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.
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Diagnostic discrimination performance
Timeframe: At the index emergency department visit (VOC sampling performed within 24 hours of emergency department arrival).