Fan Xuehui, He Chenyiyi, Xu Jing, Ye Ruixue, Zhao Jingpu, Wang Yulong
Department of Rehabilitation Medicine, The Second, The First Affiliated Hospital of Shenzhen University, People's Hospital of Shenzhen, 3002 Sungang West Road, Futian District, Shenzhen, 518025, Guangdong Province, China.
Department of Rehabilitation, Shenzhen Children's Hospital, Shenzhen, Guangdong Province, China.
BMC Neurol. 2025 Apr 30;25(1):190. doi: 10.1186/s12883-025-04201-9.
This study aims to evaluate the survival and mortality rates of stroke patients after receiving enteral nutrition, and to explore factors influencing long-term survival. With an aging society, nutritional management of stroke patients has become a focus of clinical attention.
This study is based on the MIMIC-IV database, which contains patient data from healthcare institutions in the United States. We included 81 stroke patients who received enteral nutrition, encompassing various subtypes of stroke, specifically subarachnoid hemorrhage, cerebral infarction, and intracerebral hemorrhage. The exposure variable was the type of enteral nutrition, while the outcome variables were survival rates at 30 days, 1 year, and 3 years. Covariates included age, sex, Charlson Comorbidity Index, and minimum blood glucose levels. We employed Kaplan-Meier survival analysis and machine learning models to assess survival rates and their influencing factors.
Results showed a 30-day survival rate of 66.67%, indicating 27 patient deaths within the initial 30 days. The 1-year survival rate decreased to 45.68%, with a cumulative death count of 44 during the follow-up period. The 3-year survival rate was 43.21%, with a total of 46 deaths. Kaplan-Meier survival analysis indicated that low-risk group patients had significantly higher survival rates than the high-risk group (p = 0.0229), with higher survival probability in the first 600 days, while the high-risk group showed a significant decline at 400 days. Machine learning model evaluation showed that the XGBoost model had a C-index of 0.80 in predicting survival time, with the Charlson Comorbidity Index being the most important predictor (F score = 12.0). Additionally, factors such as lowest blood glucose, age, and hospital mortality flag significantly influenced survival time.
This study highlights the role of early intervention and nutritional management in improving stroke patient outcomes. Our findings suggest that the Charlson Comorbidity Index, age, and in-hospital mortality markers are major predictors of post-stroke survival. These findings underscore the necessity for personalised nutritional strategies, and they call for validation through prospective multicentre studies.
本研究旨在评估接受肠内营养的中风患者的生存率和死亡率，并探索影响长期生存的因素。随着社会老龄化，中风患者的营养管理已成为临床关注的焦点。
本研究基于MIMIC-IV数据库，该数据库包含来自美国医疗机构的患者数据。我们纳入了81例接受肠内营养的中风患者，涵盖各种中风亚型，具体为蛛网膜下腔出血、脑梗死和脑出血。暴露变量为肠内营养类型，而结局变量为30天、1年和3年的生存率。协变量包括年龄、性别、Charlson合并症指数和最低血糖水平。我们采用Kaplan-Meier生存分析和机器学习模型来评估生存率及其影响因素。
结果显示30天生存率为66.67%，表明在最初30天内有27例患者死亡。1年生存率降至45.68%，随访期间累计死亡44例。3年生存率为43.21%，共有46例死亡。Kaplan-Meier生存分析表明，低风险组患者的生存率显著高于高风险组（p = 0.0229），在前600天生存概率更高，而高风险组在400天时显著下降。机器学习模型评估显示，XGBoost模型在预测生存时间方面的C指数为0.80，Charlson合并症指数是最重要的预测因素（F分数 = 12.0）。此外，最低血糖、年龄和医院死亡率标志等因素对生存时间有显著影响。
本研究强调了早期干预和营养管理在改善中风患者预后中的作用。我们的研究结果表明，Charlson合并症指数、年龄和医院死亡率标志物是中风后生存的主要预测因素。这些发现强调了个性化营养策略的必要性，并呼吁通过前瞻性多中心研究进行验证。
