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

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