Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis.

Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 ( = 31) with and G2 ( = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. : The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models.