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

Abstract

Abstract 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.

Keywords

• artificial intelligence
• heart failure
• machine learning
• subcutaneous implantable cardiac defibrillator
• sudden cardiac death