Large-artery atherosclerotic acute ischemic stroke (AIS) accounts for a substantial proportion of clinical stroke cases and is associated with poor prognosis. Stroke recurrence remains a major threat to long-term survival and quality of life in these patients. However, the characteristics of 1-year recurrence risk and its associated risk factors in this population remain unclear.

To identify factors associated with 1-year stroke recurrence in patients with large-artery atherosclerotic AIS and to evaluate the advantages of a random survival forest (RSF) model in predicting recurrence risk.

Patients diagnosed with large-artery atherosclerotic AIS in four hospitals in Nanchang from March 2019 to March 2021 were included. Baseline data within 48 hours of admission were collected using a standardized case report form. The follow-up outcome was stroke recurrence within 1 year. The dataset was randomly divided into a training set and a test set at a ratio of 7∶3. An RSF model was constructed to predict AIS recurrence, and model parameters were optimized using five-fold cross-validation. The model was compared with a Cox regression model. Model performance was evaluated using the concordance index (C-index) and calibration curves.

A total of 1 186 patients were included in the final analysis. During the 12-month follow-up period after discharge, 102 patients experienced recurrence, with a recurrence rate of 8.6%, and were classified as the recurrence group; 1 084 patients had no recurrence and were classified as the non-recurrence group. Seventy patients died, with a mortality rate of 5.9% (70/1 186). Significant differences were observed between the recurrence and non-recurrence groups in glycated hemoglobin (GHb), creatinine (Cr), high-density lipoprotein (HDL), and National Institutes of Health Stroke Scale (NIHSS) score on day 3 after admission (P<0.05). Model performance evaluation showed that the overall C-index of the RSF model was 0.815 in the training set and 0.704 in the validation set, both higher than those of the Cox regression model (0.784 and 0.678, respectively). Receiver operating characteristic (ROC) curve analysis showed that, at 12 months of follow-up, the area under the ROC curve (AUC) of the RSF model for predicting recurrence was 0.833 (95%CI=0.788-0.872) in the training set and 0.718 (95%CI=0.634-0.795) in the validation set. In contrast, the AUC of the Cox regression model was 0.800 (95%CI=0.748-0.849) in the training set and 0.690 (95%CI=0.602-0.765) in the validation set. Calibration curves and decision curve analysis showed that the RSF model achieved favorable clinical incremental net benefit, and that the predicted recurrence probabilities were consistent with the observed recurrence probabilities. SHAP value ranking showed that the top three risk factors for recurrence were NIHSS score, alanine aminotransferase (ALT), and Cr.

The RSF model developed in this study can effectively predict the 1-year risk of stroke recurrence in patients with large-artery atherosclerotic AIS. The top three factors associated with recurrence were NIHSS score at admission, ALT, and Cr. These findings may help clinicians perform individualized risk assessment and implement targeted interventions.