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

ABSTRACT

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 TR 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: TR 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 TR ratio, reducing the risk of TWO and inappropriate shocks in the ACHD patient cohort.