国家神经系统疾病临床医学研究中心数据管理与共享系统
Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
基本信息
- 负责人：LAMIA M BAREK
- 数据来源： CNSR-Ⅲ
- 论文编号：CNSR-Ⅲ20230084
- 发表时间：2024-10-16
2024-10-16
已发表文章
发表杂志：European Journal of Medical Research
发表类型: Original Contributions
影响因子(IF): 2.8
PMID: PMC11466038 Pubmed原文链接 全文下载
主要研究背景及目的
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方法
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.
结论
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.
主要结果
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).