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

Abstract

Timely recognition of cardiac amyloidosis is clinically important, but the diagnosis is frequently delayed. We sought to identify a multi-modality approach with the highest diagnostic accuracy in patients evaluated by cardiac biopsy, the diagnostic gold standard. 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. 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). 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.