Your browser doesn't support javascript.
Energy requirements for critically ill patients with COVID-19.
Burslem, Ryan; Gottesman, Kimberly; Newkirk, Melanie; Ziegler, Jane.
  • Burslem R; Department of Clinical and Preventive Nutrition Sciences, School of Health Professions, Rutgers University, Newark, NJ, USA.
  • Gottesman K; Department of Clinical and Preventive Nutrition Sciences, Rutgers University, Newark, NJ, USA.
  • Newkirk M; Department of Clinical and Preventive Nutrition Sciences, Rutgers University, Newark, NJ, USA.
  • Ziegler J; Department of Clinical and Preventive Nutrition Sciences, Clinical Nutrition Program, Rutgers University, Newark, NJ, USA.
Nutr Clin Pract ; 37(3): 594-604, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1750423
ABSTRACT
Early reports suggested that predictive equations significantly underestimate the energy requirements of critically ill patients with coronavirus disease 2019 (COVID-19) based on the results of indirect calorimetry (IC) measurements. IC is the gold standard for measuring energy expenditure in critically ill patients. However, IC is not available in many institutions. If predictive equations significantly underestimate energy requirements in severe COVID-19, this increases the risk of underfeeding and malnutrition, which is associated with poorer clinical outcomes. As such, the purpose of this narrative review is to summarize and synthesize evidence comparing measured resting energy expenditure via IC with predicted resting energy expenditure determined via commonly used predictive equations in adult critically ill patients with COVID-19. Five articles met the inclusion criteria for this review. Their results suggest that many critically ill patients with COVID-19 are in a hypermetabolic state, which is underestimated by commonly used predictive equations in the intensive care unit (ICU) setting. In nonobese patients, energy expenditure appears to progressively increase over the course of ICU admission, peaking at week 3. The metabolic response pattern in patients with obesity is unclear because of conflicting findings. Based on limited evidence published thus far, the most accurate predictive equations appear to be the Penn State equations; however, they still had poor individual accuracy overall, which increases the risk of underfeeding or overfeeding and, as such, renders the equations an unsuitable alternative to IC.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Critical Illness / COVID-19 Type of study: Prognostic study / Reviews Limits: Adult / Humans Language: English Journal: Nutr Clin Pract Journal subject: Nutritional Sciences / Nursing Year: 2022 Document Type: Article Affiliation country: Ncp.10852

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Critical Illness / COVID-19 Type of study: Prognostic study / Reviews Limits: Adult / Humans Language: English Journal: Nutr Clin Pract Journal subject: Nutritional Sciences / Nursing Year: 2022 Document Type: Article Affiliation country: Ncp.10852