Machine learning-based risk model using ¹²³I-metaiodobenzylguanidine to differentially predict modes of cardiac death in heart failure.

ABSTRACT:

Background

Cardiac sympathetic dysfunction is closely associated with cardiac mortality in patients with chronic heart failure (CHF). We analyzed the ability of machine learning incorporating ¹²³I-metaiodobenzylguanidine (MIBG) to differentially predict risk of life-threatening arrhythmic events (ArE) and heart failure death (HFD).

Methods and results

A model was created based on patients with documented 2-year outcomes of CHF (n = 526; age, 66 ± 14 years). Classifiers were trained using 13 variables including age, gender, NYHA functional class, left ventricular ejection fraction and planar ¹²³I-MIBG heart-to-mediastinum ratio (HMR). ArE comprised arrhythmic death and appropriate therapy with an implantable cardioverter defibrillator. The probability of

SUBMITTER: Nakajima K 

PROVIDER: S-EPMC8873155 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature