Abstract
Background
Chest pain is the most common symptom of aortic dissection (AD), but it is often confused
with other prevalent cardiopulmonary diseases. We aimed to develop deep-learning models
(DLMs) with electrocardiography (ECG) and chest x-ray (CXR) features to detect AD
and evaluate their performance.
Methods
This study included 43,473 patients in the emergency department (ED) between July
2012 and December 2019 for retrospective DLM development. A development cohort including
49,071 ED records (120 AD type A and 64 AD type B) was used to train DLMs for ECG
and CXR, and 9904 independent ED records (40 AD type A and 34 AD type B) were used
to validate DLM performance. Human-machine competitions of ECG and CXR were conducted.
Patient characteristics and laboratory results were used to enhance the diagnostic
accuracy. The DLM-enabled AD diagnostic process was prospectively evaluated in 25,885
ED visits.
Results
The area under the curves (AUCs) of the ECG and CXR models were 0.918 and 0.857 for
detecting AD in a human-machine competition, respectively, which were better than
those of the participating physicians. In the validation cohort, the AUCs of the integrated
model were 0.882, 0.960, and 0.813 in all AD, AD type A, and AD type B patients, respectively,
with a sensitivity of 100.0% and a specificity of 81.7% for AD type A. In patients
with chest pain and D-dimer tests, the DLM could predict more precisely, achieving
a positive predictive value of 62.5% in the prospective evaluation.
Conclusions
DLMs may serve as decision-supporting tools for identification of AD and facilitate
differential diagnosis in patients with acute chest pain.
Résumé
Contexte
La douleur thoracique est le symptôme le plus courant de la dissection aortique (DA),
mais celle-ci est souvent confondue avec d’autres maladies cardiopulmonaires fréquentes.
Notre objectif était de développer des modèles d’apprentissage profond (MAP) permettant
l’analyse de données d’électrocardiographie (ECG) et de radiographie pulmonaire en
vue de détecter la DA et d’évaluer leurs performances.
Méthodologie
Cette étude portait sur le développement rétrospectif de MAP à partir des données
recueillies chez 43 473 patients admis aux urgences entre juillet 2012 et décembre
2019. Une cohorte de développement comportant 49 071 dossiers des urgences (120 cas
de DA de type A et 64 cas de DA de type B) a servi à entraîner les MAP à l’analyse
des données d’ECG et de radiographie pulmonaire, et 9 904 dossiers des urgences indépendants
(40 cas de DA de type A et 34 cas de DA de type B) ont été utilisés pour valider les
performances des MAP. L’analyse des données d’ECG et de radiographie pulmonaire a
fait l’objet de compétitions entre humains et machines. Les caractéristiques des patients
et les résultats des analyses de laboratoire ont été utilisés pour améliorer la précision
du diagnostic. Le processus de diagnostic de la DA faisant appel aux MAP a été évalué
de manière prospective sur 25 885 consultations aux urgences.
Résultats
La surface sous la courbe (SSC) des modèles d’analyse des données d’ECG et de radiographie
pulmonaire était respectivement de 0,918 et 0,857 pour la détection de la DA dans
une compétition entre humains et machine, ce qui constituait un meilleur résultat
que celui obtenu par les médecins participants. Au sein de la cohorte de validation,
la SSC du modèle intégré était de 0,882 dans l’ensemble des cas de DA, de 0,960 dans
les cas de DA de type A et de 0,813 dans les cas de DA de type B, la sensibilité et
la spécificité ayant atteint respectivement 100,0 % et 81,7 % dans les cas de DA de
type A. Chez les patients présentant des douleurs thoraciques et dont le taux de D-dimères
avait été mesuré, les MAP pouvaient donner des prévisions plus précises, une valeur
prédictive positive de 62,5 % ayant été obtenue dans le cadre de l’évaluation prospective.
Conclusions
Les MAP peuvent servir d’outils d’aide à la décision dans le dépistage de la DA et
faciliter le diagnostic différentiel chez les patients présentant des douleurs thoraciques
aiguës.
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Article info
Publication history
Published online: October 03, 2021
Accepted:
September 27,
2021
Received:
May 26,
2021
Footnotes
See page 167 for disclosure information.
Identification
Copyright
© 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.
