Why the study?
Cardiac sympathetic dysfunction is linked to cardiac mortality in chronic heart failure, but whether machine learning incorporating 123I-MIBG could differentially predict risks of life-threatening arrhythmic events versus heart failure death remained to be analyzed.
Does a machine learning-based risk model using 123I-MIBG and clinical variables differentially predict modes of cardiac death (arrhythmic events vs heart failure death) in patients with chronic heart failure?
Does a machine learning-based risk model using 123I-MIBG and clinical variables differentially predict modes of cardiac death (arrhythmic events vs heart failure death) in patients with chronic heart failure?
A machine learning model incorporating 123I-MIBG imaging and clinical variables can effectively differentiate the risk of heart failure death versus arrhythmic events, potentially guiding targeted device therapy.
Answers are grounded in this paper and cite their sources.
ML-MIBG models may differentiate arrhythmic from HF death risk in CHF; hypothesis-generating and requires prospective validation before any clinical use.
Cardiac sympathetic dysfunction is closely associated with cardiac mortality in patients with chronic heart failure (CHF). We analyzed the ability of machine learning incorporating 123 I-metaiodobenzylguanidine (MIBG) to differentially predict risk of life-threatening arrhythmic events (ArE) and heart failure death (HFD). 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 123 I-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). A risk model based on machine learning using clinical variables and 123 I-MIBG differentially predicted ArE and HFD as causes of cardiac death.
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Nakajima et al. (2020) studied this question.
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