Why the study?
In heart failure patients undergoing diuresis, electrolyte and fluid shifts can alter R and T waves, making the T:R ratio dynamically variable and potentially affecting S-ICD eligibility.
Does the T:R ratio differ between heart failure patients undergoing diuresis and patients with structurally normal hearts when analyzed using a deep learning model?
Observational (n=21)
Does the T:R ratio differ between heart failure patients undergoing diuresis and patients with structurally normal hearts when analyzed using a deep learning model?
T:R ratios are higher and fluctuate more in heart failure patients undergoing diuresis, suggesting that deep learning models could improve S-ICD eligibility screening and potentially reduce inappropriate shocks.
Answers are grounded in this paper and cite their sources.
Dynamic T:R ratios during diuresis may alter S-ICD eligibility in HF; hypothesis-generating for optimal screening timing.
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.
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ElRefai et al. (2022) conducted an observational in Heart failure (n=21). Heart failure undergoing diuresis vs. Structurally normal hearts was evaluated on Mean T:R ratio (p=< .001).
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