Objective To explore the risk factors for post-acute cerebral infarction (ACI) seizures and to develop and validate a clinical prediction model based on the Fine-Gray competing risk model. Methods A retrospective cohort study was conducted including 580 ACI patients admitted between January 2021 and March 2024, with a follow-up period of 1 month. Multivariate analysis was performed using the Fine-Gray competing risk model, with seizure as the event of interest. A nomogram prediction model was developed based on the identified independent risk factors, and model performance was evaluated using the C-index and calibration curve. Results Among the 580 patients, 61 (10.52%) developed seizures. Results of the multivariate analysis using the Fine-Gray competing risk model showed that cortical infarction (sHR=1.34, 95% CI: 1.07-1.68), hemorrhagic transformation (sHR=1.38, 95% CI: 1.11-1.72), high pre-thrombolysis National Institutes of Health stroke scale (NIHSS) score (sHR=1.37, 95% CI: 1.14-1.66), cardioembolic type (sHR=1.37, 95% CI: 1.06-1.78), and carotid circulation involvement (sHR=1.35, 95% CI: 1.09-1.66) were independent risk factors (all P<0.05). The C-index of the prediction model was 0.82 (95% CI: 0.79-0.86), and the calibration curve showed high consistency between predicted probabilities and actual risks. Conclusions Based on the competing risk model analysis, cortical infarction, hemorrhagic transformation, high pre-thrombolysis NIHSS score, TOAST classification of cardioembolic type, and carotid circulation involvement are independent risk factors for post-ACI seizures. The nomogram developed on the above factors can quantitatively predict the risk of post-ACI seizures with good predictive performance. 

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