Journal of cardiology · 2023
Linear discriminant analysis on electrocardiogram achieved classification of cardiac involvement status in amyloid light-chain amyloidosis.
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Iijima Takashi, Sawa Naoki, Wake Atsushi, Kono Kei, Kinowaki Keiichi, Ubara Yoshifumi, Ohashi Kenichi
Аннотация
OBJECTIVES: Cardiac 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 METHODS: Fifty 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.
RESULTS: Prediction 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.
CONCLUSION: Here, 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.
MATERIALS AND METHODS: Fifty 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.
RESULTS: Prediction 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.
CONCLUSION: Here, 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.