Abstract
BackgroundCardiac 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 ResultsA 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 ArE and HFD at 2 years was separately calculated based on appropriate classifiers. The probability of HFD significantly increased as HMR decreased when any variables were combined. However, the probability of arrhythmic events was maximal when HMR was intermediate (1.5-2.0 for patients with NYHA class III). Actual rates of ArE were 3% (10/379) and 18% (27/147) in patients at low- (≤ 11%) and high- (> 11%) risk of developing ArE (P < .0001), respectively, whereas those of HFD were 2% (6/328) and 49% (98/198) in patients at low-(≤ 15%) and high-(> 15%) risk of HFD (P < .0001). ConclusionA risk model based on machine learning using clinical variables and 123I-MIBG differentially predicted ArE and HFD as causes of cardiac death.
Highlights
Chronic heart failure (CHF) has become a major public health burden associated with aging of the global population.[1]
A risk model based on machine learning using clinical variables and 123IMIBG differentially predicted arrhythmic events (ArE) and heart failure death (HFD) as causes of cardiac death. (J Nucl Cardiol 2020)
During a 2-year followup, 137 (26%) patients succumbed to cardiac death (HFD, n = 105 [20%]; ArE, n = 32 [6%])
Summary
Chronic heart failure (CHF) has become a major public health burden associated with aging of the global population.[1] Despite significant prognostic improvements due to recent pharmacological therapies and cardiac devices, morbidity and mortality rates remain high; nearly 50% of patients with CHF do not survive beyond 5 years after diagnosis. The conventional prognostic biomarkers of CHF include New York Heart Association (NYHA) functional class, left ventricular ejection fraction (LVEF), blood b-type natriuretic peptide (BNP) or N-terminal proBNP (NT-ProBNP), and influential comorbidities such as diabetes, chronic kidney disease and hypertension.[2,3] a significant number of patients with CHF cannot benefit from contemporary therapeutic strategies because of exclusion criteria based on current guidelines of heart failure management, cardiac device therapies, or no or minimal responses to therapies. We analyzed the ability of machine learning incorporating 123I-metaiodobenzylguanidine (MIBG) to differentially predict risk of lifethreatening arrhythmic events (ArE) and heart failure death (HFD)
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