J Breath Res. 2026 Jan 6. doi: 10.1088/1752-7163/ae33e0. Online ahead of print.
ABSTRACT
Despite major progress in diagnosis and treatment, cardiovascular disease (CVD) continues to be the leading cause of death worldwide, responsible for roughly 19.8 million lives lost each year. A key challenge in preventive cardiology is still the early detection of those at elevated risk of serious heart complications. Aims: Assess the ability of the machine learning model to stratify CVD risk using exhaled breath analysis. Materials and methods: A single-center study involved 80 participants with vs. without stress-induced myocardial perfusion defect. All participants underwent a single resting breath sample collection in PTR-TOF-MS-1000, single blood sample intake, and stress computed tomography myocardial perfusion imaging with vasodilation test. Statistical analyses were performed using Statistica 12 (StatSoft, Inc., 2014), IBM SPSS Statistics v29.0.1.1 (IBM Corp., 2024). The threshold for statistical significance was p < 0.05. Machine learning models were developed using Google Colab with Python 3. Results: The gradient-boosting model demonstrated the best performance and was therefore selected for further evaluation. The model showed an AUC of 0.77 [95% CI; 0.4976 - 1.0000] to differentiate participants with low CVD risk, moderate risk 0.55 [95% CI; 0.3345 - 0.7875], and high risk 0.66 [95% CI; 0.3765 - 0.8661]. Conclusion: The gradient boosting machine learning model provides initial evidence that rest exhaled breath analysis can differentiate cardiovascular risk strata through identifiable concentration patterns of specific volatile organic compounds. However, substantial challenges remain regarding model performance and the confounding effects of class imbalance within a limited sample. .
PMID:41494207 | DOI:10.1088/1752-7163/ae33e0