〔Abstract〕 To construct and to validate a nomogram prediction model based on Lipoprotein-associated phospholipase A2 (Lp-PLA2) and Lipoprotein(a) [LP(a)] for predicting the risk of major adverse cardiovascular events (MACE) in patients with coronary heart disease (CHD). Methods A retrospective analysis was conducted on the clinical data of 442 patients with coronary heart disease (CHD). Among them, 411 patients who completed follow-up were randomly divided into a training set (288 cases) and a validation set (123 cases) at a 7:3 ratio. Independent risk factors for major adverse cardiovascular events (MACE) in CHD patients were screened through Lasso regression analysis and Cox regression analysis, and a nomogram prediction model was constructed. The predictive performance of the model was evaluated using time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Results Variables were screened through Lasso regression and Cox regression analysis. The final model included nine independent predictors, namely age, smoking history, clinical phenotype of CHD, the number of coronary artery lesions, Gensini score, BNP, Lp-PLA2, LP(a), and the history of statin use. The area under the ROC curve in the training set was 0.897, 0.885, and 0.909 at 1, 2, and 3 years, respectively; The area under the ROC curve in the validation set was 0.885, 0.881, and 0.923 at 1, 2, and 3 years, respectively. These results demonstrated that the model had excellent discriminatory power.The calibration curves and decision curves demonstrated that the model had high clinical practicality in predicting the occurrence of MACE in CHD patients. Conclusion The nomogram prediction model based on LP-PLA2, LP(a) and other risk factors provides an effective tool for the prognosis assessment of CHD patients, facilitating the early identification of high-risk patients and enabling individualized intervention.