Usefulness of the 2-year iodine-123 metaiodobenzylguanidine-based risk model for post-discharge risk stratification of patients with acute decompensated heart failure

Cardiac sympathetic activity and lethal arrhythmic events: insight into bell-shaped relationship between 123I-meta-iodobenzylguanidine activity and event rates

BACKGROUND

123I-meta-iodobenzylguanidine (mIBG) has been applied to patients with chronic heart failure (CHF). However, the relationship between 123I-mIBG activity and lethal arrhythmic events (ArE) is not well defined. This study aimed to determine this relationship in Japanese and European cohorts.

RESULTS

We calculated heart-to-mediastinum (H/M) count ratios and washout rates (WRs) of 827 patients using planar 123I-mIBG imaging. We defined ArEs as sudden cardiac death, arrhythmic death, and potentially lethal events such as sustained ventricular tachycardia, cardiac arrest with resuscitation, and appropriate implantable cardioverter defibrillator (ICD) discharge, either from a single ICD or as part of a cardiac resynchronization therapy device (CRTD). We analyzed the incidence of ArE with respect to H/M ratios, WRs and New York Heart Association (NYHA) functional classes among Japanese (J; n = 581) and European (E; n = 246) cohorts. We also simulated ArE rates versus H/M ratios under specific conditions using a machine-learning model incorporating 13 clinical variables. Consecutive patients with CHF were selected in group J, whereas group E comprised candidates for cardiac electronic devices. Groups J and E mostly comprised patients with NYHA functional classes I/II (95%) and II/III (91%), respectively, and 21% and 72% were respectively implanted with ICD/CRTD devices. The ArE rate increased with lower H/M ratios in group J, but the relationship was bell-shaped, with a high ArE rate within the intermediate H/M range, in group E. This bell-shaped curve was also evident in patients with NYHA classes II/III in the combined J and E groups, particularly in those with a high (> 15%) mIBG WR and with ischemic, but not in those with non-ischemic etiologies. Machine learning-based prediction of ArE risk aligned with these findings, indicating a bell-shaped curve in NYHA class II/III but not in class I.

CONCLUSIONS

The relationship between cardiac 123I-mIBG activity and lethal arrhythmic events is influenced by the background of patients. The bell-shaped relationship in NYHA classes II/III, high WR, and ischemic etiology likely aids in identifying patients at high risk for ArEs.

Validation of a Five-Year Prognostic Model Using 123I-metaiodobenzylguanidine Scintigraphy in Patients with Heart Failure

Background: 123I-metaiodobenzylguanidine (MIBG) scintigraphy evaluates the severity and prognosis of patients with heart failure. A prognostic model has been proposed using a multicenter study data of 123I-MIBG scintigraphy. We evaluated the usefulness of the model using a database. Methods: The study included 208 patients with noncompensated heart failure requiring hospitalization. 123I-MIBG scintigraphy and echocardiography were performed predischarge and 6 months postdischarge. The 5-year mortality rate was calculated by the model and classified into tertiles. Results: In 208 patients, 56 cardiac deaths occurred within the observation period (median, 4.83 years). In the evaluation of predischarge parameters, the predicted 5-year mortality was 15.5% ± 5.0%, 33.5% ± 3.9%, and 51.2% ± 8.2%, and 11 (16.2%), 18 (27.3%), and 27 (36.5%) cardiac deaths occurred in groups 1, 2, and 3, respectively. At the 6-month postdischarge evaluation, the estimated mortality was 8.2% ± 2.2%, 18.5% ± 4.8%, and 43.0% ± 12.1%, and 6 (9.4%), 21 (29.2%), and 29 (40.3%) cardiac deaths occurred, respectively. The predischarge Kaplan-Meier survival analysis showed significant difference between groups 1 and 3 (P value 0.014). Moreover, the 6-month postdischarge evaluation showed significant difference between group 1 and 2, and between groups 1 and 3 (P value 0.016, <0.001, respectively). For groups 1 and 3, the 6-month postdischarge difference was more significant than the predischarge difference (Chi-square 16.7 and 8.1, respectively). Conclusions: The prognostic model using 123I-MIBG scintigraphy was useful in predicting mortality risk in patients with heart failure. The estimated mortality at 6 months postdischarge was more useful than the predischarge estimation for heart failure hospitalization.

123I-MIBG imaging in heart failure: impact of comorbidities on cardiac sympathetic innervation

PURPOSE

Heart failure (HF) is a primary cause of morbidity and mortality worldwide, with significant impact on life quality and extensive healthcare costs. Assessment of myocardial sympathetic innervation function plays a central role in prognosis assessment in HF patients. The aim of this review is to summarize the most recent evidence regarding the clinical applications of iodine-123 metaiodobenzylguanidine (¹²³I-MIBG) imaging in patients with HF and related comorbidities.

METHODS

A comprehensive literature search was conducted on PubMed and Web of Science databases. Articles describing the impact of ¹²³I-MIBG imaging on HF and related comorbidities were considered eligible for the review.

RESULTS

We collected several data reporting that ¹²³I-MIBG imaging is a safe and non-invasive tool to evaluate dysfunction of cardiac sympathetic neuronal function and to assess risk stratification in HF patients. HF is frequently associated with comorbidities that may affect cardiac adrenergic innervation. Furthermore, HF is frequently associated with comorbidities and chronic conditions, such as diabetes, obesity, kidney disease and others, that may affect cardiac adrenergic innervation.

CONCLUSION

Comorbidities and chronic conditions lead to more severe impairment of sympathetic nervous system in patients with HF, with a negative impact on disease progression and outcome. Cardiac imaging with ¹²³I-MIBG can be a useful tool to reduce morbidity and prevent adverse events in HF patients.

Nuclear Cardiology Data Analyzed Using Machine Learning

Machine learning has become popular in clinical practice, and the amount of research that uses artificial intelligence is rapidly increasing. In contrast to conventional statistical and rule-based methods, machine learning creates algorithms based only on combinations of input and output databases. Basic understanding of the internal workings of artificial intelligence, its structures and need for appropriate databases, as well as its strengths and weaknesses is important for efficient machine learning application. The cardiological applications of machine learning include diagnosing coronary artery diseases and heart failure, and examples are addressed herein. A preliminary application of machine learning to a ¹²³I-metaiodobenzylguanidine-based risk model appears promising, and further studies using similar approaches are anticipated. Nuclear medicine physicians and cardiologists should play key roles in developing machine learning-based methods to ensure practical and reliable decisions.