Linear discriminant analysis on electrocardiogram achieved classification of cardiac involvement status in amyloid light-chain amyloidosis

Abstract

ObjectivesCardiac amyloidosis (CA) is the most crucial determinant of amyloid light-chain (AL) amyloidosis patients' prognosis. We attempted cardiac involvement prediction by 12‑lead electrocardiograph (ECG) and echocardiography (UCG) in AL amyloidosis patients. Materials and methodsFifty patients with histologically confirmed AL amyloidosis underwent gadolinium-enhanced magnetic resonance imaging (Gd-MRI), and CA was assessed using late gadolinium enhancement. ECG and UCG parameters were measured on admission. Fisher's linear discriminant analysis was used to create a model for predicting CA using the ECG and UCG parameters. ResultsPrediction by five ECG parameters [QTc(B), QRS-T-angle, III-QRS, aVF-QRS, and V3-R] showed the best performance. Average sensitivity and specificity in the modeling sets, utilizing a linear discriminator based on these five variables, were 99.2 % and 96.8 % and in validation sets, 94.2 % and 90.3 %, respectively. In addition, we tested this model on an additional 26-patient cohort and survival analysis using the Kaplan-Meier method, and significant differences between CA positively predicted and negatively predicted patients were observed. ConclusionHere, we suggest the application of a condensed classical multivariate statistical technique for the diagnosis of CA. It can be used as a guide to invasive endomyocardial biopsy for those in whom Gd-MRI is contraindicated and as a guide for repeat Gd-MRI in follow-up of AL amyloidosis.