Volatilome and Single-Lead Electrocardiogram Optimize Ischemic Heart Disease Diagnosis Using Machine Learning Models

NCT06181799 | Completed Results

• 1 other identifier: 13011998
• Study Type: observational
• Target: 80
• Locations: 1 country
• Sites: 1

Brief Summary

This is a prospective, case-control, single-center, observational, non-randomized study. It is designed to evaluate the diagnostic accuracy of functional tests involving physical exertion monitored via a 12-lead ECG, combined with analysis of exhaled breath volatile organic compounds (VOCs) and single-lead ECG parameters.

Conditions

Coronary Artery Disease; Ischemic Heart Disease; Chronic Coronary Disease; Angina Pectoris; Stable Coronary Artery Disease CAD

Keywords

Coronary Artery Disease; Mass Spectrometry; Volatilome; Single Lead-ECG; Ischemic Heart Disease; Lipidome; Inflammasome; Electrocardiography; PTR-TOF-MS-1000; Stress Induced Myocardial Perfusion Defect; Qardio-Qvark; Stable Coronary Artery Disease; Atherosclerosis; Breathome; Volatile Organic Compound; Bicycle Ergometry; Prevention; SCORE2; SCORE2-OP; Smart Risk Score; Machine Learning Model; Artificial intelligence; Cardiovascular disease; Risk Factor; Angina pectoris

Outcome Measures

Primary Outcomes (7)

• Diagnostic Accuracy (AUC, Sensitivity, Specificity, NPV, PPV) of the Stress-ECG Test in Ischemic Heart Disease

  Assessing the diagnostic accuracy of the stress electrocardiography test in ischemic heart disease

  The study was completed on 10.06.2024; the outcome measure was assessed during 6 months for the stress electrocardiography test

• Diagnostic Accuracy (AUC, Sensitivity, Specificity, NPV, PPV) of Exhaled Breath Analysis for Ischemic Heart Disease

  Analyze the volatile organic compounds of the exhaled breath in individuals with stress-induced myocardial perfusion defect on stress computed tomography myocardial perfusion imaging (CTP) with vasodilation test (adenosine triphosphate) and compare them with individuals without stress-induced myocardial perfusion defect after a physical stress test, and compare them with rest results as independent variables. Machine learning model was used to assess the diagnostic accuracy of the exhaled breath in the diagnosis of ischemic heart disease

  The study was completed on 10.06.2024; the outcome measure was assessed during 6 months for the obtained volatilome data.

• Diagnostic Accuracy (AUC, Sensitivity, Specificity, NPV, PPV) of Single-Lead ECG With Pulse Wave Analysis in Ischemic Heart Disease

  Analyze the parameters of the single-lead electrocardiogram with pulse wave function in individuals with stress-induced myocardial perfusion defect on stress computed tomography myocardial perfusion imaging (CTP) with vasodilation test and compare them with individuals without stress-induced myocardial perfusion defect as an independent variable. Machine learning model was used to assess the diagnostic accuracy of the single-lead ECG with pulse wave function in the diagnosis of ischemic heart disease.

  The study was completed on 10.06.2024; the outcome measure was assessed during 6 months for the single lead ECG parameters with pulse wave function

• Changes in the Concentration of Total Cholesterol, TG (mmol/L), LDL (mmol/L), LDL (mmol/L), HDL (mmol/L), and VLDL (mmol/L) in Individuals With Stress-induced Myocardial Perfusion Defect vs. Without.

  Analyzing the taken blood samples for total cholesterol, TG (mmol/L), LDL (mmol/L), LDL (mmol/L), HDL (mmol/L), and VLDL (mmol/L) in individuals with stress-induced myocardial perfusion defect on stress computed tomography myocardial perfusion imaging (CTP) with vasodilation test and comparing them with individuals without stress-induced myocardial perfusion defect as independent variables.

  The study was completed on 10.06.2024; the outcome measure was assessed during 1 week for the total cholesterol, TG (mmol/L), LDL (mmol/L), LDL (mmol/L), HDL (mmol/L), and VLDL (mmol/L) data.

• Changes in the Concentration of Apolipoprotein B (g/L) in Individuals With Stress-induced Myocardial Perfusion Defect vs. Without.

  Analyzing the taken blood samples for Apolipoprotein B (g/L) in individuals with stress-induced myocardial perfusion defect on stress computed tomography myocardial perfusion imaging (CTP) with vasodilation test and comparing them with individuals without stress-induced myocardial perfusion defect as independent variables.

  The study was completed on 10.06.2024; the outcome measure was assessed during 1 week for the Apolipoprotein В (g/L) data.

• Changes in the Concentration of Lipoprotein (а) (mg/L) and c-RP (mg/L) in Individuals With Stress-induced Myocardial Perfusion Defect vs. Without.

  Analyzing the taken blood samples for lipoprotein (a) (mg/L) and C-RP (mg/L) in individuals with stress-induced myocardial perfusion defect on stress computed tomography myocardial perfusion imaging (CTP) with vasodilation test and comparing them with individuals without stress-induced myocardial perfusion defect as independent variables.

  The study was completed on 10.06.2024; the outcome measure was assessed during 1 week for the lipoprotein (а) (mg/L) and c-RP (mg/L) data.

• Changes in the Concentration of IL- 6 (pg/mL) in Individuals With Stress-induced Myocardial Perfusion Defect vs. Without.

  Analyzing the taken blood samples for IL-6 (pg/mL) in individuals with stress-induced myocardial perfusion defect on stress computed tomography myocardial perfusion imaging (CTP) with vasodilation test and comparing them with individuals without stress-induced myocardial perfusion defect as independent variables.

  The study was completed on 10.06.2024; the outcome measure was assessed during 1 week for the IL- 6 (pg/mL) data.

Study Arms (2)

Experimental group

The group is planned to include 31 people with myocardial perfusion defect on the stress computed tomography myocardial perfusion Imaging (by using contrast enhanced multi-slice spiral computed tomography (CE-MSCT) using adenosine triphosphate (ATP)).

Diagnostic Test: Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).

Control group

The group is planned to include 49 people without myocardial perfusion defect on the stress computed tomography myocardial perfusion imaging (by using contrast enhanced multi-slice spiral computed tomography (CE-MSCT) using adenosine triphosphate (ATP)).

Diagnostic Test: Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).

Interventions

Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe). DIAGNOSTIC_TEST

Once enrolled in the study, all participants are scheduled to undergo the following tests: Analysis of the exhaled breath volatile organic compounds using real-time analytical methods (PTR-TOF-MS-1000; real-time mass spectrometer with ionization by the proton transfer method) before and after the physical exertion test, during 1 minute. Machine learning models will be employed to analyze the patterns identified in the exhaled air volatilome data. Before and immediately after the physical exertion test, all participants are scheduled to record a single-lead ECG and pulse wave for 3 minutes, using a portable single-lead recorder (Cardio-Qvark) (Russia, Moscow). Single-lead ECG and pulse wave parameters will be analyzed using machine learning models.

Control group; Experimental group

Eligibility Criteria

• Age: 40 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The planned number of participants to include in the study is 80, admitted to the University Clinical Hospitals No. 1, at the I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University).

You may qualify if:

• Age ≥40 years;
• Absence of acute exacerbations of psychiatric disorders or cognitive impairments that would preclude study participation;
• Provision of written informed consent for study participation, blood sample collection, and anonymous publication of research results;
• Pre-test probability of ischemic heart disease between 1% and 33%.
• Pregnancy and breastfeeding;
• Diabetes mellitus;
• Presence of acute myocardial ischemia (acute coronary syndrome or myocardial infarction within the preceding 48 hours) or a history of myocardial infarction;
• Active infectious or non-infectious inflammatory diseases in the acute/exacerbation phase;
• Connective tissue diseases (regardless of disease activity);
• Respiratory disorders (e.g., bronchial asthma, chronic bronchitis, cystic fibrosis, or other conditions associated with significant respiratory dysfunction);
• Acute pulmonary thromboembolism involving the pulmonary artery or its branches;
• Aortic dissection;
• Hemodynamically significant decompensated cardiac valvular defects\*\*;
• Active malignancy;
• Decompensated chronic heart failure (NYHA class III-IV) or acute heart failure;

You may not qualify if:

• Poor recording quality of single-channel electrocardiogram (ECG) and/or plethysmography data;
• Failure to complete the stress test due to reasons unrelated to cardiac conditions;
• Voluntary withdrawal of consent to continue participation in the study;

Sponsors & Collaborators

• I.M. Sechenov First Moscow State Medical University: lead

Study Sites (1)

Federal State Budgetary Educational Institution of Higher Education First Moscow State Medical University named after I.M. Sechenov of the Ministry of Health of Russia, City Clinical Hospital No. 1, Cardiology Clinic, Institute of Personalized Cardiology

Moscow, 119992, Russia

Location

Related Publications (12)

• Marzoog BA, Kopylov P. Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives. World J Cardiol. 2025 Apr 26;17(4):106593. doi: 10.4330/wjc.v17.i4.106593.

  PMID: 40308617 BACKGROUND

• Marzoog BA, Chomakhidze P, Gognieva D, Parunova AY, Demchuk SN, Silantyev A, Kuznetsova N, Kostikova A, Podgalo D, Nagornov E, Gadzhiakhmedova A, Kopylov P. Updates in breathomics behavior in ischemic heart disease and heart failure, mass-spectrometry. World J Cardiol. 2025 Feb 26;17(2):102851. doi: 10.4330/wjc.v17.i2.102851.

  PMID: 40061284 BACKGROUND

• Marzoog B. Breathomics Detect the Cardiovascular Disease: Delusion or Dilution of the Metabolomic Signature. Curr Cardiol Rev. 2024;20(4):e020224226647. doi: 10.2174/011573403X283768240124065853.

  PMID: 38318837 BACKGROUND

• Marzoog BA, Gognieva D, Chomakhidze P, Kopylov P. Cardi-Ankle Vascular Index Optimizes Ischemic Heart disease Diagnosis. MedRxiv 2024:2024.07.03.24309877. https://doi.org/10.1101/2024.07.03.24309877.

  BACKGROUND

• Marzoog BA. Volatilome: A Novel Tool for Risk Scoring in Ischemic Heart Disease. Curr Cardiol Rev. 2024;20(6):e080724231719. doi: 10.2174/011573403X304090240705063536.

  PMID: 38982923 BACKGROUND

• Marzoog BA. Volatilome is Inflammasome- and Lipidome-dependent in Ischemic Heart Disease. Curr Cardiol Rev. 2024;20(6):e190724232038. doi: 10.2174/011573403X302934240715113647.

  PMID: 39039680 BACKGROUND

• Marzoog BA, Chomakhidze P, Gognieva D, Silantyev A, Suvorov A, Abdullaev M, Mozzhukhina N, Filippova DA, Kostin SV, Kolpashnikova M, Ershova N, Ushakov N, Mesitskaya D, Kopylov P. Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters. World J Cardiol. 2025 Apr 26;17(4):104396. doi: 10.4330/wjc.v17.i4.104396.

  PMID: 40308623 RESULT

• Marzoog BA, Abdullaev M, Suvorov A, Chomakhidze P, Gognieva D, Gagarina NV, et al. Single Channel Electrocardiography Optimizes the Diagnostic Accuracy of Bicycle Ergometry! MedRxiv 2024:2024.04.20.24306122. https://doi.org/10.1101/2024.04.20.24306122.

  RESULT

• Marzoog BA, Chomakhidze P, Kopylov P. Reevaluation of the Bicycle Ergometry in the Diagnosis of Ischemic Heart Disease. MedRxiv 2024:2024.07.03.24309879. https://doi.org/10.1101/2024.07.03.24309879.

  RESULT

• B.A. Marzoog, P. Chomakhidze, A. Suvorov, P. Kopylov, CARDIO-QVARK Diagnose Ischemic Myocardiocyte!, (n.d.). https://doi.org/10.1101/2024.07.16.24310485.

  RESULT

• Marzoog BA, Chomakhidze P, Gognieva D, Gagarina NV, Silantyev A, Suvorov A, Fominykha E, Mustafina M, Natalya E, Gadzhiakhmedova A, Kopylov P. Machine Learning Model Discriminate Ischemic Heart Disease Using Breathome Analysis. Biomedicines. 2024 Dec 11;12(12):2814. doi: 10.3390/biomedicines12122814.

  PMID: 39767720 RESULT

• Marzoog BA, Chomakhidze P, Gognieva D, Silantyev A, Suvorov A, Stroeva A, Mustafina M, Fedorova AY, Syrkin A, Kopylov P. Exhaled Breath Biomarkers Reflect the Inflammasome and Lipidome Changes in Ischemic Heart Disease: A Study Using Machine Learning Models and Network Analysis. J Lipid Atheroscler. 2025 Sep;14(3):350-371. doi: 10.12997/jla.2025.14.3.350. Epub 2025 Jul 8.

  PMID: 41048598 RESULT

MeSH Terms

Conditions

Coronary Artery Disease; Myocardial Ischemia; Angina Pectoris; Atherosclerosis; Cardiovascular Diseases

Condition Hierarchy (Ancestors)

Coronary Disease; Heart Diseases; Arteriosclerosis; Arterial Occlusive Diseases; Vascular Diseases; Chest Pain; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Limitations and Caveats

Breath analysis for CAD diagnosis has limitations: bulky/expensive spirometers need portable VOC-specific devices; small sample size requires larger validation trials for combined ECG-breath biomarkers; lack of standardized protocols/reference databases hinders reproducibility. Statistical bias was mitigated via resampling, normalization, and median statistics. Partial consistency with known physiological patterns supports validity despite constraints.

Results Point of Contact

• Title: Basheer Abdullah Marzoog
• Organization: I.M. Sechenov First Moscow State Medical University (Sechenov University)

marzug@mail.ru

+7(996) 960 28 20

Study Officials

• Philipp Kopylov, Professor

  I.M. Sechenov First Moscow State Medical University (Sechenov University)

  PRINCIPAL INVESTIGATOR

Publication Agreements

• PI is Sponsor Employee: Yes

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 13, 2023
• First Posted: December 26, 2023
• Study Start: November 1, 2023
• Primary Completion: June 10, 2024
• Study Completion: June 10, 2024
• Last Updated: August 15, 2025
• Results First Posted: August 15, 2025

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will not share

Locations

RU (1)