ side by side devices was 86.7%, 83.3% and 70% respectively. Time to sinus restoration when achieved was within three minutes was also not significantly changed. Conclusion Placing a cool device in the oblique sinus can terminate AF and efficacy is not affected by the size of device. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): MediCool Technologies

Abstract Background His bundle pacing aims to mimic the activation pattern of normal conduction to maintain ventricular synchrony. However, selective His capture can be challenging, and the activation sequence during His pacing may not replicate normal conduction. Purpose Compare the right ventricular (RV) and left ventricular (LV) activation pattern in sinus rhythm and His bundle pacing. Methods Baseline LV and RV map was created in sinus rhythm using Rhythmia mapping system (Boston Scientific Corporation) in canine animal model. Medtronic 3830 lead was placed near the bundle of His under fluoroscopic, intracardiac echocardiogram, and electroanatomic guidance. Conduction system capture was confirmed by observing a QRS duration &lt;120ms and an isoelectric segment between pacing artifact and QRS on surface ECG. Repeat LV and RV activation map was obtained during His pacing. Average QRS, HV and pacing to V intervals were calculated with standard deviation. Results Mapping was performed successfully in four animals. At baseline, the average QRS duration was 44±2.6ms and HV interval was 32±4.2ms. Earliest site of myocardial activation was in the mid-septal LV region. The earliest RV myocardial activation was also at the septum closer to the apex, but later than the LV (Figure1A). With His pacing, the average QRS duration was 70±17.0ms and the average stim to V interval was 31±8.7ms. During His pacing, the earliest site of activation was in the RV septum, with an activation pattern from base to apex in both the RV and LV. Conclusion Unlike normal physiology, the activation pattern during conduction system pacing is from base to apex with earliest site in the RV. Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): Mayo Clinic