S'abonner

A multi-modal diagnostic model improves detection of cardiac amyloidosis among patients with diagnostic confirmation by cardiac biopsy - 23/01/21

Doi : 10.1016/j.ahj.2020.11.006 
Kathleen W. Zhang, MD a, , Ray Zhang, MD PhD b, Elena Deych, MS c, Keith E. Stockerl-Goldstein, MD d, John Gorcsan, MD c, Daniel J. Lenihan, MD a
a Cardio-Oncology Center of Excellence, Washington University School of Medicine, St. Louis, MO 
b Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 
c Cardiovascular Division, Washington University School of Medicine, St. Louis, MO 
d Division of Oncology, Section of Bone Marrow Transplantation, Washington University School of Medicine, St. Louis, MO 

Reprint requests: Kathleen W. Zhang, MD, Cardio-Oncology Center of Excellence, Cardiovascular Division, Washington University School of Medicine, Campus Box 8086, 660 South Euclid Avenue, St. Louis, MO 63110-1093.Cardio-Oncology Center of ExcellenceCardiovascular DivisionWashington University School of MedicineCampus Box 8086, 660 South Euclid AvenueSt. LouisMO63110-1093

Résumé

Background

Timely recognition of cardiac amyloidosis is clinically important, but the diagnosis is frequently delayed.

Objectives

We sought to identify a multi-modality approach with the highest diagnostic accuracy in patients evaluated by cardiac biopsy, the diagnostic gold standard.

Methods

Consecutive patients (N = 242) who underwent cardiac biopsy for suspected amyloidosis within an 18-year period were retrospectively identified. Cardiac biomarker, ECG, and echocardiography results were examined for correlation with biopsy-proven disease. A prediction model for cardiac amyloidosis was derived using multivariable logistic regression.

Results

The overall cohort was characterized by elevated BNP (median 727 ng/mL), increased left ventricular wall thickness (IWT; median 1.7 cm), and reduced voltage-to-mass ratio (median 0.06 mm/[g/m2]). One hundred and thirteen patients (46%) had either light chain (n = 53) or transthyretin (n = 60) amyloidosis by cardiac biopsy. A prediction model including age, relative wall thickness, left atrial pressure by E/e’, and low limb lead voltage (<0.5 mV) showed good discrimination for cardiac amyloidosis with an optimism-corrected c-index of 0.87 (95% CI 0.83-0.92). The diagnostic accuracy of this model (79% sensitivity, 84% specificity) surpassed that of traditional screening parameters, such as IWT in the absence of left ventricular hypertrophy on ECG (98% sensitivity, 20% specificity) and IWT with low limb lead voltage (49% sensitivity, 91% specificity).

Conclusion

Among patients with an advanced infiltrative cardiomyopathy phenotype, traditional biomarker, ECG, and echocardiography-based screening tests have limited individual diagnostic utility for cardiac amyloidosis. A prediction algorithm including age, relative wall thickness, E/e’, and low limb lead voltage improves the detection of cardiac biopsy-proven disease.

Le texte complet de cet article est disponible en PDF.

Graphical abstract




Image, graphical abstract

Le texte complet de cet article est disponible en PDF.

Abbreviations : AL, ATTR, AV, BNP, ECG, HFpEF, IQR, IWT, LGE, LS, LV, LVEF, LVIDd, MRI, PWT, RWT, TTE


Plan


 Relationships with Industry: Dr. Zhang has received consulting fees from Eidos Therapeutics. Dr. Stockerl-Goldstein has received consulting fees from Celgene and grants from Millenium Pharmaceuticals, Janssen Pharmaceuticals, BioLineRx, Pfizer, and GlaxoSmithKline. Dr. Gorcsan has received research funding from GE Healthcare, TomTec, Hitachi, and Canon. Dr. Lenihan has received consulting fees from Lilly, Roche, Pfizer, Prothena, and Acorda, and research funding from Myocardial Solutions. All other authors have no corporate relationships to disclose.
 Disclosure: None of the authors report conflicts of interest.


© 2020  Elsevier Inc. Tous droits réservés.
Ajouter à ma bibliothèque Retirer de ma bibliothèque Imprimer
Export

    Export citations

  • Fichier

  • Contenu

Vol 232

P. 137-145 - février 2021 Retour au numéro
Article précédent Article précédent
  • Sex and race differences in safety and effectiveness of the HEART pathway accelerated diagnostic protocol for acute chest pain
  • Anna C. Snavely, Nella Hendley, Jason P. Stopyra, Kristin M. Lenoir, Brian J. Wells, David M. Herrington, Brian C. Hiestand, Chadwick D. Miller, Simon A. Mahler
| Article suivant Article suivant
  • Incidence of acute coronary syndrome during national lock-down: Insights from nationwide data during the Coronavirus disease 2019 (COVID-19) pandemic
  • Lauge Østergaard, Jawad Haider Butt, Kristian Kragholm, Morten Schou, Matthew Phelps, Rikke Sørensen, Morten Lamberts, Gunnar Gislason, Christian Torp-Pedersen, Lars Køber, Emil L. Fosbøl

Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.

Déjà abonné à cette revue ?

Mon compte


Plateformes Elsevier Masson

Déclaration CNIL

EM-CONSULTE.COM est déclaré à la CNIL, déclaration n° 1286925.

En application de la loi nº78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés, vous disposez des droits d'opposition (art.26 de la loi), d'accès (art.34 à 38 de la loi), et de rectification (art.36 de la loi) des données vous concernant. Ainsi, vous pouvez exiger que soient rectifiées, complétées, clarifiées, mises à jour ou effacées les informations vous concernant qui sont inexactes, incomplètes, équivoques, périmées ou dont la collecte ou l'utilisation ou la conservation est interdite.
Les informations personnelles concernant les visiteurs de notre site, y compris leur identité, sont confidentielles.
Le responsable du site s'engage sur l'honneur à respecter les conditions légales de confidentialité applicables en France et à ne pas divulguer ces informations à des tiers.


Tout le contenu de ce site: Copyright © 2024 Elsevier, ses concédants de licence et ses contributeurs. Tout les droits sont réservés, y compris ceux relatifs à l'exploration de textes et de données, a la formation en IA et aux technologies similaires. Pour tout contenu en libre accès, les conditions de licence Creative Commons s'appliquent.