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Predictive equations for energy needs for the critically ill.

Abstract

Nutrition may affect clinical outcomes in critically ill patients, and providing either more or fewer calories than the patient needs can adversely affect outcomes. Calorie need fluctuates substantially over the course of critical illness, and nutrition delivery is often influenced by: the risk of refeeding syndrome; a hypocaloric feeding regimen; lack of feeding access; intolerance of feeding; and feeding-delay for procedures. Lean body mass is the strongest determinant of resting energy expenditure, but age, sex, medications, and metabolic stress also influence the calorie requirement. Indirect calorimetry is the accepted standard for determining calorie requirement, but is unavailable or unaffordable in many centers. Moreover, indirect calorimetry is not infallible and care must be taken when interpreting the results. In the absence of calorimetry, clinicians use equations and clinical judgment to estimate calorie need. We reviewed 7 equations (American College of Chest Physicians, Harris-Benedict, Ireton-Jones 1992 and 1997, Penn State 1998 and 2003, Swinamer 1990) and their prediction accuracy. Understanding an equation's reference population and using the equation with similar patients are essential for the equation to perform similarly. Prediction accuracy among equations is rarely within 10% of the measured energy expenditure; however, in the absence of indirect calorimetry, a prediction equation is the best alternative.

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  • Authors+Show Affiliations

    ,

    Nutrition and Food Services, Michael E DeBakey Veteran Affairs Medical Center, 2002 Holcombe Boulevard, Room 4a-340, Houston TX 77030, USA. renee.walker2@va.gov

    Source

    Respiratory care 54:4 2009 Apr pg 509-21

    MeSH

    Body Weight
    Calorimetry, Indirect
    Critical Illness
    Energy Intake
    Energy Metabolism
    Humans
    Nutrition Assessment
    Nutritional Support
    Respiration, Artificial

    Pub Type(s)

    Journal Article
    Review

    Language

    eng

    PubMed ID

    19327188

    Citation

    Walker, Renee N., and Roschelle A. Heuberger. "Predictive Equations for Energy Needs for the Critically Ill." Respiratory Care, vol. 54, no. 4, 2009, pp. 509-21.
    Walker RN, Heuberger RA. Predictive equations for energy needs for the critically ill. Respir Care. 2009;54(4):509-21.
    Walker, R. N., & Heuberger, R. A. (2009). Predictive equations for energy needs for the critically ill. Respiratory Care, 54(4), pp. 509-21.
    Walker RN, Heuberger RA. Predictive Equations for Energy Needs for the Critically Ill. Respir Care. 2009;54(4):509-21. PubMed PMID: 19327188.
    * Article titles in AMA citation format should be in sentence-case
    TY - JOUR T1 - Predictive equations for energy needs for the critically ill. AU - Walker,Renee N, AU - Heuberger,Roschelle A, PY - 2009/3/31/entrez PY - 2009/3/31/pubmed PY - 2009/6/12/medline SP - 509 EP - 21 JF - Respiratory care JO - Respir Care VL - 54 IS - 4 N2 - Nutrition may affect clinical outcomes in critically ill patients, and providing either more or fewer calories than the patient needs can adversely affect outcomes. Calorie need fluctuates substantially over the course of critical illness, and nutrition delivery is often influenced by: the risk of refeeding syndrome; a hypocaloric feeding regimen; lack of feeding access; intolerance of feeding; and feeding-delay for procedures. Lean body mass is the strongest determinant of resting energy expenditure, but age, sex, medications, and metabolic stress also influence the calorie requirement. Indirect calorimetry is the accepted standard for determining calorie requirement, but is unavailable or unaffordable in many centers. Moreover, indirect calorimetry is not infallible and care must be taken when interpreting the results. In the absence of calorimetry, clinicians use equations and clinical judgment to estimate calorie need. We reviewed 7 equations (American College of Chest Physicians, Harris-Benedict, Ireton-Jones 1992 and 1997, Penn State 1998 and 2003, Swinamer 1990) and their prediction accuracy. Understanding an equation's reference population and using the equation with similar patients are essential for the equation to perform similarly. Prediction accuracy among equations is rarely within 10% of the measured energy expenditure; however, in the absence of indirect calorimetry, a prediction equation is the best alternative. SN - 0020-1324 UR - https://www.unboundmedicine.com/medline/citation/19327188/Predictive_equations_for_energy_needs_for_the_critically_ill_ L2 - http://www.rcjournal.com/contents/04.09/04.09.0509.pdf DB - PRIME DP - Unbound Medicine ER -