What is the clinical evidence from this study?

Study design: Other. Population: Healthy subjects (n=42). Intervention: Deep learning (convolutional neural network) analysis of surface ECG vs. Linear model based on QTc. Primary outcome: Correlation (r) with dofetilide plasma concentration.

August 22, 2018Open Access

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

ZAZachi I. AttiaElectrophysiology ASAlan SugrueMayo Clinic SASamuel J. AsirvathamElectrophysiology

Key Result

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).

Structured PICO

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?

Population

42 healthy subjects (21 females, 21 males, mean age 26.9 ± 5.5 years) who received dofetilide or placebo in a prior cross-over randomized controlled clinical trial

Intervention

Deep learning algorithm (convolutional neural network) applied to surface ECGs

Comparator

Linear model based only on QTc interval

Outcome

Prediction of dofetilide plasma concentration (correlation coefficient)surrogate

A deep learning algorithm applied to the surface ECG predicts dofetilide plasma concentration more accurately than the QTc interval alone.

Main Result

Absolute Event Rate: 0.85% vs 0.64%

Limitations

Population includes only a small sample of healthy volunteers
Did not have data to permit assessment of arrhythmic risk
Used Bazett's formula to calculate QTc, which may overestimate repolarization at higher heart rates and underestimate at lower heart rates
Unable to assess the relationship between genetic variants and QT drug-response
Application of this data to those on long-term therapy with dofetilide is unknown

Abstract

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.

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