Concordancia entre la calorimetría indirecta y las ecuaciones predictivas para estimar gasto energético en reposo

Lina Maria Londoño-Londoño; Ángela Patricia  Montoya-Bernal; Fernando  Arango; José Fernando  Escobar-Serna; Maria Cristina Florián Pérez; Diana  Trejos-Gallego

doi:10.35454/rncm.v7n1.496

Authors

Lina Maria Londoño-Londoño
Ángela Patricia Montoya-Bernal
Fernando Arango
José Fernando Escobar-Serna
Maria Cristina Florián Pérez
Diana Trejos-Gallego

DOI:

https://doi.org/10.35454/rncm.v7n1.496

Keywords:

Indirect calorimetry, Energy metabolism, Critical care, Forecasting

Abstract

Introduction: determining energy expenditure is essential for critically ill patients because underfeeding or overfeeding increases their morbidity and mortality.

Objective: to determine the accuracy and concordance of resting energy expenditure (REE) measurement by indirect calorimetry (IC) and three predictive formulas (Harris-Benedict, rule of thumb, and Penn State) in ventilated patients in an ICU in Manizales, Colombia.

Methods: 31 patients hospitalized at S.E.S. Hospital Universitario de Caldas, with mechanical ventilation ≥48 hours were included. REE was calculated for all body weight variations. As a measure of the accuracy of the equations, the distribution of patients with REE below 80 % of that measured by IC (underestimation), between 80 % and 110 % (adequate), and >110 % (overestimation) was calculated, and from analyses with Bland-Altman, concordance was evaluated.

Results: the average REE per IC was 1441.1 (CI 95 %; 1205.7–1616.5) kcal/kg for women and 1624.5 (CI 95 %; 1414.7– 1834.2) for men. Accuracy analysis showed that the Penn State equation calculated with current weight had a concordance of 44.4 % and was the most underestimated with ideal and adjusted weight (51.9 %), and the rule of thumb calculated with current weight was the most overestimated (64.6 %), and the analysis with Bland-Altman graphs showed positive and negative biases.

Conclusions: a poor agreement was found between the different predictive equations and REE values by IC in critically ill patients in an ICU.

Downloads

Download data is not yet available.

References

Ndahimana D, Kim EK. Energy Requirements in Critically Ill Patients. Clin Nutr Res. 2018;7(2):81-90. doi: 10.7762/cnr.2018.7.2.81.

Faisy C, Lerolle N, Dachraoui F, Savard JF, Abboud I, Tadie JM, et al. Impact of energy deficit calculated by a predictive method on outcome in medical patients requiring prolonged acute mechanical ventilation. British Journal of Nutrition. 2009;101(7):1079–87. doi: 10.1017/S0007114508055669.

Giner M, Laviano A, Meguid MM, Gleason JR. In 1995 a correlation between malnutrition and poor outcome in critically ill patients still exists. Nutrition. 1996; 12:23-29. doi: 10.1016/0899-9007(95)00015-1.

Weijs PJ, Looijaard WG, Beishuizen A, Girbes AR, Oudemans-van Straaten HM. Early high protein intake is associated with low mortality and energy overfeeding with high mortality in non-septic mechanically ventilated critically ill patients. Crit Care. 2014; 18:701. doi: 10.1186/s13054-014-0701-z.

Vargas M, Lancheros P, Barrera MP. Gasto energético en reposo y composición corporal en adultos. Rev.fac.med. 2011,59 (1): 43-58. ISSN 0120-0011.

Singer P, Blaser AR, Berger MM, Alhazzani W, Calder PC, Casaer MP, et al. ESPEN guideline on clinical nutrition in the intensive care unit. Clinical Nutrition. 2019 Feb 1;38(1):48–79.

McClave SA, Taylor BE, Martindale RG, Warren MM, Johnson DR, Braunschweig C, et al. Guidelines for the Provision and Assessment of Nutrition Support Therapy in the Adult Critically Ill Patient: Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.). Journal of Parenteral and Enteral Nutrition. 2016;40(2):159–211.

Wichansawakun S, Meddings L, Alberda C, Robbins S, Gramlich L. Energy requirements and the use of predictive equations versus indirect calorimetry in critically ill patients. Appl Physiol Nutr Metab. 2015; 40:207-210. doi: 10.1139/apnm-2014-0276.

De Waele E, Jonckheer J, Wischmeyer PE. Indirect calorimetry in critical illness: ¿A new standard of care? Vol. 27, Current Opinion in Critical Care. 2021.1;27(4):334-43. doi: 10.1097/MCC.0000000000000844.

Zusman O, Theilla M, Cohen J, Kagan I, Bendavid I, Singer P. Resting energy expenditure, calorie and protein consumption in critically ill patients: A retrospective cohort study. Crit Care. 2016 Nov 10;20(1). doi: 10.1186/s13054-016-1538-4.

Reid CL. Poor agreement between continuous measurements of energy expenditure and routinely used prediction equations in intensive care unit patients. Clin Nutr. 2007; 26:649-657. doi: 10.1016/j.clnu.2007.02.003.

Chumlea WC, Guo S, Steinba Ugh ML. Prediction of stature from knee height for black and white adults and children with application to mobility-impaired or handicapped persons. JAm Diet Assoc. 1994;94(12):1285-8. doi: 10.1016/0002-8223(94)92540-2.

de Vries MC, Koekkoek WK, Opdam MH, van Blokland D, van Zanten AR. Nutritional assessment of critically ill patients: validation of the modified NUTRIC score. Eur J Clin Nutr. 2018; 72:428-435. doi: 10.1038/s41430-017-0008-7.

Schlein KM, Coulter SP. Best practices for determining resting energy expenditure in critically Ill adults. Nutr Clin Pract. 2014; 29(1):44–55. doi: 10.1177/0884533613515002.

Harris BJ, Benedict FG. A biometric study of human basal metabolism. Vol. 23, Bull. Amer. Math. Soc. Veit & Co; 1918.

Frankenfield D. Validation of an equation for resting metabolic rate in older obese, critically ill patients. JPEN J Parenter Enteral Nutr. 2011;35(2):264-9. doi: 10.1177/0148607110377903.

Nuttall FQ. Body mass index: Obesity, BMI, and health: A critical review. Nutr Today. 2015; 50:117-128. doi: 10.1097/NT.0000000000000092.

Flancbaum L, Choban PS, Sambucco S, Verducci J, Burge JC. Comparison of indirect calorimetry, the Fick method, and prediction equations in estimating the energy requirements of critically ill patients. Am J Clin Nuutr. 1999; 69:461–6. doi: 10.1093/ajcn/69.3.461.

Tatucu-Babet OA, Ridley EJ, Tierney AC. Prevalence of underprescription or overprescription of energy needs in critically ill mechanically ventilated adults as determined by indirect calorimetry: A systematic literature review. JPEN J Parenter Enteral Nutr. 2016; 40:212-225. doi: 10.1177/0148607114567898.

Aliasgharzadeh S, Mahdavi R, Asghari Jafarabadi M, Namazi N. Comparison of Indirect Calorimetry and Predictive Equations in Estimating Resting Metabolic Rate in Underweight Females. 2015;44(6):822-29.

Jagim AR, Camic CL, Kisiolek J, Luedke J, Erickson J, Jones MT, et al. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res. 2018;32(7):1875–81. doi: 10.1519/JSC.0000000000002111.

Alexander E, Susla GM, Burstein AH, Brown DT, Ognibene FP. Retrospective evaluation of commonly used equations to predict energy expenditure in mechanically ventilated, critically ill patients. Pharmacotherapy. 2004; 24(12):1659-67. doi: 10.1592/phco.24.17.1659.52342.

Kamel AY, Robayo L, Liang D, Rosenthal MD, Croft CA, Ghita G, et al. Estimated vs measured energy expenditure in ventilated surgical-trauma critically ill patients. PEN J Parenter Enteral Nutr. 2022; 46 (6): 1431-40. doi: 10.1002/jpen.2314.

Espinosa JJ, Vergara A, Landaeta DP. Calorimetría indirecta versis Harris-Benedict para determinar gasto energético basal en pacientes ventilados. Colegio Mayor del Rosario. 201. doi: 10.48713/10336_3168