Titre
Prediction of hemodynamic severity of coarctation by magnetic resonance imaging.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Muzzarelli, S.
Auteure/Auteur
Meadows, A.K.
Auteure/Auteur
Ordovas, K.G.
Auteure/Auteur
Hope, M.D.
Auteure/Auteur
Higgins, C.B.
Auteure/Auteur
Nielsen, J.C.
Auteure/Auteur
Geva, T.
Auteure/Auteur
Meadows, J.J.
Auteure/Auteur
Liens vers les unités
ISSN
1879-1913
Statut éditorial
Publié
Date de publication
2011
Volume
108
Numéro
9
Première page
1335
Dernière page/numéro d’article
1340
Langue
anglais
Résumé
A published formula containing minimal aortic cross-sectional area and the flow deceleration pattern in the descending aorta obtained by cardiovascular magnetic resonance predicts significant coarctation of the aorta (CoA). However, the existing formula is complicated to use in clinical practice and has not been externally validated. Consequently, its clinical utility has been limited. The aim of this study was to derive a simple and clinically practical algorithm to predict severe CoA from data obtained by cardiovascular magnetic resonance. Seventy-nine consecutive patients who underwent cardiovascular magnetic resonance and cardiac catheterization for the evaluation of native or recurrent CoA at Children's Hospital Boston (n = 30) and the University of California, San Francisco (n = 49), were retrospectively reviewed. The published formula derived from data obtained at Children's Hospital Boston was first validated from data obtained at the University of California, San Francisco. Next, pooled data from the 2 institutions were analyzed, and a refined model was created using logistic regression methods. Finally, recursive partitioning was used to develop a clinically practical prediction tree to predict transcatheter systolic pressure gradient ≥ 20 mm Hg. Severe CoA was present in 48 patients (61%). Indexed minimal aortic cross-sectional area and heart rate-corrected flow deceleration time in the descending aorta were independent predictors of CoA gradient ≥ 20 mm Hg (p <0.01 for both). A prediction tree combining these variables reached a sensitivity and specificity of 90% and 76%, respectively. In conclusion, the presented prediction tree on the basis of cutoff values is easy to use and may help guide the management of patients investigated for CoA.
PID Serval
serval:BIB_EFD87C4FFD42
PMID
Date de création
2011-12-15T13:44:51.622Z
Date de création dans IRIS
2025-05-21T07:17:07Z