Objective To analyze the influencing factors of in-hospital major adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) in elderly patients with acute ST-segment elevation myocardial infarction (STEMI) based on multimodal clinical data, to establish a predictive model, and to evaluate the predictive performance of the model.

Methods Elderly STEMI patients who underwent PCI at the Fourth Hospital of Changsha from May 2020 to May 2025 were retrospectively enrolled as research subjects. The patients were randomly divided into a training set and a validation set at a ratio of 7∶3. Patients in the training set were further assigned to the MACE group and the non-MACE group according to whether in-hospital MACE occurred. Lasso regression and multivariate Logistic regression were applied to screen independent predictive factors, and a nomogram model was constructed. The model performance was assessed using the receiver operating characteristic curve and the area under the curve (AUC), as well as calibration curve and decision curve analysis. Bootstrap resampling (1, 000 repetitions) was performed for internal validation.

Results A total of 515 elderly STEMI patients were included, with an in-hospital MACE incidence of 26.02%, of whom 361 were assigned to the training set and 154 to the validation set. Multivariate Logistic regression revealed that elevodbed Killip classification ( ≥ Grade Ⅱ)[OR=4.186, 95%CI (1.967, 8.907)], troponin [OR=2.415, 95%CI (1.690, 3.451)], N-terminal pro-brain natriuretic peptide (NT-proBNP) [OR=1.233, 95%CI (1.129, 1.347)], and Synergy between Percutaneous Coronary Intervention with TAXUS and Cardiac Surgery (SYNTAX) score [OR=1.214, 95%CI (1.129, 1.304)] were independent risk factors for in-hospital MACE, whereas eleuated left ventricular ejection fraction (LVEF) [OR=0.889, 95%CI (0.843, 0.938)] and prognostic nutritional index (PNI) [OR=0.843, 95%CI (0.784, 0.905)] were independent protective factors for in-hospital MACE. The AUC values of the training set and validation set were 0.941 and 0.934, respectively. The calibration curve largely overlapped with the ideal diagonal line (Hosmer-Lemeshow test, P > 0.05). Decision curve analysis demonstrated superior clinical net benefit compared with extreme strategies.

Conclusion The nomogram model constructed based on Killip classification, troponin, NT-proBNP, LVEF, SYNTAX score and PNI exhibits favorable predictive performance and clinical practicability for in-hospital MACE risk in elderly STEMI patients after PCI. It preliminarily suggests that this model can be used for early clinical identification of high-risk patients and individualized intervention. Further validation with multicenter, large-sample external independent cohorts is still required.

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