Journal of Medical and Surgical Intensive Care Medicine 2019 , Vol 10, Issue 1
Energy Expenditure in Mechanically Ventilated Patients: Indirect Calorimetry vs Predictive Equations
Hülya SUNGURTEKİN 1 ,Serdar KARAKUZU 1 ,Simay SERİN 1
1Department of Anesthesiology, School of Medicine, Pamukkale University, Denizli, Turkey DOI : 10.33381/dcbybd.2019.1951 Background & Objectives: Indirect calorimetry(IC) is used in the calculation of energy consumption (EE) in critical care patients. In this study, it was aimed to compare the frequently used equations with IC in different body weight and disease classes and to determine relationship between them and disease severity.

Materials & Methods: 100 mechanically ventilated critical care patients were prospectively included in the study. Measurements were done on 3th, 4th and 5th days of ICU stay with IC and Harris Benedict (HB), Penn State 2003(PS), Schofield(SCH), Swinamer (SW) and Ireton-Jones(IJ) equations were calculated and APACHE II and SAPS II scores were determined. Bland-Altman limits of agreement analysis was done to determine the range of error with each predictive equation compared to the measured IC.

Results: The mean age±standard deviation was 66,10 ± 14,98 years and mean body mass index was 24,91 ± 4,45 kg.m-2 for the study group. Mean±standard deviation for APACHE II score and SAPS II were 23,42 ± 8,47 and 42,23 ± 10,62. Measured EE was 1828, 580 ± 436, 272 kcal/day. Correlation analysis between equations and IC showed that all equations were moderately correlated with IC. For all weight categories and equations , the limits-of agreement range was large. For the patient group, the bias was lowest with the PS predictive equation (mean error 14 kcal/ day). HB and PS equations have better agreement with IC than others do. No correlation was observed between severity scores and EE.

Conclusion: Predictive formulas for EE is not reliable in determining the energy, confidence intervals are wide in ICU patients necessitating mechanical ventilation. Keywords : indirect calorimetry, energy expenditure, predictive equations, intensive care, nutrition