Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis
Basheer Abdullah Marzoog1, Peter Chomakhidze1, Daria Gognieva1
1World-Class Research Center «Digital Biodesign and Personalized Healthcare», I.M. Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya Street, 119991 Moscow, Russia.
Insights
Analyzing exhaled volatile organic compounds (VOCs) shows promise for early ischemic heart disease (IHD) diagnosis. This breath analysis, using machine learning, offers higher accuracy than traditional stress tests.
Area of Science:
- Cardiology
- Biochemistry
- Medical Diagnostics
Background:
- Ischemic heart disease (IHD) is a leading global cause of mortality and morbidity, significantly impacting patient quality of life.
- Current diagnostic and therapeutic strategies for IHD, including primary prevention, are often insufficient, highlighting the need for improved early detection methods.
- Early diagnosis and management of IHD remain critical challenges in clinical practice.
Purpose of the Study:
- To investigate the potential of exhaled volatile organic compounds (VOCs) as biomarkers for detecting ischemic heart disease (IHD).
- To compare the diagnostic accuracy of VOC analysis with conventional stress testing methods for IHD.
- To explore the application of machine learning models in analyzing VOC profiles for IHD identification.
Main Methods:
- An observational study involving 80 participants (≥40 years) categorized into IHD and non-IHD groups based on stress computed tomography myocardial perfusion (CTP) imaging.
- Exhaled breath samples were collected at rest, immediately after bicycle ergometry, and three minutes post-exercise using PTR-TOF-MS-1000.
- LASSO regression with nested cross-validation was employed to associate VOCs with myocardial perfusion defects, utilizing statistical software R and Python.
Main Results:
- Exhaled VOC analysis demonstrated a high diagnostic accuracy for IHD, with a sensitivity of 83.9% and specificity of 77.6% (AUC 83.8%).
- In contrast, bicycle ergometry showed significantly lower diagnostic performance, with sensitivity of 48.4% and specificity of 53.1% (AUC 50.7%).
- Machine learning models effectively utilized VOC profiles to differentiate between individuals with and without IHD.
Conclusions:
- Exhaled breath analysis, reflecting metabolomic signatures and cellular homeostasis, shows significant potential for improving IHD diagnosis.
- VOC analysis, when integrated with machine learning, offers a promising, non-invasive approach to enhance the accuracy of physical stress tests for IHD detection.
- This metabolomic approach may lead to more effective early diagnosis and management strategies for ischemic heart disease.
Abstract:
Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: 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. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and 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 (n = 31) with and G2 (n = 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. Results: 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]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: 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.
