Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Genomics and personalized strategies in nutrition

Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements

A Correction to this article was published on 25 October 2021

This article has been updated

Abstract

Background

Predictive equations (PEs) for estimating resting energy expenditure (REE) that have been developed from acute phase data may not be applicable in the late phase and vice versa. This study aimed to assess whether separate PEs are needed for acute and late phases of critical illness and to develop and validate PE(s) based on the results of this assessment.

Methods

Using indirect calorimetry, REE was measured at acute (≤5 days; n = 294) and late (≥6 days; n = 180) phases of intensive care unit admission. PEs were developed by multiple linear regression. A multi-fold cross-validation approach was used to validate the PEs. The best PEs were selected based on the highest coefficient of determination (R2), the lowest root mean square error (RMSE) and the lowest standard error of estimate (SEE). Two PEs developed from paired 168-patient data were compared with measured REE using mean absolute percentage difference.

Results

Mean absolute percentage difference between predicted and measured REE was <20%, which is not clinically significant. Thus, a single PE was developed and validated from data of the larger sample size measured in the acute phase. The best PE for REE (kcal/day) was 891.6(Height) + 9.0(Weight) + 39.7(Minute Ventilation)−5.6(Age) – 354, with R2 = 0.442, RMSE = 348.3, SEE = 325.6 and mean absolute percentage difference with measured REE was: 15.1 ± 14.2% [acute], 15.0 ± 13.1% [late].

Conclusions

Separate PEs for acute and late phases may not be necessary. Thus, we have developed and validated a PE from acute phase data and demonstrated that it can provide optimal estimates of REE for patients in both acute and late phases.

Trial registration

ClinicalTrials.gov NCT03319329.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Exclusion flow diagram.

Similar content being viewed by others

Data availability

The data sets generated and/or analysed during the current study are not publicly available due to the data confidentiality requirements of the ethics committee but are available from the corresponding author on reasonable request and approval from the ethics committee.

Change history

References

  1. 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.). J Parenter Enter Nutr. 2016;40:159–211.

    Article  CAS  Google Scholar 

  2. Singer P, Blaser AR, Berger MM, Alhazzani W, Calder PC, Hiesmayr M, et al. ESPEN guideline on clinical nutrition in the intensive care unit. Clin Nutr. 2019;38:48–79.

    Article  PubMed  Google Scholar 

  3. Singer P, Anbar R, Cohen J, Shapiro H, Shalita-Chesner M, Lev S, et al. The tight calorie control study (TICACOS): a prospective, randomized, controlled pilot study of nutritional support in critically ill patients. Intensive Care Med. 2011;37:601–9.

    Article  PubMed  Google Scholar 

  4. 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;20:1–8.

    Article  Google Scholar 

  5. Singer P, De Waele E, Sanchez C, Ruiz Santana S, Montejo JC, Laterre PF, et al. TICACOS international: a multi-center, randomized, prospective controlled study comparing tight calorie control versus Liberal calorie administration study. Clin Nutr. 2021 40:380–7.

    Article  CAS  PubMed  Google Scholar 

  6. Heyland DK, Cahill N, Day AG. Optimal amount of calories for critically ill patients: depends on how you slice the cake! Crit Care Med. 2011;39:2619–26.

    Article  PubMed  Google Scholar 

  7. Singer P, Singer J. Clinical guide for the use of metabolic carts: indirect calorimetry—No longer the orphan of energy estimation. Nutr Clin Pr. 2016;31:30–8.

    Article  CAS  Google Scholar 

  8. Rattanachaiwong S, Singer P Should we calculate or measure energy expenditure? Practical aspects in the ICU. Vols. 55–56, Nutrition. Elsevier Inc.; 2018. p. 71–5.

  9. Frankenfield D, Coleman A, Alam S, Cooney RN. Analysis of estimation methods for resting metabolic rate in critically ill adults. J Parenter Enter Nutr. 2009;33:27–36.

    Article  Google Scholar 

  10. Harris JA, Benedict FG A biometric study of human basal metabolism. Washington, DC Carnegie Inst Washingt 1919;370–3.

  11. Mifflin D, Jeor T, A HL, Scott BJ, Daughertky SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51:241–7.

    Article  CAS  PubMed  Google Scholar 

  12. Swinamer DL, Grace MG, Hamilton SM, Jones RL, King EG, Roberts P. Predictive equation for assessing energy expenditure in mechanically ventilated critically ill patients. Crit Care Med. 1990;18:657–61.

    Article  CAS  PubMed  Google Scholar 

  13. Faisy C, Guerot E, Diehl JL, Labrousse J, Fagon JY. Assessment of resting energy expenditure in mechanically ventilated patients. Am J Clin Nutr. 2003;78:241–9.

    Article  CAS  PubMed  Google Scholar 

  14. Tah PC, Lee Z-Y, Poh BK, Abdul Majid H, Hakumat-Rai V-R, Mat Nor MB, et al. A single-center prospective observational study comparing resting energy expenditure in different phases of critical illness: indirect calorimetry versus predictive equations. Crit Care Med. 2020;48:e380–90.

    Article  PubMed  Google Scholar 

  15. Arabi YM, Aldawood AS, Haddad SH, Al-Dorzi HM, Tamim HM, Jones G, et al. Permissive underfeeding or standard enteral feeding in critically ill adults. N Engl J Med. 2015;372:2398–408.

    Article  CAS  PubMed  Google Scholar 

  16. Frankenfield D, Ashcraft CM, Galvan DA. Prediction of resting metabolic rate in critically ill patients at the extremes of body mass index. J Parenter Enter Nutr. 2013;37:361–7.

    Article  Google Scholar 

  17. Knofczynski GT, Mundfrom D. Sample sizes when using multiple linear regression for prediction. Educ Psychol Meas. 2008;68:431–42.

    Article  Google Scholar 

  18. Walter SD, Eliasziw M, Donner A. Sample size and optimal designs for reliability studies. Stat Med. 1998;17:101–10.

    Article  CAS  PubMed  Google Scholar 

  19. Oshima T, Berger MM, De Waele E, Guttormsen AB, Heidegger C-P, Hiesmayr M, et al. Indirect calorimetry in nutritional therapy. A position paper by the ICALIC study group. Clin Nutr. 2017 36:651–62.

    Article  PubMed  Google Scholar 

  20. Ejlerskov KT, Jensen SM, Christensen LB, Ritz C, Michaelsen KF, Mølgaard C. Prediction of fat-free body mass from bioelectrical impedance and anthropometry among 3-year-old children using DXA. Sci Rep. 2015;4:3889. May 27

    Article  CAS  Google Scholar 

  21. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Springe Ser Stat. 2009;27:764.

    Google Scholar 

  22. Frankenfield D, Roth-Yousey L, Compher C. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J Am Diet Assoc. 2005;105:775–89.

    Article  PubMed  Google Scholar 

  23. 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. J Parenter Enter Nutr. 2016;40:212–25.

    Article  CAS  Google Scholar 

  24. National Heart, Lung, and Blood Institute Acute Respiratory Distress Syndrome (ARDS) Clinical Trials Network, Rice TW, Wheeler AP, et al. Initial trophic vs full enteral feeding in patients with acute lung injury: the EDEN randomized trial. JAMA. 2012;307:795–803.

  25. Chapple L anne S, Weinel L, Ridley EJ, Jones D, Chapman MJ, Peake SL. Clinical sequelae from overfeeding in enterally fed critically Ill adults: where is the evidence? J Parenter Enter Nutr. 2019;00.

  26. Frankenfield D, Ashcraft CM. Estimating energy needs in nutrition support patients. J Parenter Enter Nutr. 2011;35:563–70. Sep 10

    Article  Google Scholar 

  27. Frankenfield D. Factors related to the assessment of resting metabolic rate in critically Ill patients. J Parenter Enter Nutr. 2019;43:234–44. Feb 21

    Article  Google Scholar 

  28. Mtaweh H, Jose M, Aguero S, Campbell M, Allard JP, Pencharz P, et al. Systematic review of factors associated with energy expenditure in the critically ill. Clin Nutr ESPEN. 2019;33:111–24.

    Article  PubMed  Google Scholar 

  29. Schulman RC, Mechanick JI. Metabolic and nutrition support in the chronic critical illness syndrome. Respir Care. 2012;57:958–77.

    Article  PubMed  Google Scholar 

  30. Psota T, Chen KY. Measuring energy expenditure in clinical populations: rewards and challenges. Eur J Clin Nutr. 2013;67:436–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Frankenfield D, Smith JS, Cooney RN. Validation of 2 approaches to predicting resting metabolic rate in critically ill patients. J Parenter Enter Nutr. 2004;28:259–64.

    Article  Google Scholar 

  32. Raurich JM, Llompart-Pou JA, Ferreruela M, Riera M, Homar J, Marse P, et al. A simplified equation for total energy expenditure in mechanically ventilated critically ill patients. Nutr Hosp. 2015;32:1273–80.

    PubMed  Google Scholar 

  33. Brandi LS, Santini L, Bertolini R, Malacarne P, Casagli S, Baraglia AM. Energy expenditure and severity of injury and illness indices in multiple trauma patients. Crit Care Med. 1999;27:2684–9.

    Article  CAS  PubMed  Google Scholar 

  34. Javed F, He Q, Davidson LE, Thornton JC, Albu J, Boxt L, et al. Brain and high metabolic rate organ mass: Contributions to resting energy expenditure beyond fat-free mass. Am J Clin Nutr. 2010;91:907–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hölzel C, Weidhase L, Petros S. The effect of age and body mass index on energy expenditure of critically ill medical patients. Eur J Clin Nutr. 2021;75:464–72. Mar 16

    Article  PubMed  Google Scholar 

  36. Müller MJ, Bosy-Westphal A, Kutzner D, Heller M. Metabolically active components of fat-free mass and resting energy expenditure in humans: recent lessons from imaging technologies. Obes Rev. 2002;3:113–22.

    Article  PubMed  Google Scholar 

  37. Purcell SA, Elliott SA, Walter PJ, Preston T, Cai H, Skipworth RJE, et al. Total energy expenditure in patients with colorectal cancer: associations with body composition, physical activity, and energy recommendations. Am J Clin Nutr. 2019;110:367–76.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Livingston EH, Kohlstadt I. Simplified resting metabolic rate-predicting formulas for normal-sized and obese individuals. Obes Res. 2005;13:1255–62.

    Article  PubMed  Google Scholar 

  39. Kiiski R, Takala J. Hypermetabolism and efficiency of CO2 removal in acute respiratory failure. Chest. 1994;105:1198–203.

    Article  CAS  PubMed  Google Scholar 

  40. Ismael S, Savalle M, Trivin C, Gillaizeau F, D’Auzac C, Faisy C. The consequences of sudden fluid shifts on body composition in critically ill patients. Crit Care. 2014;18:1–10.

    Article  Google Scholar 

  41. Soares MJ, Müller MJ. Resting energy expenditure and body composition: critical aspects for clinical nutrition. Eur J Clin Nutr. 2018;72:1208–14. Sep 5

    Article  CAS  PubMed  Google Scholar 

  42. Savalle M, Gillaizeau F, Maruani G, Puymirat E, Bellenfant F, Houillier P, et al. Assessment of body cell mass at bedside in critically ill patients. Am J Physiol Metab. 2012;303:E389–96.

    CAS  Google Scholar 

  43. Raurich JM, Ibanez J, Marse P. CO2 production and thermogenesis induced by enteral and parenteral nutrition. Nutr Hosp. 1996;11:108–13.

    CAS  PubMed  Google Scholar 

  44. Heymsfield SB, Casper K. Continuous nasoenteric feeding: bioenergetic and metabolic response during recovery from semistarvation. Am J Clin Nutr. 1988;47:900–10.

    Article  CAS  PubMed  Google Scholar 

  45. Frankenfield D. Impact of feeding on resting metabolic rate and gas exchange in critically Ill patients. J Parenter Enter Nutr. 2019;43:226–33.

    Article  CAS  Google Scholar 

  46. Stapel SN, de Grooth HJS, Alimohamad H, Elbers PWG, Girbes ARJ, Weijs PJM, et al. Ventilator-derived carbon dioxide production to assess energy expenditure in critically ill patients: Proof of concept. Crit Care. 2015;19.

  47. Rousing ML, Hahn-Pedersen MH, Andreassen S, Pielmeier U, Preiser JC. Energy expenditure in critically ill patients estimated by population-based equations, indirect calorimetry and CO2-based indirect calorimetry. Ann Intensive Care. 2016;6:1–11.

    Article  Google Scholar 

  48. Kagan I, Zusman O, Bendavid I, Theilla M, Cohen J, Singer P Validation of carbon dioxide production (VCO2) as a tool to calculate resting energy expenditure (REE) in mechanically ventilated critically ill patients: a retrospective observational study. Crit Care. 2018.

  49. Singer P. Simple equations for complex physiology: can we use VCO2 for calculating energy expenditure? Crit Care. 2016;20:72.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Funding Support: Ministry of Education Malaysia, Fundamental Research Grant Scheme (FRGS) [grant number FRGS/1/2018/SKK02/UM/02/3 with project code FP003-2018A]; Educational grant from the Parenteral and Enteral Nutrition Society of Malaysia (PENSMA). We are grateful to the subjects and their families for their participation in this project. We wish to thank all the personnel from the Intensive Care Unit, University of Malaya Medical Centre, Department of Anaesthesiology, University of Malaya, and Department of Dietetics, University of Malaya Medical Centre for their contributions and support of this project.

Funding

The study was funded by the Ministry of Education Malaysia, Fundamental Research Grant Scheme (FRGS) [grant number FRGS/1/2018/SKK02/UM/02/3 with project code FP003-2018A] awarded to Associate Professor Dr M. Shahnaz Hasan. Ms Tah Pei Chien received educational grant from the Parenteral and Enteral Nutrition Society of Malaysia (PENSMA). All other co-authors have no financial disclosures. The sponsor of the study had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript

Author information

Authors and Affiliations

Authors

Contributions

PCT, BKP, CCK, ZYL, VRHR, MBMN, MKZ, HAM and MSH contributed to the conceptualization and design of the study; PCT recruited the patients and collected the data; PCT, CCK and ZYL contributed to the acquisition and analysis of data; PCT, BKP, CCK, ZYL, HAM and MSH contributed to the interpretation of data; PCT drafted the manuscript; PCT and MSH contributed to the funding acquisition. All authors agree to be fully accountable for ensuring the integrity and accuracy of the work. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to M. Shahnaz Hasan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Ethics approval was granted by the Medical Research Ethics Committee (MREC), UMMC (MREC ID NO: 20161024-4407) and registered with the National Medical Research Register (NMRR) Malaysia (NMRR.ID: NMRR-16-2030-33143) and ClinicalTrials.gov (NCT03319329). Informed consent was obtained from the patients or their legal representative.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Edited by Professor Claudio Maffeis

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tah, P.C., Poh, B.K., Kee, C.C. et al. Do we need different predictive equations for the acute and late phases of critical illness? A prospective observational study with repeated indirect calorimetry measurements. Eur J Clin Nutr 76, 527–534 (2022). https://doi.org/10.1038/s41430-021-00999-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41430-021-00999-y

Search

Quick links