Prognostic value of stress phase entropy for predicting major cardiac events in patients with known or suspected stable coronary artery disease.

Abstract

There are no large-scale reports on the prediction of major cardiac events (MCEs) and risk stratification using phase entropy, a measure of left ventricular (LV) dyssynchrony, in Japanese patients with known or suspected coronary artery disease (CAD). We retrospectively investigated 5541 patients with known or suspected CAD who underwent rest ²⁰¹Tl and stress ^99mTc-tetrofosmin electrocardiogram (ECG)-gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) and followed them for three years to confirm their prognosis. We excluded patients undergoing revascularization within 3 months before and after the SPECT and those with non-sinus rhythm. The composite endpoint was the onset of MCEs, consisting of cardiac death, non-fatal myocardial infarction (MI), and severe heart failure requiring hospitalization. Poststress phase standard deviation (SD), bandwidth, and entropy were calculated by phase analysis using Heart Risk View-F software. During follow-up, 401 patients experienced MCEs: cardiac death (n = 98), non-fatal MI (n = 76), and severe heart failure (n = 227). Multivariate analysis showed that age, diabetes, estimated glomerular filtration rate (eGFR), stress LV ejection fraction (EF), and stress phase entropy were independent predictors of MCEs. Kaplan-Meier analysis showed significant risk stratification between each group in the stress phase entropy quartile. The global chi-squared value predicting MCE onset was significantly improved in the model with summed stress score and LVEF plus entropy but was comparable in the model with added bandwidth or phase SD. Stress phase entropy was a strong predictor of MCEs in Japanese patients with CAD (known or suspected) and was considered the most useful left ventricular dyssynchrony index to improve risk stratification over prediction by perfusion and LVEF assessment alone.