Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
Predictive Risk factor of recurrence stroke in young adults
First author：LAMIA M BAREK
Corresponding author：Yongjun Wang
essential_information
- Person in charge：LAMIA M BAREK
- Data Sources： CNSR-Ⅲ
- Number of papers：CNSR-Ⅲ20230084
- Published time：2024-10-16
2024-10-16
Published articles
Journal：European Journal of Medical Research
Publication type: Original Contributions
IF(IF): 2.8
PMID: PMC11466038 Link To Pubmed Full text Download
Background And Purpose
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Method
Methods We enrolled AIS patients aged between 18 and 50 years from the Third Chinese National Stroke Registry (CNSR-III), collected between 2015 and 2018. A modified Rankin Scale (mRS) ≥ 3 was a poor functional outcome at 3 months. Four ML tree models were developed: The extreme Gradient Boosting (XGBoost), Light Gradient Boosted Machine (lightGBM), Random Forest (RF), and The Gradient Boosting Decision Trees (GBDT), compared with logistic regression. We assess the model performance based on both discrimination and calibration.
Conclusion
Conclusions ML outperformed logistic regression, where XGBoost the boost was the best model for predicting poor functional outcomes in young AIS patients. It is important to consider living alone conditions with high severity scores to improve stroke prognosis.
Main results
Results A total of 2268 young patients with a mean age of 44.3 ± 5.5 years were included. Among them, (9%) had poor functional outcomes. The mRS at admission, living alone conditions, and high National Institutes of Health Stroke Scale (NIHSS) at discharge remained independent predictors of poor 3-month outcomes. The best AUC in the test group was XGBoost (AUC = 0.801), followed by GBDT, RF, and lightGBM (AUCs of 0.795, 0, 794, and 0.792, respectively). The XGBoost, RF, and lightGBM models were significantly better than logistic regression (P < 0.05).