BREVOC Study: Exhaled VOCs for High-Risk Chest Pain in the ED

NCT07379567 | Not Yet Recruiting

• 1 other identifier: KYLL-202506-022-1
• Study Type: observational
• Target: 6,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

Study Objectives

1. 1.Screening and identification of diagnostic biomarkers: To establish the exhaled volatile organic compound (VOC) profile of patients with acute high-risk chest pain and to differentiate high-risk chest pain patients.
2. 2.Exploration of aldehyde detection: To investigate the role of exhaled aldehyde detection in high-risk chest pain patients; to establish and validate an early differential diagnostic model of exhaled VOCs for high-risk chest pain, thereby optimizing emergency triage procedures.
3. 3.Prognostic evaluation: To assess the predictive value of VOC concentration changes for in-hospital mortality and major adverse cardiovascular events (MACE) in high-risk chest pain patients.
4. 4.Novel diagnostic markers: To explore new biomarker combinations superior to conventional diagnostic indicators.
5. 5.High-risk chest pain patients present a VOC profile distinct from that of healthy individuals and patients with other causes of chest pain.
6. 6.Baseline levels and early changes of exhaled VOCs can achieve both rapid diagnosis and risk stratification.
7. 7.Exhaled VOCs can predict the prognosis of high-risk chest pain patients. Sample Size Calculation This is an exploratory study, aiming to enroll all patients presenting with acute chest pain to the emergency department of our hospital between May 2025 and June 2026. Based on prior studies, the primary endpoint is assessed using area under the receiver operating characteristic curve (AUC-ROC) analysis, with α = 0.05 and 1-β = 0.90. The expected model AUC is 0.75, compared to a minimum acceptable AUC of 0.65. Assuming a group ratio of 1:2 (high-risk: non-high-risk), Power Analysis and Sample Size (PASS) software estimates a minimum sample size of approximately 1,320 patients.
8. 8.Missed diagnosis rate (the proportion of high-risk patients misclassified as low or intermediate risk by the model).
9. 9.Average emergency department length of stay and medical costs under model-guided triage.
10. 10.In-hospital mortality and incidence of major adverse cardiovascular events (MACE).
11. 11.Categorical variables will be expressed as frequencies or percentages; normally or approximately normally distributed continuous variables as mean ± standard deviation; and skewed data as median (P25, P75). Between-group comparisons will be performed using independent-samples t-tests, one-way analysis of variance (ANOVA), Mann-Whitney U tests, or Kruskal-Wallis tests for continuous variables, and chi-square (χ²) tests or Fisher's exact tests for categorical variables.
12. 12.Feature selection of VOCs will be performed using methods such as least absolute shrinkage and selection operator (LASSO) regression, followed by the construction of a VOC scoring model.
13. 13.Prognostic factors will be assessed using Cox proportional hazards models.
14. 14.Trajectory analysis will be applied to evaluate changes in VOC concentrations over time.

Conditions

Acute Coronary Syndrome; High-Risk Chest Pain; Volatile Organic Compounds

Keywords

Acute coronary syndrome; Breath analysis; Volatile organic compounds; Chest Pain; Aortic dissection; Pulmonary embolism

Outcome Measures

Primary Outcomes (1)

• Diagnostic discrimination performance

  Ability of the exhaled volatile organic compound (VOC)-based model to differentiate high-risk chest pain patients from low- or intermediate-risk patients. Unit of Measure: Area under the receiver operating characteristic curve (AUC-ROC), sensitivity (%), specificity (%).

  At the index emergency department visit (VOC sampling performed within 24 hours of emergency department arrival).

Secondary Outcomes (5)

• Missed Diagnosis Rate

  From emergency department admission to 24 hours post-triage

• Emergency Department Length of Stay

  From emergency department admission to emergency department discharge or transfer to an inpatient unit, up to 48 hours.

• Emergency Department Medical Cost

  From emergency department admission to emergency department discharge or transfer to an inpatient unit, up to 48 hours.

• In-Hospital Mortality

  From hospital admission to hospital discharge, assessed up to 30 days

• Incidence of Major Adverse Cardiovascular Events (MACE)

  From hospital admission to day 30 post-admission

Other Outcomes (2)

• Change in VOC Concentration Over Time

  From hospital admission (baseline sampling within 2 hours of emergency department arrival) through hospital discharge, with repeat sampling during hospitalization, up to 48h.

• VOC-Based Prognostic Model Discrimination for In-Hospital Mortality and MACE

  From hospital admission to day 30 post-admission (end of in-hospital follow-up).

Interventions

observational study DIAGNOSTIC_TEST

Exposure factors: Concentration monitoring of 190 candidate VOCs including acetaldehyde and acetone in exhaled air

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

In this study, the emergency department, EICU and ICU of Qilu Hospital of Shandong University were set as the research sites. The study subjects were all patients with chest pain who were admitted to the emergency department of our hospital due to acute chest pain.

You may qualify if:

• Age between 18 and 80 years, regardless of gender
• Presenting to the Emergency Department with acute chest pain=
• Able to provide informed consent

You may not qualify if:

• Unable to perform breath sampling
• Incomplete medical records
• Refusal to participate by the patient or legal representative
• Presence of any of the following conditions:Recent pulmonary infection、 Primary liver or kidney dysfunction、Chronic respiratory or digestive system diseases、Terminal illness or receiving palliative care
• Participation in another clinical research study within the past 30 days

Sponsors & Collaborators

• Qilu Hospital of Shandong University: lead

Related Publications (5)

• Phillips M. Breath tests in medicine. Scientific American. 1992;267(1):74-79. doi:10.1038/scientificamerican0792-74. 呼气挥发性有机物急诊快速检测在鉴别高危胸痛患者中的应用 Pauling L, Robinson AB, Teranishi R, et al. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc Natl Acad Sci U S A. 1971;68(10):2374-2376. doi:10.1073/pnas.68.10.2374. 何雅珍, 高汭, 吴智君, 等. 呼出气挥发性有机物的采集及分析方法研究进展. 环境与职业医学. 2024;41(6):707-712. doi:10.11836/JEOM24036. Hanna GB, Boshier PR, Markar SR, et al. Accuracy and methodological challenges of volatile organic compound-based exhaled breath tests for cancer diagnosis: A systematic review and meta-analysis. JAMA Oncology. 2019;5(1):e182815. doi:10.1001/jamaoncol.2018.2815. 陶雨寒, 毛辉. 呼出气挥发性有机物在呼吸系统非感染性疾病中的应用. 中国呼吸与危重监护杂志. 2024;23(8):599-604. doi:10.7507/1671-6205.202304046. 吴昊坪, 李磊, 曾睿, 等. 糖尿病呼出气体检测与分析研究进展. 化学进展. 2024;36(4):601-611. doi:10.7536/PC231110. Marzoog BA, Chomakhidze P, Gognieva D, et al. Machine learning model discriminate ischemic heart disease using breathome analysis. Biomedicines. 2024;12(12):2814. doi:10.3390/biomedicines12122814. PMID:39767720. 中国医疗保健国际交流促进会胸痛学分会, 中国医师协会胸痛专业委员会. 急性非创伤性胸痛生物标志物联合检测专家共识（2024版）. 中华急诊医学杂志. 2024;33(12):1681-1696. doi:10.3760/cma.j.issn.1671-0282.2024.12.005.

  RESULT

• Ibrahim W, Carr L, Cordell R, et al. Visualization of exhaled breath metabolites reveals distinct signatures of acute disease. Sci Transl Med. 2022;14(641):eabl5849. doi:10.1126/scitranslmed.abl5849.

  RESULT

• Ibrahim W, Carr L, Cordell R, et al. Assessment of breath volatile organic compounds in acute cardiorespiratory disease. Clin Transl Med. 2019;8(1):33. doi:10.1186/s40169-019-0244-8.

  RESULT

• Fens N, Douma RA, Sterk PJ, Kamphuisen PW. Breathomics as a diagnostic tool for pulmonary embolism. J Thromb Haemost. 2010 Dec;8(12):2831-3. doi: 10.1111/j.1538-7836.2010.04064.x. No abstract available.

  PMID: 20860678 RESULT

• Nardi Agmon I, Broza YY, Alaa G, Eisen A, Hamdan A, Kornowski R, Haick H. Detecting Coronary Artery Disease Using Exhaled Breath Analysis. Cardiology. 2022;147(4):389-397. doi: 10.1159/000525688. Epub 2022 Jul 12.

  PMID: 35820369 RESULT

MeSH Terms

Conditions

Acute Coronary Syndrome; Chest Pain; Aortic Dissection; Pulmonary Embolism

Condition Hierarchy (Ancestors)

Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Vascular Diseases; Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Dissection, Blood Vessel; Aneurysm; Acute Aortic Syndrome; Aortic Diseases; Lung Diseases; Respiratory Tract Diseases; Embolism; Embolism and Thrombosis

Central Study Contacts

Yuan Bian, PhD

CONTACT

8618560083065; bianyuan@sdu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: September 7, 2025
• First Posted: January 30, 2026
• Study Start: March 1, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: January 30, 2026

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will not share