QTNet: Predicting Drug-Induced QT Prolongation With Artificial Intelligence-Enabled Electrocardiograms.
JACC Clin Electrophysiol
; 10(5): 956-966, 2024 May.
Article
in En
| MEDLINE
| ID: mdl-38703162
ABSTRACT
BACKGROUND:
Prediction of drug-induced long QT syndrome (diLQTS) is of critical importance given its association with torsades de pointes. There is no reliable method for the outpatient prediction of diLQTS.OBJECTIVES:
This study sought to evaluate the use of a convolutional neural network (CNN) applied to electrocardiograms (ECGs) to predict diLQTS in an outpatient population.METHODS:
We identified all adult outpatients newly prescribed a QT-prolonging medication between January 1, 2003, and March 31, 2022, who had a 12-lead sinus ECG in the preceding 6 months. Using risk factor data and the ECG signal as inputs, the CNN QTNet was implemented in TensorFlow to predict diLQTS.RESULTS:
Models were evaluated in a held-out test dataset of 44,386 patients (57% female) with a median age of 62 years. Compared with 3 other models relying on risk factors or ECG signal or baseline QTc alone, QTNet achieved the best (P < 0.001) performance with a mean area under the curve of 0.802 (95% CI 0.786-0.818). In a survival analysis, QTNet also had the highest inverse probability of censorship-weighted area under the receiver-operating characteristic curve at day 2 (0.875; 95% CI 0.848-0.904) and up to 6 months. In a subgroup analysis, QTNet performed best among males and patients ≤50 years or with baseline QTc <450 ms. In an external validation cohort of solely suburban outpatient practices, QTNet similarly maintained the highest predictive performance.CONCLUSIONS:
An ECG-based CNN can accurately predict diLQTS in the outpatient setting while maintaining its predictive performance over time. In the outpatient setting, our model could identify higher-risk individuals who would benefit from closer monitoring.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Long QT Syndrome
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Artificial Intelligence
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Neural Networks, Computer
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Electrocardiography
Limits:
Adult
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Aged
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Female
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Humans
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Male
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Middle aged
Language:
En
Journal:
JACC Clin Electrophysiol
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JACC Clin. Electrophysiol
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JACC. Clinical electrophysiology (Online)
Year:
2024
Type:
Article