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Updated: Jan 8, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Multimodal Machine Learning Integrating N-13 Ammonia PET and Clinical Variables Predicts Major Adverse Cardiac

Ryo Mikurino^1,2, Michinobu Nagao³, Masateru Kawakubo⁴

¹Department of Health Sciences, School of Medicine, Kyushu University, Fukuoka, Japan.

Journal of Imaging Informatics in Medicine

|December 19, 2025

Summary

Machine learning models using static myocardial perfusion positron emission tomography (PET) images and clinical data can predict major adverse cardiac events (MACE). This approach offers a valuable alternative to complex dynamic PET scans for cardiac risk assessment.

Keywords:

Major adverse cardiac event Multimodal machine learning Myocardial flow reserve Positron emission tomography

Related Experiment Videos

Last Updated: Jan 8, 2026

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics

Published on: November 29, 2024

Area of Science:

Cardiovascular Imaging
Machine Learning in Medicine
Nuclear Cardiology

Background:

Myocardial perfusion positron emission tomography (PET) often requires burdensome dynamic and static stress/rest acquisitions.
The short half-life of N-13 ammonia complicates scanning protocols.
Predicting major adverse cardiac events (MACE) is crucial for patient management.

Purpose of the Study:

To investigate the utility of combining static PET-derived images with clinical parameters using machine learning for MACE prediction.
To explore an expanded application of ammonia PET without the need for dynamic scanning.
To compare the predictive performance against a standard myocardial flow reserve (MFR) threshold.

Main Methods:

A cohort of 386 patients was analyzed with a mean follow-up of 345 days.
Stratified fivefold cross-validation was employed for model evaluation.
A logistic regression model was trained using key features like age, dyslipidemia, and resting end-diastolic volume, prioritizing features accounting for over 50% of cumulative MACE prediction importance.

Main Results:

The developed machine learning model achieved an accuracy of 0.74 ± 0.06, sensitivity of 0.74 ± 0.23, and specificity of 0.74 ± 0.07 on an independent test set.
In comparison, simple MFR < 2.0 prediction yielded lower accuracy (0.58 ± 0.05) and specificity (0.56 ± 0.05), despite similar sensitivity (0.77 ± 0.13).

Conclusions:

A multimodal machine learning approach integrating static PET data and clinical factors can effectively predict MACE.
This method presents a clinically useful alternative to conventional dynamic PET scanning protocols.
The findings suggest a streamlined approach to cardiac risk stratification using readily available data.