Prevalence of Underprescription or Overprescription of Energy Needs in Critically Ill Mechanically Ventilated Adults as Determined by Indirect Calorimetry: A Systematic Literature Review
Abstract
BACKGROUND: Underfeeding and overfeeding has been associated with adverse patient outcomes. Resting energy expenditure can be measured using indirect calorimetry. In its absence, predictive equations are used. A systematic literature review was conducted to determine the prevalence of underprescription and overprescription of energy needs in adult mechanically ventilated critically ill patients by comparing predictive equations to indirect calorimetry measurements. METHODS: Ovid MEDLINE, CINAHL Plus, Scopus, and EMBASE databases were searched in May 2013 to identify studies that used both predictive equations and indirect calorimetry to determine energy expenditure. Reference lists of included publications were also searched. The number of predictive equations that underestimated or overestimated energy expenditure by ±10% when compared to indirect calorimetry measurements were noted at both an individual and group level. RESULTS: In total, 2349 publications were retrieved, with 18 studies included. Of the 160 variations of 13 predictive equations reviewed at a group level, 38% underestimated and 12% overestimated energy expenditure by more than 10%. The remaining 50% of equations estimated energy expenditure to within ±10 of indirect calorimetry measurements. On an individual patient level, predictive equations underestimated and overestimated energy expenditure in 13-90% and 0-88% of patients, respectively. Differences of up to 43% below and 66% above indirect calorimetry values were observed. CONCLUSIONS: Large discrepancies exist between predictive equation estimates and indirect calorimetry measurements in individuals and groups. Further research is needed to determine the influence of indirect calorimetry and predictive equation limitations in contributing to these observed differences.
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Predictive equations are associated with significant inaccuracy (up to 60%), leading to over or under evaluation of the needs and inducing over or underfeeding.
“The predictive equations are associated with significant inaccuracy (up to 60%), leading to over or under evaluation of the needs and inducing over or underfeeding (120).”
From ESPEN guideline on clinical nutrition in the intensive care unit. (2019) · cites this paper as [120]
Numerous meta-analyses have demonstrated the poor value of predictive equations, and variability is increased because body weight remains difficult to accurately assess.
“Numerous metaanalyses have demonstrated the poor value of predictive equations [43,44], variability that is increased because body weight remains difficult to accurately assess [45].”
From ESPEN practical and partially revised guideline: Clinical nutrition in the intensive care unit (2023) · cites this paper as [44]
Previous studies have demonstrated the low accuracy of various REE predictive equations based on weight, height, age, gender, etc., for critical illness.
“Previous studies have demonstrated the low accuracy of various REE predictive equations based on weight, height, age, gender, etc., for critical illness [2, 25].”
From Energy delivery guided by indirect calorimetry in critically ill patients: a systematic review and meta-analysis (2021) · cites this paper as [2]
Predictive equations are mostly inaccurate in predicting REE in critically ill patients.
“However, these equations, which were originally developed basing data from healthy individuals, have been studied extensively and found to be mostly inaccurate in predicting REE in critically ill patients [2].”
From Energy delivery guided by indirect calorimetry in critically ill patients: a systematic review and meta-analysis (2021) · cites this paper as [2]
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ESPEN guideline on clinical nutrition in the intensive care unit.
Singer, P., Blaser, A. R., Berger, M. M., Alhazzani, W., Calder, P. C., Casaer, M. P., Hiesmayr, M., Mayer, K., Montejo, J. C., Pichard, C., et al. (2019) · Clinical Nutrition · doi:10.1016/j.clnu.2018.08.037 · Cited by 2,904 · see citing works
ESPEN practical and partially revised guideline: Clinical nutrition in the intensive care unit
Singer, P., Blaser, A. R., Berger, M. M., Calder, P. C., Casaer, M., Hiesmayr, M., Mayer, K., Montejo-Gonzalez, J. C., Pichard, C., Preiser, J.-C., et al. (2023) · Clinical Nutrition · doi:10.1016/j.clnu.2023.07.011 · Cited by 793 · see citing works
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Self-citation
- Authors
- Tatucu‐Babet, O. A., Ridley, E. J., & Tierney, A. C.
- Affiliations
- The Alfred Hospital (AU)
- La Trobe University (AU)
- Venue
- Journal of Parenteral and Enteral Nutrition
- Publisher
- Wiley
- Published
- 2015-01-20
- Volume
- 40
- Issue
- 2
- Pages
- 212-225
- ISSN
- 0148-6071 (ISSN-L)
- 1941-2444
- OpenAlex
- W2055203107
- Cited by
- 133
- References
- 3
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 16 · 2023: 12 · 2022: 9 · 2021: 17 · 2020: 20 · 2019: 15 · 2018: 13 · 2017: 3 · 2016: 5 · 2015: 7
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Parenteral and Enteral Nutrition (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Nursing › Nutrition and Dietetics › Clinical Nutrition and Gastroenterology
- Health Sciences › Medicine › Surgery › Hemodynamic Monitoring and Therapy
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Keywords
- resting energy expenditure, load forecasting, prediction equations, indirect calorimetry, mechanical ventilation, critically ill adults, underfeeding, overfeeding, enteral nutrition
- MeSH terms
- Energy Intake, Calorimetry, Indirect, Energy Metabolism, Humans, Nutritional Requirements, Respiration, Artificial, Prevalence, Randomized Controlled Trials as Topic, Critical Illness, Nutritional Support
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