Using artificial intelligence and deep learning to optimise the selection of adult congenital heart disease patients in S-ICD screening.

INTRODUCTION: The risk of complications associated with transvenous ICDs make the subcutaneous implantable cardiac defibrillator (S-ICD) a valuable alternative in patients with adult congenital heart disease (ACHD). However, higher S-ICD ineligibility and higher inappropriate shock rates-mostly caused by T wave oversensing (TWO)- are observed in this population. We report a novel application of deep learning methods to screen patients for S-ICD eligibility over a longer period than conventional screening. METHODS: Adult patients with ACHD and a control group of normal subjects were fitted with a 24-h Holters to record their S-ICD vectors. Their T:R ratio was analysed utilising phase space reconstruction matrices and a deep learning-based model to provide an in-depth description of the T: R variation plot for each vector. T: R variation was compared statistically using t-test. RESULTS: 13 patients (age 37.4 ± 7.89 years, 61.5 % male, 6 ACHD and 7 control subjects) were enrolled. A significant difference was observed in the mean and median T: R values between the two groups (p < 0.001). There was also a significant difference in the standard deviation of T: R between both groups (p = 0.04). CONCLUSIONS: T:R ratio, a main determinant for S-ICD eligibility, is significantly higher with more tendency to fluctuate in ACHD patients when compared to a population with normal hearts. We hypothesise that our novel model could be used to select S-ICD eligible patients by better characterisation of T:R ratio, reducing the risk of TWO and inappropriate shocks in the ACHD patient cohort.

Role of routine investigations post cardiac devices implants in detecting peri-procedural complications: A retrospective analysis from a tertiary UK center.

BACKGROUND: Peri-procedural complications associated with cardiac implantable electronic devices are not uncommon. European Society of Cardiology guidelines recommend device checks of all devices within 72 h of implant. European Heart Rhythm Association expert practical guide on Cardiac implantable electronic devices (CIEDs) recommend that a chest x-ray (CXR) should be performed within 24 h to rule out pneumothorax and document lead positions. First, the rate of peri-procedural complications associated with CIED implants at our center, as well as patient and/or procedural-related factors that are associated with higher rates of complications, is analyzed. Second, the yield of the guideline-recommended measures in the early detection of peri-procedural complications is examined. MATERIALS AND METHODS: Consecutive de novo transvenous device implants at our center in 2019 were retrospectively analyzed. Patients' demographics, types and indications for device therapy, procedural reports, device checks, and CXRs were obtained from the hospital electronic records. RESULTS: A total of 578 patients (Age 74 ± 16 years, 68% male) were included. All patients had routine post-procedure CXRs and device checks. There were 16 (2.8%) complications; 7 (1.2%) pneumothoraxes, 6 (1%) pericardial effusions, and 3 (0.5%) lead displacements. Procedure time correlated significantly with complications; in uncomplicated cases it was 99 ± 43 min versus 127 ± 50 min in procedures associated with complications (p = .02). CONCLUSIONS: Routine post CIED implantation CXRs can detect early peri-procedural complications, while repeat post mobilization device checks has low yield of detection of complications. The only statistically significant predictor of peri-procedural complications is the duration of the procedure; longer procedures were associated with higher rates of complications.

The application of fluoroscopic criteria to define leadless pacemakers implant position and the effect of location on device performance.

OBJECTIVE: Leadless pacemakers (LPs) were designed to avoid complications associated with transvenous pacing. To minimise risk of perforations, there is preference towards implanting LPs into the septum rather than the apex or free wall.An objective yet feasible way of characterising the LP location is currently lacking. We report a simple radiological method of defining LP position and our analysis of the impact of implantation site on performance of LPs. METHODS: The first 100 LPs implanted at our UK centre were reviewed and the devices' positions in fluoroscopy images and X-rays based on conventional criteria for lead positions and conventional practice for LPs positioning were assessed. The devices' electrical parameters at implant and at the latest device follow-up were used to compare performance between implantation sites. RESULTS: 35.6% of implants were in the apex. 31.1% in mid-septum, 16.7% in apical septum, 15.5% on the septal right ventricular inflow and 1.1% in the septal RV outflow tract. We had no major complications.Thresholds, R-wave amplitudes, and impedance averaged at 0.67 ± 0.41 V, 10.64 ± 5.30 mV, and 777.67 ± 201.67 Ohms, respectively, at the time of implantation, and 0.66 ± 0.39 V, 14.08 ± 6.14 mV, and 564.29 ± 96.76 Ohms at the last device check. There was no difference in the pacing thresholds or impedance between implant sites. CONCLUSIONS: We propose a simple, reproducible way of defining the LP location which can help standardise the assessment of the device location sites across LP implantation centres. ADVANCES IN KNOWLEDGE: Emphasis on the safety and reliability of the leadless pacemakers in a real-world setting.Establishing the variation in the implantation sites for leadless pacemakers and reporting the effect of the implantation sites on the devices' performance.We propose a simple, reproducible way of defining the LP location which can help standardise the assessment of the device location sites across LP implantation centres.

Correlation analysis of deep learning methods in S-ICD screening.

BACKGROUND: Machine learning methods are used in the classification of various cardiovascular diseases through ECG data analysis. The concept of varying subcutaneous implantable cardiac defibrillator (S-ICD) eligibility, owing to the dynamicity of ECG signals, has been introduced before. There are practical limitations to acquiring longer durations of ECG signals for S-ICD screening. This study explored the potential use of deep learning methods in S-ICD screening. METHODS: This was a retrospective study. A deep learning tool was used to provide descriptive analysis of the T:R ratios over 24 h recordings of S-ICD vectors. Spearman's rank correlation test was used to compare the results statistically to those of a "gold standard" S-ICD simulator. RESULTS: A total of 14 patients (mean age: 63.7 ± 5.2 years, 71.4% male) were recruited and 28 vectors were analyzed. Mean T:R, standard deviation of T:R, and favorable ratio time (FVR)-a new concept introduced in this study-for all vectors combined were 0.21 ± 0.11, 0.08 ± 0.04, and 79 ± 30%, respectively. There were statistically significant strong correlations between the outcomes of our novel tool and the S-ICD simulator (p < .001). CONCLUSION: Deep learning methods could provide a practical software solution to analyze data acquired for longer durations than current S-ICD screening practices. This could help select patients better suited for S-ICD therapy as well as guide vector selection in S-ICD eligible patients. Further work is needed before this could be translated into clinical practice.

Role of deep learning methods in screening for subcutaneous implantable cardioverter defibrillator in heart failure.

INTRODUCTION: S-ICD eligibility is assessed at pre-implant screening where surface ECG traces are used as surrogates for S-ICD vectors. In heart failure (HF) patients undergoing diuresis, electrolytes and fluid shifts can cause changes in R and T waves. Subsequently, T:R ratio, a major predictor of S-ICD eligibility, can be dynamic. METHODS: This is a prospective study of patients with structurally normal hearts and HF patients undergoing diuresis. All patients were fitted with Holters® to record their S-ICD vectors. Our deep learning model was used to analyze the T:R ratios across the recordings. Welch two sample t-test and Mann-Whitney U were used to compare the data between the two groups. RESULTS: Twenty-one patients (age 58.43 ± 18.92, 62% male, 14 HF, 7 normal hearts) were enrolled. There was a significant difference in the T:R ratios between both groups. Mean T: R was higher in the HF group (0.18 ± 0.08 vs 0.10 ± 0.05, p < .001). Standard deviation of T: R was also higher in the HF group (0.09 ± 0.05 vs 0.07 ± 0.04, p = .024). There was no difference between leads within the same group. CONCLUSIONS: T:R ratio, a main determinant for S-ICD eligibility, is higher and has more tendency to fluctuate in HF patients undergoing diuresis. We hypothesize that our novel neural network model could be used to select HF patients eligible for S-ICD by better characterization of T:R ratio reducing the risk of T-wave over-sensing (TWO) and inappropriate shocks. Further work is required to consolidate our findings before applying to clinical practice.

A leadless pacemaker matched with a vasovagal syncope: how long can it last?

BACKGROUND: Guidelines recommend that cardiac pacing should be considered in patients suffering from frequent vasovagal syncopal (VVS) episodes. Studies have demonstrated the safety and efficacy of leadless pacemakers (LP) in cardioinhibitory vasovagal populations specifically, rendering them a reasonable alternative to transvenous pacing in these patients. However, due to the paucity of data on extraction and the number of concomitant LPs that can be safely implanted, there are concerns regarding LPs' battery longevity, especially in younger patients who may require decades of pacing therapy. METHODS: This is a retrospective analysis of the first 100 LPs implanted at a tertiary cardiac centre in the UK. Demographical data and device parameters at implant and follow-ups were obtained from the hospital's medical records. The battery life of the LPs in the VVS patients was compared to that of patients with other pacing indications. RESULTS: Ninety patients were included in the analysis. 14 patients (15.6%) had VVS, and 76 patients (84.4%) had other indications for pacing. Mean ages were 34 ± 13 years and 62 ± 20 years for the VVS and the other group, respectively. The estimated total battery life was 15.22 ± 0.35 and 13.65 ± 2.97 years in the VVS and the other indications group respectively (p = .04). There were no complications in the VVS group. CONCLUSION: LPs provide a promising treatment for patients with vasovagal syncope with reassuring battery performance at the short/intermediate term. Further longer-term follow-up data are needed to identify the true battery potential in this patient cohort.

Deep learning-based insights on T:R ratio behaviour during prolonged screening for S-ICD eligibility.

BACKGROUND: A major predictor of eligibility of subcutaneous implantable cardiac defibrillators (S-ICD) is the T:R ratio. The eligibility cut-off of the T:R ratio incorporates a safety margin to accommodate for fluctuations of ECG signal amplitudes. We introduce a deep learning-based tool that accurately measures the degree of T:R ratio fluctuations and explore its role in S-ICD screening. METHODS: Patients were fitted with Holters for 24 h to record their S-ICD vectors. Our tool was used to assess the T:R ratio over the duration of the recordings. Multiple T:R ratio cut-off values were applied, identifying patients at high risk of T-wave oversensing (TWO) at each of the proposed values. The purpose of our study is to identify the ratio that recognises patients at high risk of TWO while not inappropriately excluding true S-ICD candidates. RESULTS: Thirty-seven patients (age 54.5 + / - 21.3 years, 64.8% male) were recruited. Fourteen patients had heart-failure, 7 hypertrophic cardiomyopathy, 7 had normal hearts, 6 had congenital heart disease, and 3 had prior inappropriate S-ICD shocks due to TWO. 54% of patients passed the screening at a T: R of 1:3. All patients passed the screening at a T: R of 1:1. The only subgroup to wholly pass the screening utilising all the proposed ratios are the participants with normal hearts. CONCLUSION: We propose adopting prolonged screening to select patients eligible for S-ICD with low probability of TWO and inappropriate shocks. The appropriate T:R ratio likely lies between 1:3 and 1:1. Further studies are required to identify the optimal screening thresholds.

Impact of right ventricular pacing site on the subcutaneous ICD sensing-a step towards personalised device therapy?

BACKGROUND: Patients with an existing subcutaneous implantable cardiac defibrillator (S-ICD) may develop a pacing indication. When transvenous pacing is not feasible, combining an S-ICD and a leadless pacemaker (LP) can be a reasonable option. There are reports of concomitant use of both devices. However, the effect of pacing on the S-ICD sensing is not well studied. We hypothesise that pacing changes R and T-wave amplitudes, causing changes in R:T ratios as perceived by a S-ICD, increasing the risk for T wave oversensing (TWO) during paced rhythm with a subsequent risk of inappropriate shocks. METHODS: This is a prospective study in patients undergoing electrophysiological studies. Participants were fitted with a Holter®, and the leads were placed to correspond to the vectors of an S-ICD. The right ventricle was paced at four positions for 10 beats each at 8 mA/2 ms. The Holter® traces were analysed, using two-way analysis of variance (ANOVA) to assess the effect of pacing on the R:T ratio. RESULTS: Forty-seven patients (age 56.02 ± 16.02, 72% male) were enrolled (81% structurally normal heart, 15% dilated cardiomyopathy, 2% ischaemic cardiomyopathy, and 2% adult congenital heart disease). Age, sex, and aetiology had no effect on the R:T ratio. Pacing caused significant changes in the R:T ratio. There was no significant difference in the R:T ratios between the pacing sites (p < 0.001). CONCLUSIONS: Pacing alters the R:T ratio significantly in most patients, theoretically increasing the risk for TWO and inappropriate shocks. Tailored programming for both devices is important for concomitant use of LPs and S-ICDs.