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Objectives To develop and validate a simple risk score for predicting major adverse cardiovascular events (MACE) in coronary artery disease (CAD) patients using routinely available clinical variables.Design This was a cohort study with retrospective analysis of prospectively collected data.Setting This study was conducted at a tertiary care centre in China.Participants This cohort study included 7182 CAD patients, randomly divided into training dataset and testing dataset in a ratio of 3:1.Primary and secondary outcome measures The primary outcome was a composite of MACE (cardiovascular death, non-fatal MI, stroke and revascularisation). A Cox regression model was developed on a training set to identify independent predictors. Variables were assigned points based on their β-coefficients to construct a risk score. The model was validated on a testing set. Discrimination was assessed using the concordance index (C-index) and area under the receiver operating characteristic (ROC) curve (AUC). Risk groups were defined according to the total score.Results Over a median follow-up of 27.3 months, 487 (6.8%) MACE events occurred. Six independent predictors were identified and included in the score: age ≥65 years (three points), NT-proBNP ≥125 pg/mL (four points), HbA1c ≥7% (3 points), elevated serum creatinine (>106 µmol/L for male or >97 µmol/L for female, 4 points), low-density lipoprotein-cholesterol (LDL-C) ≥1.8 mmol/L (two points), and cardiac troponin T (cTnT) ≥0.15 ng/mL (four points). The score stratified patients into low- (0–4 points), middle- (5–9 points), and high-risk (10–14 points) groups. In the testing set, the middle- and high-risk groups had significantly increased MACE risk compared with the low-risk group (HR 1.54, 95% CI 1.03 to 2.29; HR 2.70, 95% CI 1.78 to 4.08, respectively). The model showed consistent discrimination in both training (C-index = 0.726, AUC = 0.728) and testing sets (C-index = 0.702, AUC = 0.705).Conclusion A simple risk score comprising six clinical variables effectively stratified CAD patients into distinct MACE risk categories. This tool may aid in clinical decision-making and resource prioritisation in secondary prevention, pending external validation.