Scholarlink

<p>This study aimed to characterize blood glucose trajectories during the early phase of enteral nutrition (EN) in critically ill patients and develop a predictive model for these trajectories to improve clinical management and patient outcomes. A retrospective analysis was conducted on critically ill patients who received continuous EN for ≥ 2 consecutive days, using data from the intensive care unit (ICU) of a tertiary hospital. Group-Based Trajectory Modeling (GBTM) was employed to identify distinct subgroups based on patterns of blood glucose changes. An early risk prediction model for trajectory classification was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm. A total of 478 patients met the inclusion criteria. Three distinct blood glucose trajectory subgroups were identified: Mild Hyperglycemia Stable (41.84%), Moderate Hyperglycemia Peaking (36.40%), and Severe Hyperglycemia Peaking (21.76%). The XGBoost model exhibited robust discriminative ability and good calibration. Key predictors of trajectory classification included insulin use, history of diabetes, C-reactive protein, and interleukin-6. This study highlights the heterogeneity of glycemic trajectories during the early phase of EN in critically ill patients. The developed XGBoost model demonstrated satisfactory predictive performance and may serve as a valuable tool for maintaining glycemic stability and optimizing outcomes in critically ill patients receiving nutritional support in the ICU.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and validation of glucose trajectory subphenotypes in critically ill patients on early enteral nutrition: a retrospective cohort study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月2日

查看全文
  • Chenxi Weng,
  • Jie Su,
  • Huijuan Wang,
  • Qi Lu,
  • Lixia Chen

摘要

This study aimed to characterize blood glucose trajectories during the early phase of enteral nutrition (EN) in critically ill patients and develop a predictive model for these trajectories to improve clinical management and patient outcomes. A retrospective analysis was conducted on critically ill patients who received continuous EN for ≥ 2 consecutive days, using data from the intensive care unit (ICU) of a tertiary hospital. Group-Based Trajectory Modeling (GBTM) was employed to identify distinct subgroups based on patterns of blood glucose changes. An early risk prediction model for trajectory classification was constructed using the eXtreme Gradient Boosting (XGBoost) algorithm. A total of 478 patients met the inclusion criteria. Three distinct blood glucose trajectory subgroups were identified: Mild Hyperglycemia Stable (41.84%), Moderate Hyperglycemia Peaking (36.40%), and Severe Hyperglycemia Peaking (21.76%). The XGBoost model exhibited robust discriminative ability and good calibration. Key predictors of trajectory classification included insulin use, history of diabetes, C-reactive protein, and interleukin-6. This study highlights the heterogeneity of glycemic trajectories during the early phase of EN in critically ill patients. The developed XGBoost model demonstrated satisfactory predictive performance and may serve as a valuable tool for maintaining glycemic stability and optimizing outcomes in critically ill patients receiving nutritional support in the ICU.