Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study

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...

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Bibliographic Details
Published in:PloS one 2018-08, Vol.13 (8), p.e0201059-e0201059
Main Authors: Attia, Zachi I, Sugrue, Alan, Asirvatham, Samuel J, Ackerman, Michael J, Kapa, Suraj, Friedman, Paul A, Noseworthy, Peter A
Format: Article
Language:English
Subjects:
Antiarrhythmics
Arrhythmia
Artificial intelligence
Artificial neural networks
Biology and Life Sciences
Blood plasma
Cardiac arrhythmia
Cardiology
Clinical medicine
Clinical trials
Computer and Information Sciences
Deep learning
Dofetilide
Dosage and administration
Drug dosages
EKG
Electrocardiography
Females
Fibrillation
Heart
Laboratories
Learning
Machine learning
Males
Mathematical models
Measurement techniques
Medicine and Health Sciences
Model testing
Monitoring
Neural networks
Open source software
Pediatrics
Plasma
Research and Analysis Methods
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Summary: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. 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. 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. 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). 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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0201059