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

Kathleen W. Zhang, Ray Zhang, Elena Deych, Keith E. Stockerl-Goldstein, John Gorcsan, Daniel J. Lenihan

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)137-145
Number of pages9
JournalAmerican Heart Journal
Volume232
DOIs
StatePublished - Feb 2021

All Science Journal Classification (ASJC) codes

  • Cardiology and Cardiovascular Medicine

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