Energy expenditure measurement in critical care: Implications for personalized nutrition support

Table 2 Studies included in review

No.	Ref.	Method	Sample size	Study findings and outcomes	Challenges of implementation
1	Ferreruela et al[36]	IC	60	Good accuracy at FiO2 ≤ 0.6	Requires stable FiO2 settings
2	Graham et al[38]	IC	56 patients (all male)	VO2 and REE were significantly altered in sepsis, offering potential for early sepsis diagnosis	ICU operational challenges, patient-specific contraindications
3	Grguric et al[42]	IC, PE	68 patients	Significant differences between predictive equations and measured REE. Predictive equations often underestimated energy expenditure	Complexity of accurately applying predictive equations
4	Hickmann et al[35]	IC, PE	49 ICU 15 healthy	Early exercise increased REE, influenced by inflammation markers	Control of exercise conditions in ICU
5	Jonckheer et al[59]	ET	19 CVVH runs	Bioenergetic imbalances ranged from -28% to +42% of REE; citrate use added significant non-intentional calories	Complexity of CVVH settings and caloric calculations
6	Jonckheer et al[59]	ET	10 patients	CO2 removal by CVVH slightly alters REE; changes were not clinically significant	Complexity of IC during CVVH
7	Kagan et al[53]	ET	80 patients, 497 measurements	Low agreement between REE-VCO2 and indirect calorimetry. Indirect calorimetry remains the gold standard	Variability in VCO2 accuracy, reliance on predefined respiratory quotient
8	Koekkoek et al[58]	ET	31 patients	VCO2 overestimated REE; low accuracy compared to IC	Technical challenges in integrating VCO2-based REE measurements
9	Kongpolprom[55]	PE	24	No predictive equation accurately estimated REE; Penn State 2010 was the most reliable	Cost and availability of IC in routine practice
10	Liew et al[57]	PE	108	HBE overestimated REE, especially in obese patients (BMI ≥ 30)	Resource constraints for IC use
11	Lindner et al[46]	IC, PE	90 ICU patients, 58 healthy controls	Predictive equations showed low accuracy rates; IC recommended	ICU-specific variables influencing REE measurements
12	Murray et al[52]	IC, PE	326 patients	Equations underestimated REE; IC superior, particularly in obese populations	Resource and training limitations for IC use
13	Niederer et al[37]	IC	38 patients over 7 weeks	Progressive hypermetabolism peaking at 3 weeks post-intubation; prolonged stress response	Operational challenges in maintaining longitudinal IC measurements
14	Oshima et al[31]	ET	278 patients	EEVCO2 showed insufficient accuracy compared to indirect calorimetry, particularly in critical states	Reliance on stable ventilator settings, limited utility in unstable patients
15	Rehal et al[40]	IC, ET	22 patients, 48 measurements	E-sCOVX and Quark RMR overestimated VO2 and VCO2 by 10% compared to Deltatrac II. Limits of agreement within ± 20%	High variability and technical setup requirements
16	Rousseau et al[56]	IC, PE	55	None of the predictive equations were accurate; Penn State showed closest agreement	Need for accessible IC devices
17	Saseedharan et al[50]	IC, ET, PE	58 patients, 117 paired measurements	EE from IC was significantly lower than weight-based predictions, highlighting risk of overfeeding	Resource constraints during the pandemic
18	Shinozaki et al[43]	ET	10 (4 post-surgery, 6 critically ill)	Continuous and repeat measurements matched gold standard	Integration with existing ICU setups
19	Slingerland-Boot et al[41]	IC	27 patients	Beacon showed acceptable reliability but slightly underestimated REE compared to Quark	Need for device calibration and standardization
20	Sobhy et al[44]	IC, PE	50 patients	Faisy-Fagon overestimated caloric needs; PSUm showed higher accuracy. 23 kcal/kg/day offered unbiased estimates.	Complexity in applying equations accurately in varied patient conditions
21	Stapel et al[48]	IC, ET, PE	84	Comparable accuracy to IC, better than predictive equations	Requires integration with ventilator systems
22	Tah et al[51]	PE	294 (acute phase), 180 (late phase)	Single predictive equation valid for both phases, REE influenced by height, weight, age, and minute ventilation	Dynamic metabolic changes affect predictive accuracy
23	Takemae et al[60]	IC, PE	95	Novel equation (KTE) outperformed existing equations for Japanese patients	Operational complexity of IC
24	Tatucu-Babet et al[45]	ET	21 patients	Feasible for early ICU admission; EE lower than predicted by equations; increased over time	Complex setup, reliance on specific ECMO protocols
25	Vest et al[54]	IC, PE	25 patients	IC showed predictive equations underestimated energy needs; actual intake often < 70% of target	IC feasibility low; reliance on empirical equations

PE: Predictive equations; ET: Emerging technologies; ICU: Intensive care unit; BMI: Body mass index; REE: Resting energy expenditure; HBE: Harris-Benedict equation; ECMO: Extracorporeal Membrane Oxygenation; CVVH: Continuous veno-venous hemofiltration; IC: Indirect calorimetry; COVID-19: Coronavirus disease 2019.