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 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).

Conclusion: A risk model based on machine learning using clinical variables and ¹²³I-MIBG differentially predicted ArE and HFD as causes of cardiac death.

Keywords: Risk stratification; arrhythmia; artificial intelligence; cardiac mortality; neuroimaging.

Figure 1

Receiver operating characteristics (ROC) curves of fourfold cross-validation using various machine learning methods

Figure 2

Probability of heart failure death (HFD), arrhythmic events (ArE), survival (no events) against ¹²³I-MIBG heart-to-mediastinum ratio (HMR). The probabilities were calculated by a three-category classifier. Selected conditions of the variables are shown in blue

Figure 3

Probability of heart failure death and arrhythmic events vs ¹²³I-MIBG heart-to-mediastinum ratio (HMR) in patients with NYHA classes I to IV. Dotted line: Decreased reliability because no patients with NYHA class IV had HMR > 2.5

Figure 4

Fraction of arrhythmic event (ArE) probability divided by heart failure death (HFD) probability in patients with NYHA classes I, II, III, and IV vs ¹²³I-MIBG heart-to-mediastinum ratio (HMR)

Figure 5

Probabilities of heart failure death and arrhythmic events plotted against ¹²³I-MIBG heart-to-mediastinum ratio (HMR) in patients aged 40, 60 and 80 years (A), male and female (B) patients with different LVEF (C) and BNP category (D)

Figure 6

Calibration plots for all events (A), heart failure death (B) and arrhythmic events (C). Number of patients in each bin and actual number of events shown at bottom

Figure 7

Patients with high and low probabilities of HFD and ArE, and actual incidence of HFD and ArE in each group. Estimated probability groups are as follows: HFD > 15% and ArE ≤ 11% (A), ArE > 11% and HFD ≤ 15% (B), both HFD > 15% and ArE > 11% (C), and HFD ≤ 15% and ArE ≤ 11% (D)