Dataset Information

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.

SUBMITTER: Nakajima K

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

REPOSITORIES: biostudies-literature

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Publications

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

Nakajima Kenichi K Nakata Tomoaki T Doi Takahiro T Tada Hayato H Maruyama Koji K

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology 20200514 1

<h4>Background</h4>Cardiac sympathetic dysfunction is closely associated with cardiac mortality in patients with chronic heart failure (CHF). We analyzed the ability of machine learning incorporating <sup>123</sup>I-metaiodobenzylguanidine (MIBG) to differentially predict risk of life-threatening arrhythmic events (ArE) and heart failure death (HFD).<h4>Methods and results</h4>A model was created based on patients with documented 2-year outcomes of CHF (n = 526; age, 66 ± 14 years). Classifiers ...[more]

PMID: 32410060

Similar Datasets

Project description:BackgroundSince three-dimensional segmentation of cardiac region in 123I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with 123I-MIBG single photon emission computed tomography (SPECT) imaging, to calculate heart counts and washout rates (WR) automatically and to compare with conventional quantitation based on planar imaging.MethodsWe assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late 123I-MIBG planar and SPECT imaging. The CNN was initially trained to individually segment the lungs and liver on early and late SPECT images. The segmentation masks were aligned, and then, the CNN was trained to directly segment the heart, and all models were evaluated using fourfold cross-validation. The CNN-based average heart counts and WR were calculated and compared with those determined using planar parameters. The CNN-based SPECT and conventional planar heart counts were corrected by physical time decay, injected dose of 123I-MIBG, and body weight. We also divided WR into normal and abnormal groups from linear regression lines determined by the relationship between planar WR and CNN-based WR and then analyzed agreement between them.ResultsThe CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R2 = 0.862, 0.827, and 0.729, p < 0.0001, respectively). The CNN-based and planar WRs also correlated with and without background correction and WR based on heart-to-mediastinum ratios of R2 = 0.584, 0.568 and 0.507, respectively (p < 0.0001). Contingency table findings of high and low WR (cutoffs: 34% and 30% for planar and SPECT studies, respectively) showed 87.2% agreement between CNN-based and planar methods.ConclusionsThe CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation.

Dataset Information

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

Background

Methods and results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

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

Background

Methods and results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

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