A deep learning convolutional neural network applied to the surface ECG predicted plasma dofetilide concentration with a stronger correlation (r = 0.85) than a linear model based on QTc alone (r = 0.64).
Does a deep learning algorithm applied to the surface ECG improve the prediction of dofetilide plasma concentration compared to QTc interval alone in healthy subjects?
A deep learning algorithm applied to the surface ECG predicts dofetilide plasma concentration more accurately than the QTc interval alone.
Absolute Event Rate: 0.85% vs 0.64%
BACKGROUND: Dofetilide is an effective antiarrhythmic medication for rhythm control in atrial fibrillation, but carries a significant risk of pro-arrhythmia and requires meticulous dosing and monitoring. The cornerstone of this monitoring, measurement of the QT/QTc interval, is an imperfect surrogate for plasma concentration, efficacy, and risk of pro-arrhythmic potential. OBJECTIVE: The aim of our study was to test the application of a deep learning approach (using a convolutional neural network) to assess morphological changes on the surface ECG (beyond the QT interval) in relation to dofetilide plasma concentrations. METHODS: We obtained publically available serial ECGs and plasma drug concentrations from 42 healthy subjects who received dofetilide or placebo in a placebo-controlled cross-over randomized controlled clinical trial. Three replicate 10-s ECGs were extracted at predefined time-points with simultaneous measurement of dofetilide plasma concentration We developed a deep learning algorithm to predict dofetilide plasma concentration in 30 subjects and then tested the model in the remaining 12 subjects. We compared the deep leaning approach to a linear model based only on QTc. RESULTS: Fourty two healthy subjects (21 females, 21 males) were studied with a mean age of 26.9 ± 5.5 years. A linear model of the QTc correlated reasonably well with dofetilide drug levels (r = 0.64). The best correlation to dofetilide level was achieved with the deep learning model (r = 0.85). CONCLUSION: This proof of concept study suggests that artificial intelligence (deep learning/neural network) applied to the surface ECG is superior to analysis of the QT interval alone in predicting plasma dofetilide concentration.
Attia et al. (Wed,) conducted a other in Healthy subjects (n=42). Deep learning (convolutional neural network) analysis of surface ECG vs. Linear model based on QTc was evaluated on Correlation (r) with dofetilide plasma concentration. A deep learning convolutional neural network applied to the surface ECG predicted plasma dofetilide concentration with a stronger correlation (r = 0.85) than a linear model based on QTc alone (r = 0.64).
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