ArticleBMC neurology2025
Factors influencing short-term and long-term survival rates in stroke patients receiving enteral nutrition: a machine learning approach using MIMIC-IV database.
Article in BMC neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Predicting time-to-event outcomes in critically ill patients with intracerebral hemorrhage using machine learning.The Journal of international medical research · 2026Article
- An interpretable AutoML-based prediction model for enteral nutrition intolerance in severe pulmonary tuberculosis patients and development of a clinical decision system.Frontiers in nutrition · 2026Article
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Authors and funding
6 authors.
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Abstract
purposeThis 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.
methodsThis 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.
resultsResults 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.
conclusionThis 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.
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