Nutritional support in trauma and critically ill patients
DOI:
10.1007/s10353-025-00872-3
Publication Date:
2025-05-12T08:51:13Z
AUTHORS (5)
ABSTRACT
Summary
Background
Trauma and critical illness induce profound metabolic and inflammatory responses, thus leading to increased energy and protein requirements. Inadequate nutritional support is associated with poor outcomes, including infections, prolonged mechanical ventilation, and higher mortality. Despite advancements, challenges remain in terms of optimizing nutritional strategies for these vulnerable populations. The aim of this review is to explore the current evidence on nutritional support for trauma and critically ill patients.
Methods
A systematic search of PubMed was conducted, focusing on energy estimation, protein requirements, timing, delivery methods, and monitoring strategies of nutritional support. Particular attention was given to emerging technologies and tailored approaches.
Results
This review highlights the importance of early enteral nutrition (EN) within 24–48 h, prioritizing post-pyloric feeding in cases of gastrointestinal intolerance. Simplified energy calculations (20–30 kcal/kg/day) are recommended when indirect calorimetry is unavailable. Protein needs range from 1.5–2.5 g/kg/day, particularly in hypercatabolic states. Biochemical markers, including CRP and procalcitonin, guide adjustments by contextualizing inflammatory responses. Bioimpedance analysis and exhaled CO2 measurements represent promising advances in dynamic nutritional monitoring, offering noninvasive alternatives to traditional methods. Multidisciplinary collaboration is essential to address unique challenges, such as trauma-induced metabolic dysregulation and complications like refeeding syndrome.
Conclusion
Optimized nutritional support in trauma and critically ill patients requires a combination of evidence-based practices and individualized care. Early initiation, dynamic monitoring, and advanced technologies are key to improving outcomes. Future research should refine predictive models and explore novel interventions such as advanced monitoring technologies to further enhance recovery in these populations.
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