重症监护的能量消耗测量:个性化营养支持的意义。

Jiayang Chen, Kay Choong See
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引用次数: 0

摘要

背景:准确测量能量消耗(EE)对于优化危重患者的营养支持至关重要。间接量热法(IC)是常用的参考方法,但其在床边的可用性有限。因此,已经设计了许多预测方程来估计危重患者的情感表达,以及最近提出的其他更新颖的方法。目的:评估目前在重症监护中测量情感表达的方法,重点关注实际挑战、准确性、可行性和局限性。我们还将讨论这些方法如何有助于改善重症监护病房患者的营养支持策略,以获得更个性化和有效的解决方案。方法:在PubMed和EMBASE中全面检索2014年12月至2024年12月发表的研究。符合条件的研究比较了危重患者群体的情感表达测量方法。数据提取和质量评估遵循PRISMA指南。使用TRIPOD问卷评估对报告标准的依从性,使用PROBAST工具评估偏倚风险。结果:25项原始研究符合纳入标准并进行了分析。结论:每种方法都有其独特的优势和局限性。我们发现,虽然IC仍然是参考标准,但不太准确的预测方程具有更大的可访问性和易于实现性。新兴技术有望应用于临床。未来的研究应该解决实际的障碍并验证新的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Energy expenditure measurement in critical care: Implications for personalized nutrition support.

Energy expenditure measurement in critical care: Implications for personalized nutrition support.

Energy expenditure measurement in critical care: Implications for personalized nutrition support.

Energy expenditure measurement in critical care: Implications for personalized nutrition support.

Background: Accurate measurement of energy expenditure (EE) is critical for optimizing nutritional support in critically ill patients. Indirect calorimetry (IC) is the reference method used, but its availability at the bedside is limited. As a result, numerous predictive equations have been devised to estimate EE in critically ill patients, along with other more novel methods recently proposed.

Aim: To evaluate current methods of measuring EE in critical care, focusing on practical challenges, accuracy, feasibility, and limitations. We will also discuss how these methods contribute to improving nutrition support strategies for intensive care unit patients for a more personalised and effective solution.

Methods: A comprehensive search was conducted in PubMed and EMBASE for studies published from December 2014 to December 2024. Eligible studies compared EE measurement methods in critically ill populations. Data extraction and quality assessment followed PRISMA guidelines. Adherence to reporting standards was assessed using the TRIPOD questionnaire and risk of bias was evaluated using the PROBAST tool.

Results: Twenty five original studies met the inclusion criteria and were analysed.

Conclusion: Each method has unique strengths and limitations. We found that while IC remains the reference standard, less accurate predictive equations have greater accessibility and ease of implementation. Emerging technologies show promise for bedside applicability. Future research should address practical barriers and validate newer approaches.

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