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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.
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).
The study includes the following stages:
3.1. Analysis of exhaled air will be carried out with the Compact PTR-MS instrument manufactured by Ionicon (Austria) (analytical device), registration certificate No. (C16)07/C05.
3.2. All the participants will undergo a single blood sampling during the day of performing the study, a blood test, 10 ml from a peripheral vein to determine the level of total cholesterol, low-density lipoprotein (LDL), very low-density lipoprotein (VLDL), high-density lipoprotein (HDL), triglycerides, C-reactive protein (CRP), lipoprotein a, apolipoprotein B, and interleukin-6 (IL-6).
3.3. Both groups will perform a bicycle ergometry test (on a SCHILLER c200 device) to evaluate the response to physical activity.
3.4. Before and immediately after the exercise test, all patients are scheduled to record a single-lead ECG and pulse wave, using a portable single-lead recorder (Cardio-Qvark) (Russia, Moscow).
4.5. Stress computed tomography myocardial perfusion imaging (CTP) with a vasodilation test using adenosine triphosphate on a CT device with 640 slices (Canon; Aquilion One Genesis) will be performed.
After completion of the instrumental and laboratory analysis, a statistical analysis will be conducted using classical statistics and machine learning methods, including gradient boosting.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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)). |
| |
| 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)). |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
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Inclusion Criteria:
Non-inclusion criteria:
Exclusion Criteria:
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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).
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| Name | Affiliation | Role |
|---|---|---|
| Philipp Kopylov, Professor | I.M. Sechenov First Moscow State Medical University (Sechenov University) | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| 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 |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40308617 | Background | 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. | |
| 40061284 | 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. |
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No, due to the prohibition by the local ethical committee
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| ID | Title | Description |
|---|---|---|
| FG000 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).: 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. |
| Title | Milestones | Reasons Not Completed | |||||
|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Jun 17, 2024 | Jul 23, 2025 |
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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. |
| Russia |
| 38318837 | 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. |
| 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. |
| 38982923 | 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. |
| 39039680 | Background | Marzoog BA. Volatilome is Inflammasome- and Lipidome-dependent in Ischemic Heart Disease. Curr Cardiol Rev. 2024;20(6):e190724232038. doi: 10.2174/011573403X302934240715113647. |
| 40308623 | Result | 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. |
| 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. |
| 39767720 | 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. |
| 41048598 | 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. |
| FG001 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).: 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. |
| COMPLETED |
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| NOT COMPLETED |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).: 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. |
| BG001 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).: 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. |
| BG002 | Total | Total of all reporting groups |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | The patients were collected from the Sechenov University clinical hospital number 1 | Mean | Standard Deviation | years |
| ||||||||||||||
| Sex: Female, Male | Count of Participants | Participants |
| ||||||||||||||||
| Race and Ethnicity Not Collected | Race and Ethnicity were not collected from any participant. | Count of Participants | Participants |
| |||||||||||||||
| Region of Enrollment | Count of Participants | Participants |
|
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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 | Posted | Number | 95% Confidence Interval | Proportion probability | The study was completed on 10.06.2024; the outcome measure was assessed during 6 months for the stress electrocardiography test |
|
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| ||||||||||||||||||||||||||||||||||||
| Primary | 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 | Posted | Number | 95% Confidence Interval | Proportion probability | The study was completed on 10.06.2024; the outcome measure was assessed during 6 months for the obtained volatilome data. |
| ||||||||||||||||||||||||||||||||||||||
| Primary | 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. | Posted | Number | 95% Confidence Interval | Proportion probability | 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 |
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| Primary | 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. | Posted | Mean | Standard Deviation | mmol/L | 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. |
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| Primary | 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. | Posted | Mean | Standard Deviation | g/L | The study was completed on 10.06.2024; the outcome measure was assessed during 1 week for the Apolipoprotein В (g/L) data. |
| ||||||||||||||||||||||||||||||||||||||
| Primary | 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. | Posted | Mean | Standard Deviation | mg/L | 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. |
| ||||||||||||||||||||||||||||||||||||||
| Primary | 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. | Posted | Mean | Standard Deviation | pg/mL | The study was completed on 10.06.2024; the outcome measure was assessed during 1 week for the IL- 6 (pg/mL) data. |
|
Over 1 month
All the adverse events, including the "All-Cause Mortality, Serious, and Other (Not Including Serious) Adverse Events were assesed.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).: 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. | 0 | 31 | 0 | 31 | 0 | 31 |
| EG001 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).: 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. | 0 | 49 | 0 | 49 | 0 | 49 |
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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.
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Basheer Abdullah Marzoog | I.M. Sechenov First Moscow State Medical University (Sechenov University) | +7(996) 960 28 20 | marzug@mail.ru |
| Prot_000.pdf |
| SAP | No | Yes | No | Statistical Analysis Plan | Jun 17, 2024 | Jul 1, 2025 | SAP_001.pdf |
| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| D017202 | Myocardial Ischemia |
| D000787 | Angina Pectoris |
| D050197 | Atherosclerosis |
| D002318 | Cardiovascular Diseases |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D006331 | Heart Diseases |
| D001161 | Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |
| D002637 | Chest Pain |
| D010146 | Pain |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| Specificity |
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| NPV NPV NPV NPV |
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| PPV |
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| OG001 | Control Group | The group includes 49 people without stress-induced myocardial perfusion defect on the stress computed tomography myocardial perfusion imaging (by using contrast-enhanced multi-slice spiral computed tomography (CE-MSCT) with adenosine triphosphate (ATP)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe)) during 1 minute. Machine learning model is employed to analyze the patterns identified in the exhaled air volatilome data. The assessed predictors exceed 750 because the participants exhaled three times at each time. The method used to evaluate these results is calculating delta and then selecting the 5 or 10 most mathematically important predictors for the diagnosis of positive stress-induced myocardial perfusion defect using the machine learning model. Then building the final machine learning model and checking its features, AUC, sensitivity, specificity, PPV, and NPV. For full results check the paper DOI: 10.3390/biomedicines12122814 |
|
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| OG001 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe)): 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. For full results check the paper DOI: 10.4330/wjc.v17.i4.104396 |
|
|
| OG001 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe).: 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. For full results check the paper DOI: 10.12997/jla.2025.14.e30 |
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| OG001 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe)): 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. For full results check the paper DOI: 10.12997/jla.2025.14.e30 |
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| OG001 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe)): 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. For full results check the paper DOI: 10.12997/jla.2025.14.e30 |
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| OG001 | 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)). Mass spectrometry using the PTR TOF-1000 (IONICON PTR-TOF-MS - Trace VOC Analyzer, Eduard-Bodem-Gasse 3, 6020 Innsbruck, Austria (Europe)): 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. For full results check the paper DOI: 10.12997/jla.2025.14.e30 |
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