Objective  To explore the risk factors of 1-year readmission in patients after percutaneous coronary intervention (PCI) treated with Bayaspirin combined with Clopidogrel and to construct a risk prediction model.

Methods  The clinical data of patients with myocardial infarction (MI) who underwent primary PCI in the Department of Cardiovascular Medicine of Lishui People's Hospital from January 2020 to June 2023 were retrospectively analyzed. The patients were divided into the readmission group (RG) and the non-readmission group (NRG) according to whether they were readmitted due to myocardial reinfarction or complications of MI within 1 year. Univariate analysis was used to explore the differential variables between the RG and NRG groups. Multivariate Logistic regression (Stepwise) was used to explore the risk factors of 1-year readmission in patients after PCI and the "optimal model". The "optimal model" was visualized using R software and transformed into a nomogram risk prediction model. The predictive ability of the Nomogram risk prediction model was evaluated using the receiver operating characteristic (ROC) curve. The calibration of the Nomogram risk prediction model was evaluated using the calibration curve (resampling, Bootstrap n=1 000). The net benefit of the nomogram risk prediction model was evaluated using the decision curve.

Results  A total of 100 patients were included in the study and the readmission rate within 1 year was 34.00%. Age (≥63 years old), diabetes, the number of diseased vessels (≥2), monocyte-high-density lipoprotein ratio (≥0.36), and prognosis nutrition (<39.39) were independent risk factors for readmission in patients with MI after PCI (all P<0.05). ROC analysis showed that the readmission risk prediction model had a good predictive efficiency for readmission in patients with MI after PCI, with an area under ROC curve of 0.903 (95% CI: 0.836-0.970). The calibration curve showed that the "predicted readmission probability" was approximately consistent with the "actual readmission probability"; the decision curve showed that the net benefit of the readmission risk prediction model nomogram was higher than that of the "all" clinical net benefit.

Conclusion  The readmission prediction model of patients with MI after PCI constructed in this study can accurately identify high-risk groups of readmission and may be beneficial for the standardized management of patients after PCI in clinical practice, improve the long-term prognosis of patients.

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