Development and Validation of an Early Diagnostic Prediction Model for Acute Coronary Syndrome in Patients with Chest Pain

Objective To develop and validate an early diagnostic prediction model for acute coronary syndrome (ACS) in patients with chest pain; thus providing scientific evidence for clinical decision-making.Material and methods A retrospective cohort study design was employed, including 480 chest pain patients who presented to the emergency department from January 2020 to January 2025. The patients were randomly divided into a modeling set (336 cases) and a validation set (144 cases) at a 7:3 ratio. Data collected included demographic characteristics, clinical symptoms and signs, medical history, laboratory tests, electrocardiogram, and imaging examinations. Univariate and multivariate logistic regression analyses were used to screen independent predictors and establish the prediction model. Model performance was evaluated through receiver operating characteristic (ROC) curves, Hosmer-Lemeshow test, and Bootstrap resampling, and a simplified risk scoring system was established.Results Multivariate logistic regression analysis showed that elevated cTnI (OR=17.231), ST-segment changes (OR=8.451), typical chest pain (OR=4.047), age ≥60 years (OR=2.441), smoking history (OR=2.103), sweating (OR=1.931), male sex (OR=1.799), and pain duration >30 min (OR=1.689) were independent predictors of ACS (all p