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Background <p>This study collected and analyzed clinical data on enteral nutrition therapy in neurocritical patients, developed and validated a risk prediction model for feeding intolerance (FI), and transformed the model into a visual risk scoring tool,provide a reference for clinical staff to screen for people at high risk of enteral nutrition feeding intolerance in neurocritically ill patients.</p> Methods <p>Using prospective study,440 eligible inpatients from a Chinese tertiary hospital (April–December 2022) were divided into derivation (70%) and validation (30%) cohorts.Univariate and binary logistic regression analyses were conducted to construct the FI prediction model, and a simplified risk assessment scale for FI in the neurological intensive care unit (NCU) was developed.</p> Results <p>FI incidence was 71.0% (213/300) in the derivation cohort. Independent risk factors included age, Glasgow Coma Scale (GCS) score, APACHE II score, mechanical ventilation, nasogastric tube feeding, hyperglycemia, and hypoalbuminemia (<i>P</i> &lt; 0.05). The model showed excellent discrimination (AUC = 0.941, 95% CI:0.912–0.970) and calibration (Hosmer–Lemeshow <i>P</i> = 0.293), with 85.9% sensitivity and 90.8% specificity. In the validation cohort (140 patients, FI incidence was 72.1%), predictive accuracy was 82.9% (AUC = 0.924, 95% CI:0.878–0.970; sensitivity=96.0%, specificity=74.4%). The visual scoring tool achieved 84.3% accuracy (Kappa=0.700, <i>P</i> &lt; 0.001), aligning with the original model.</p> Conclusion <p>The early enteral nutrition FI risk prediction model and corresponding scoring table developed in this study showed good predictive performance and could serve as a useful reference for the clinical assessment of FI risk in neurocritical patients.</p>

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Early prediction of enteral nutrition feeding intolerance risk in neurocritical patients and development of a simplified risk scoring tables

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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  • Rong Yuan,
  • Lei Liu,
  • Jiao Mi,
  • Xue Li,
  • Fang Yang,
  • Shifang Mao

摘要

Background

This study collected and analyzed clinical data on enteral nutrition therapy in neurocritical patients, developed and validated a risk prediction model for feeding intolerance (FI), and transformed the model into a visual risk scoring tool,provide a reference for clinical staff to screen for people at high risk of enteral nutrition feeding intolerance in neurocritically ill patients.

Methods

Using prospective study,440 eligible inpatients from a Chinese tertiary hospital (April–December 2022) were divided into derivation (70%) and validation (30%) cohorts.Univariate and binary logistic regression analyses were conducted to construct the FI prediction model, and a simplified risk assessment scale for FI in the neurological intensive care unit (NCU) was developed.

Results

FI incidence was 71.0% (213/300) in the derivation cohort. Independent risk factors included age, Glasgow Coma Scale (GCS) score, APACHE II score, mechanical ventilation, nasogastric tube feeding, hyperglycemia, and hypoalbuminemia (P < 0.05). The model showed excellent discrimination (AUC = 0.941, 95% CI:0.912–0.970) and calibration (Hosmer–Lemeshow P = 0.293), with 85.9% sensitivity and 90.8% specificity. In the validation cohort (140 patients, FI incidence was 72.1%), predictive accuracy was 82.9% (AUC = 0.924, 95% CI:0.878–0.970; sensitivity=96.0%, specificity=74.4%). The visual scoring tool achieved 84.3% accuracy (Kappa=0.700, P < 0.001), aligning with the original model.

Conclusion

The early enteral nutrition FI risk prediction model and corresponding scoring table developed in this study showed good predictive performance and could serve as a useful reference for the clinical assessment of FI risk in neurocritical patients.