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

Application of Rapid Detection of Exhaled Volatile Organic Compounds in the Emergency Department for Differentiating High-Risk Chest Pain Patients

Study Objectives

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. 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. 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. Novel diagnostic markers: To explore new biomarker combinations superior to conventional diagnostic indicators.

Study Hypotheses

1. High-risk chest pain patients present a VOC profile distinct from that of healthy individuals and patients with other causes of chest pain.
2. Baseline levels and early changes of exhaled VOCs can achieve both rapid diagnosis and risk stratification.
3. 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.

To enable subgroup analyses (e.g., acute coronary syndrome [ACS], pulmonary embolism [PE], aortic dissection [AD]) and the construction of multivariable predictive models, at least 150-300 patients per subgroup are required. According to preliminary investigation, the emergency department admits approximately 20 chest pain patients daily. To ensure model stability, cross-validation, and sufficient subgroup evaluation, a total of 6,000 patients will be prospectively enrolled, thereby enhancing scientific rigor and external validity.

Primary Outcome Discrimination between high-risk and non-high-risk chest pain patients, assessed by AUC-ROC, sensitivity, and specificity.

Secondary Outcomes

1. Missed diagnosis rate (the proportion of high-risk patients misclassified as low or intermediate risk by the model).
2. Average emergency department length of stay and medical costs under model-guided triage.
3. In-hospital mortality and incidence of major adverse cardiovascular events (MACE).

Statistical Methods

1. 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.
2. 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.
3. Prognostic factors will be assessed using Cox proportional hazards models.
4. Trajectory analysis will be applied to evaluate changes in VOC concentrations over time.

Study Overview

• Status: Not yet recruiting
• Conditions: Acute Coronary Syndrome; Volatile Organic Compounds; High-Risk Chest Pain
• Intervention / Treatment: Diagnostic test: observational study

Detailed Description

1. Research Background Since the concept of precision medicine was introduced, the innovation and optimization of clinical diagnostic technologies have become a central issue in medical research. Traditional diagnostic methods-including imaging, hematology, and histology-often rely on invasive procedures. While these techniques can provide highly accurate diagnostic information, they are associated with high costs, operational complexity, and potential risks of harm to patients, making them less suitable for rapid screening and dynamic monitoring. Therefore, the development of non-invasive, rapid, and cost-effective diagnostic technologies has become an urgent need in modern medicine.

As one of the most metabolically active organs, the lungs exchange metabolites produced by body tissues into the bloodstream, which are subsequently exhaled. Thus, exhaled breath can reflect the physiological and pathological states of the body. Historically, physicians identified certain diseases through changes in body odor-for instance, a fruity "rotten apple" smell in diabetic ketoacidosis or a fishy odor in patients with liver disease. At that time, the specific chemical composition underlying these odors was unknown. In 1971, Pauling et al. first applied gas chromatography to human exhaled breath analysis, identifying hundreds of volatile organic compounds (VOCs), thereby laying the foundation for breath analysis technology.

Acute chest pain is one of the most common presenting symptoms in emergency departments, with diverse etiologies and varying prognoses. Several life-threatening cardiovascular emergencies-such as acute coronary syndrome (ACS), acute pulmonary embolism (APE), and acute aortic syndrome (AAS)-typically present with acute chest pain and require prompt diagnosis and treatment in the emergency setting. However, not all chest pain cases are high risk. A considerable proportion of patients (e.g., those with intercostal neuralgia) have favorable outcomes and do not require extensive diagnostic testing or monitoring. Therefore, accurate risk stratification is critical: rapid recognition and management of high-risk chest pain, while avoiding overtreatment in low-risk cases, are essential to safeguard public health and reduce healthcare burden.

Breath analysis of exhaled VOCs has attracted increasing attention in recent years as a unique biomarker-based tool for early disease diagnosis and screening. VOCs are highly volatile organic molecules present not only in the environment but also generated through human metabolic processes. As endogenous metabolites, VOCs reflect health status and disease progression. Due to their non-invasive detection and high sensitivity, VOCs show great promise in early diagnosis, personalized treatment, and disease monitoring. Compared with traditional biomarker assays, breath analysis offers unique advantages. Instead of detecting a single molecule, VOC analysis can identify molecular patterns, providing more comprehensive and accurate diagnostic information. Current evidence has demonstrated strong associations between VOCs and various diseases-including cancer, respiratory disorders, and diabetes-highlighting their value in precision medicine.

Precision medicine emphasizes individualized treatment strategies tailored to patient-specific biological characteristics. Within this framework, VOCs-as biomarkers of physiological and pathological states-may hold significant clinical value in the early diagnosis, screening, and monitoring of acute chest pain. However, research on breath analysis for acute chest pain remains limited. Marzoog et al. demonstrated that VOCs could distinguish ischemic heart disease patients from controls using machine learning models, suggesting the potential application of breath analysis in acute cardiogenic chest pain. In 2024, the Chinese Chest Pain Consensus proposed incorporating novel metabolic markers, such as fatty aldehydes, into chest pain evaluation systems. Nevertheless, standardized studies on exhaled VOCs remain scarce.

Therefore, this study aims to apply exhaled VOC detection for the identification of high-risk acute chest pain patients, establish a database of VOC profiles, and evaluate the prognostic value of dynamic VOC changes. This research will, for the first time, systematically characterize the VOC profile of high-risk acute chest pain, analyze dynamic changes, and construct a VOC-based predictive scoring model for patient prognosis. These efforts are expected to provide novel strategies for individualized treatment, disease monitoring, and management in high-risk acute chest pain.

2. Research Objectives

1. Biomarker identification: Establish a VOC profile for high-risk acute chest pain patients and differentiate them from other chest pain etiologies.

   Aldehyde detection: Explore the role of exhaled aldehyde detection in high-risk chest pain; establish and validate an early diagnostic model using VOCs to optimize emergency triage.

2. Prognostic assessment: Evaluate the predictive value of VOC concentration changes for in-hospital mortality and major adverse cardiovascular events (MACE) in high-risk chest pain patients.
3. Novel indicators: Explore new diagnostic marker combinations superior to conventional biomarkers.

3. Research Methods Study Design This study is designed as a prospective cohort study. 3.1 Study Sites and Participants The study will be conducted in the Emergency Department (ED), Emergency Intensive Care Unit (EICU), and Intensive Care Unit (ICU) of Qilu Hospital, Shandong University. Eligible participants will include all patients presenting with acute chest pain to the ED.

3.2 Study Outcomes

1. Primary outcome: Discrimination between high-risk and low/intermediate-risk chest pain, assessed by area under the receiver operating characteristic curve (AUC-ROC), sensitivity, and specificity.

2. Secondary outcomes:

   1. Missed diagnosis rate (proportion of high-risk patients misclassified as low/intermediate risk by the model).
   2. ED length of stay and healthcare costs under model-guided triage.

   3. In-hospital mortality and incidence of MACE. 3.3 Statistical Analysis To ensure data accuracy, an Excel database will be established with dual-entry verification. Data will be analyzed using R software (version 4.3.2), with a two-tailed significance level of α = 0.05 (P < 0.05 considered statistically significant).

      1. Descriptive analysis: Baseline characteristics and VOC concentrations will be summarized. Categorical variables will be expressed as frequencies or percentages. Continuous variables with normal or approximately normal distribution will be presented as mean ± standard deviation (SD). Skewed data will be reported as median (P25, P75). Between-group comparisons will use independent-samples t-tests, one-way analysis of variance (ANOVA), Mann-Whitney U test, or Kruskal-Wallis test, as appropriate. Categorical variables will be analyzed using chi-squared (χ²) test or Fisher's exact test.
      2. Feature selection: Characteristic VOCs will be screened, and a VOC scoring model will be developed using machine learning methods.
      3. Prognostic analysis: Prognostic factors will be evaluated using Cox proportional hazards regression.
      4. Trajectory analysis: VOC concentration dynamics will be modeled to explore temporal patterns.

• Study Type: Observational
• Enrollment (Estimated): 6000

Contacts and Locations

• Study Contact: Name: Yuan Bian, PhD; Phone Number: 8618560083065; Email: bianyuan@sdu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria：

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

Exclusion Criteria：

• 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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Missed Diagnosis Rate	Proportion of high-risk chest pain patients misclassified as low or intermediate risk by the VOC-based model. Unit of Measure: Percentage (%).	From emergency department admission to 24 hours post-triage
Emergency Department Length of Stay	Duration of stay in the emergency department. Unit of Measure: Hours (h).	From emergency department admission to emergency department discharge or transfer to an inpatient unit, up to 48 hours.
Emergency Department Medical Cost	Direct medical cost incurred during emergency department stay. Unit of Measure: Chinese Yuan (CNY).	From emergency department admission to emergency department discharge or transfer to an inpatient unit, up to 48 hours.
In-Hospital Mortality	All-cause mortality during hospitalization among high-risk chest pain patients. Unit of Measure: Mortality rate (%).	From hospital admission to hospital discharge, assessed up to 30 days
Incidence of Major Adverse Cardiovascular Events (MACE)	Occurrence of predefined MACE, including myocardial infarction, stroke, cardiac arrest, or urgent revascularization. Unit of Measure: Cumulative incidence (%).	From hospital admission to day 30 post-admission

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in VOC Concentration Over Time	Temporal change in exhaled VOC concentrations measured at predefined time points during hospitalization. Unit of Measure: VOC concentration (parts per billion, ppb).	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	Discrimination ability of the VOC-based prognostic scoring model for predicting in-hospital mortality and major adverse cardiovascular events (MACE), compared with observed outcomes during follow-up. Unit of Measure: Area under the receiver operating characteristic curve (AUC-ROC, unitless).	From hospital admission to day 30 post-admission (end of in-hospital follow-up).

Collaborators and Investigators

• Sponsor: Qilu Hospital of Shandong University

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Estimated): March 1, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027

Study Registration Dates

• First Submitted: September 7, 2025
• First Submitted That Met QC Criteria: January 23, 2026
• First Posted (Actual): January 30, 2026

Study Record Updates

• Last Update Posted (Actual): January 30, 2026
• Last Update Submitted That Met QC Criteria: January 23, 2026
• Last Verified: October 1, 2025

More Information

Terms related to this study

• Keywords: Acute coronary syndrome; Pulmonary embolism; Chest Pain; Volatile organic compounds; Aortic dissection; Breath analysis
• Additional Relevant MeSH Terms: Dissection, Blood Vessel; Acute Aortic Syndrome; Pain; Neurologic Manifestations; Vascular Diseases; Cardiovascular Diseases; Heart Diseases; Respiratory Tract Diseases; Lung Diseases; Embolism and Thrombosis; Embolism; Myocardial Ischemia; Aortic Diseases; Aneurysm; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Pulmonary Embolism; Aortic Dissection; Acute Coronary Syndrome; Chest Pain
• Other Study ID Numbers: KYLL-202506-022-1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No