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.

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.

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.

Deep learning methods for screening patients' S-ICD implantation eligibility.

Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden cardiac death triggered by ventricular arrhythmias. T Wave Over Sensing (TWOS) is an inherent risk with S-ICDs which can lead to inappropriate shocks. A major predictor of TWOS is a high T:R ratio (the ratio between the amplitudes of the T and R waves). Currently, patients' Electrocardiograms (ECGs) are screened over 10 s to measure the T:R ratio to determine the patients' eligibility for S-ICD implantation. Due to temporal variations in the T:R ratio, 10 s is not a long enough window to reliably determine the normal values of a patient's T:R ratio. In this paper, we develop a convolutional neural network (CNN) based model utilising phase space reconstruction matrices to predict T:R ratios from 10-second ECG segments without explicitly locating the R or T waves, thus avoiding the issue of TWOS. This tool can be used to automatically screen patients over a much longer period and provide an in-depth description of the behavior of the T:R ratio over that period. The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.

Comparison of microsphere penetration with LC Bead LUMI™ versus other commercial microspheres.

The purpose of this study was to evaluate LC Bead LUMI™ (40-90µm and 70-150µm) in order to determine if their increased resistance to compression influences microsphere penetration and distribution compared to more compressible commercial microspheres. LC Bead LUMI™ 40-90µm and 70-150µm, LC BeadM1® 70-150µm, Embozene™ 40µm and Embozene™ 100µm size and distributions were measured using optical microscopy. Penetration in vitro was evaluated using an established 'plate model', consisting of a calibrated tapered gap between a glass plate and plastic housing to allow visual observation of microsphere penetration depth. Behaviour in vivo was assessed using a rabbit renal embolization model with histopathologic confirmation of vessel penetration depth. Penetration behaviour in vitro was reproducible and commensurate with the measured microsphere size, the smaller the microsphere the deeper the penetration. Comparison of the microsphere diameter measured on the 2D plate model versus the corresponding average microsphere size measured by histopathology in the kidney showed no significant differences (p = > 0.05 Mann-Whitney, demonstrating good in vitro - in vivo predictive capabilities of the plate model) confirming predictable performance for LC Bead LUMI™ (40-90µm and 70-150µm) based on microsphere size, their increased rigidity having no bearing on their depth of penetration and distribution. An assessment of a LC Bead LUMI™ (40-90µm and 70-150µm) has shown that despite having greater resistance to compression, these microspheres behave in a predictable manner within in vitro and in vivo models comparable with more compressible microspheres of similar sizes.

DC BeadM1™: towards an optimal transcatheter hepatic tumour therapy.

Clinical use of DC Bead™ loaded with doxorubicin (DEBDOX™) or irinotecan (DEBIRI™), for the treatment of primary and secondary tumours of the liver respectively, is showing great promise. Recently there has been a tendency to select smaller bead size ranges to treat tumours in an effort to allow more drug dose to be administered, improve tumoural penetration and resultant drug delivery and tumour coverage. Herein we describe the development and performance characterisation of a new DC Bead size range (DC BeadM1 (TM), 70-150 µm) capable of an increased bead delivery in the distal vasculature, corresponding to greater tumour coverage and drug dose delivered. Both unloaded and drug loaded DC BeadM1 were shown to have a greater density of distal volume of penetration although the ultimate distal level of penetration was the same as that of the 100-300 µm beads in an in vitro penetration model. Elution of doxorubicin was slower than irinotecan elution, but it was similar when comparing the same drug elution from 70 to 150 µm compared to 100-300 µm beads. Radiopaque versions of 70-150 and 100-300 µm beads were prepared in order to evaluate distribution ex vivo using µ-CT and doxorubicin distribution using epifluorescent microscopy. Liver distribution of the radiopaque versions of the beads was shown to be more distal and efficient at filling smaller vessels with the DC BeadM1 and correspondingly more beads were found per vessel histologically with a larger area of drug coverage with the smaller size range. This study indicates that the smaller (70-150 µm) beads should permit an increased dose of drug to be administered to both hypervascular and hypovascular tumours as compared to 100-300 µm beads.

Synthesis and characterisation of cationically modified phospholipid polymers.

Phospholipid-like copolymers based on 2-(methacryloyloxyethyl) phosphorylcholine were synthesised using monomer-starved free radical polymerisation methods and incorporating cationic charge in the form of the choline methacrylate monomer in amounts varying from 0 to 30 wt%, together with a 5 wt% silyl cross-linking agent in order to render them water-insoluble once thermally cured. Characterisation using a variety of techniques including nuclear magnetic resonance spectroscopy, high-pressure liquid chromatography and gel permeation chromatography showed the cationic monomer did not interfere with the polymerisation and that the desired amount of charge had been incorporated. Gravimetric and differential scanning calorimetry methods were used to evaluate the water contents of polymer membranes cured at 70 degrees C, which was seen to increase with increasing cation content, producing materials with water contents ranging from 50% to 98%. Surface plasmon resonance indicated that the coatings swelled rapidly in water, the rate and extent of swelling increasing with increasing cation level. Dynamic contact angle showed that coatings of all the polymers possessed a hydrophobic surface when dry in air, characteristic of the alkyl chains expressed at the surface (>100 degrees advancing angle). Rearrangement of the hydrophilic groups to the surface occurred once wet, to produce highly wettable surfaces with a decrease in advancing angle with increasing cation content. Atomic force microscopy showed all polymer films to be smooth with no features in topographical or phase imaging. Mechanical properties of the dry films were also unaffected by the increase in cation content.