Estimation Of Resting Energy Expenditure Research Articles

Evaluating predictive equations for energy requirements throughout breast cancer trajectory: A comparative study

Background & AimsAccurately estimating resting energy requirements is crucial for optimizing energy intake, particularly in the context of patients with varying energy needs, such as individuals with cancer. We sought to evaluate the agreement between resting energy expenditure (REE) predicted by 40 equations and that measured by reference methods in women undergoing active breast cancer treatment stage (I-IV) and post-completion (i.e., survivors). MethodsData from 4 studies were combined. REE values estimated from 40 predictive equations identified by a systematic search were compared with REE assessed by indirect calorimetry (IC) using a metabolic cart (MC-REE N=46) or a whole-room indirect calorimeter (WRIC-REE N=44). Agreement between methods was evaluated using Bland-Altman and Lin's concordance coefficient correlation (Lin's CCC). Results90 participants (24 % survivors, 61.1% had early-stage breast cancer I or II, mean age: 56.8 ± 11 years; body mass index: 28.7 ± 6.4 kg/m2) were included in this analysis. Mean MC-REE and WRIC-REE values were 1389 ± 199 kcal/day and 1506 ± 247 kcal/day, respectively. Limits of agreement were wide for all equations compared to both MC and WRIC (∼ 300kcal for both methods), including the most commonly used ones, such as Harris-Benedict and Mifflin ST. Jeor equations; none had a bias within ±10% of measured REE, and all had low agreement per Lin's CCC analysis (< 0.90). The Korth equation exhibited the best performance against WRIC and the Lvingston-Kohlstadt equation against MC. Similar patterns of bias were observed between survivors and patients and between patients with stages I-III versus IV cancer. ConclusionMost equations failed to accurately predict REE at the group level, and none were effective at the individual level. This inaccuracy has significant implications for women with or surviving breast cancer, who may experience weight gain, maintenance, or loss due to inaccurate energy needs estimations. Therefore, our research underscores the need for further efforts to improve REE estimation.

Assessment of Energy Consumption Patterns and Validation of Predictive Equations among ICU Patients at Muhimbili National Hospital in Dar es Salaam, Tanzania

Background: Nutritional support in critically-ill patients is one of the most important parameters guarding the prognosis and influencing morbidity and mortality in these patients, owing to that fact, accurate measurement of the resting energy expenditure using Indirect calorimetry is recommended by guidelines as a gold standard. But due to lack of resources and other technicalities predictive equations are conveniently used as surrogates. Aims: This study was intended to examine the extent of malnutrition, poor nutrition support practices and to validate the common used predictive equations in critically-ill patients in our setting. Methodology: A hospital-based descriptive cross-sectional study was conducted on consecutively sampled 110 mechanically ventilated ICU patients at Muhimbili National Hospital Mloganzila. Anthropometric measurements, duration of stay in ICU, Temperature and Minute Volumes were recorded so as to estimate resting energy expenditure, REE from different predictive equations. Using Indirect Calorimetry Module patient’s REE was measured and recorded then a statistical data analyzed using SPSS software version 23. Results: The prevalence of poor nutritional support was 69%; underfeeding and overfeeding were observed in 41.8% and 27.3% of all participants respectively. Prevalence of malnutrition was 51.8%; underweight and overweight were found to be in 29.1% and 22.7% of all participants respectively. The accuracy of predictive equation in ±10% difference was 30%, 45.5%, 46.4% and 68.2% in HB, MSJ, ESPEN and PENN respectively. Conclusion and Recommendation: Malnutrition and Poor Nutritional support are common problems in ICU. Predictive equations have poor accuracy and validity in comparison to indirect calorimetry. Penn State Equation was the most accurate and with the highest agreement with IC.

Resting energy expenditure based on equation estimation can predict renal outcomes in patients with type 2 diabetes mellitus and biopsy-proven diabetic kidney disease

Aims The aim of this study was to investigate the relationship between resting energy expenditure (REE) based on equation estimation and renal outcomes in patients with diabetes kidney disease (DKD). Methods A total of 124 patients were enrolled from a retrospective cohort of Type 2 Diabetes mellitus (T2DM) patients with biopsy-proven DKD. Renal outcome defined as End-Stage Renal Disease (ESRD). To compare the predictive ability of different REE estimation equations on ESRD. Patients’ REE was assessed according to the estimating equation with the best predictive power, and then the relationship between REE and ESRD risk was fitted using a restricted cubic spline curve (RCS) plot and REE cutoff values were obtained. Grouping using cutoff values, and ultimately evaluate the relationship between REE and the risk of ESRD using a Multivariate Cox regression model. Results The strongest predictive validity for renal outcomes was the NDCKD-equation. The patients were divided into the higher-REE group (n = 78) and the lower-REE group (n = 46), based on the cutoff value. During the follow-up, 30 of 124 patients (24.2%) proceeded to ESRD. Multivariate Cox regression models showed that the risk of ESRD in patients with lower REE was 6.08 times increased compared with that in those with higher REE (HR = 6.08; 95% CI, 1.28–28.80, p = 0.023). Conclusion These findings suggested that the lower REE was an independent risk factor for unfavorable renal outcomes in patients with DKD.

Accuracy of equations for estimating resting energy expenditure in children and adolescents living with phenylketonuria.

Measuring resting energy expenditure (REE) in individuals living with phenylketonuria (PKU) using indirect calorimetry (IC) is unusual in healthcare facilities because it requires specific protocols and expensive equipment. Considering that determining REE is crucial for devising nutritional strategies for the management of PKU, the aim of this study was to identify the predictive equations that provide the best estimates of REE in children and adolescents living with PKU and to propose a predictive equation for determining REE in this population. An REE concordance study was conducted with children and adolescents living with PKU. Anthropometric and body composition assessments using bioimpedance and REE assessment using IC were performed. The results were compared to 29 predictive equations. Fifty-four children and adolescents were evaluated. The REE obtained using IC differed from all estimated REE, except Henry's equation for male children (p = 0.058). Only this equation showed good agreement (0.900) with IC. Eight variables were associated with the REE obtained using IC with emphasis on fat-free mass (kg) (r = 0.786), weight (r = 0.775), height (r = 0.759) and blood phenylalanine (r = 0.503). With these variables, three REE equations were suggested, with R2 = 0.660, 0.635 and 0.618, respectively, and the third equation, which involves weight and height, showed adequate sample size for a statistical power of 0.942. Most equations, not specific for individuals living with PKU, overestimate the REE of this population. We propose a predictive equation for assessing REE for children and adolescents living with PKU to be used in settings where IC is not available.

Development and validation of new predictive equations for resting energy expenditure in physically active boys

Measurement or estimation of resting energy expenditure (REE) should be the first step in determining energy demand in physically active boys. The purpose of this study was to develop and validate new equations for resting energy expenditure in male children and adolescents practicing soccer. The cross-sectional studywas carried out among 184 boys in the derivation group and 148 boys in the validation group (mean age 13.20 ± 2.16 years and 13.24 ± 1.75 years, respectively). The calorimeter and device for assessing body composition by bioelectrical impedance analysis (BIA) were used. Model of multiple regression showed that REE can be predicted in this population with Eq. (1) (with height and weight data) or Eq. (2) (with age, height, and fat free mass data). Predictive Eq. (1) had an average error of 51 ± 199 kcal and predictive Eq. (2) − 39 ± 193 kcal. Cohen's d coefficient was 0.2, which confirms the small difference. The bias was 4.7% and 3.9%, respectively. The accuracy was 61.2% in the population for predictive Eq. (1) and 66.2% for predictive Eq. (2). Therefore, the new equations developed and validated in this study are recommended for the estimation of REE in physically active boys, when the use of IC is not feasible or available.

Comparison of resting energy expenditure measured with metabolic cart and calculated with predictive formulas in critically ill patients on mechanical ventilation

IntroductionThe purpose was to compare the resting energy expenditure (REE) measured with the Q-NRG™+ metabolic-cart (MREE) with REE predicted by equations (the Harris-Benedict formula and an equation developed in ward, REE-HB and REE-W, respectively). We also aimed to assess the agreement of the measurements of oxygen consumption (V̇O2) and carbon dioxide production (V̇CO2) at different inspired fractions of oxygen (FiO2). Methods27 mechanically ventilated ICU patients were enrolled. V̇O2 and V̇CO2 were measured by Q-NRG™+ during breathing 40% and 60% FiO2. MREE was compared with REE-W and REE-HB normalized for body weight. ResultsV̇O2 was 233.0 (95.2) ml/min and 217.5 (89.8) ml/min at FiO2 40% and 60%, respectively (NS). V̇CO2 was 199.0 (91.7) ml/min at FiO2 40%, and 197.5 (85.5) ml/min at FiO2 60% (NS). The REE estimated from the equations was significantly different from the MREE. The best agreement was found for the Harris-Benedict equation without correction for stress-factors. Harris-Benedict equation corrected overestimates REE. ConclusionsThis new metabolic cart Q-NRG™+ provides a concordance of values for V̇O2 and V̇CO2 when measured at different FiO2, and is a reliable tool for estimating energy expenditure and assessing the nutritional needs of the patient. This study demonstrates that the estimation of REE using predictive formulas does not allow accurate calculation of metabolic demands in ventilated intensive care patient. However, predictive equations allow for a rapid assessment of REE and calculation of the amount of energy derived from different substrates.

Accuracy of predictive equations for resting energy expenditure estimation in mechanically ventilated Thai patients.

Indirect calorimetry (IC) is the most precise approach for estimating calorie demand in critically ill patients. Despite this, owing to unaffordable devices, it is rarely used in practice. Predictive equations are the alternatives. To assess the accuracy of 14 predictive resting energy expenditure(REE) equations in ventilated Thai patients. We compared the accuracy and agreement of 14 equations. The equations included the American College of Chest Physicians(ACCP) equation, Harris-Benedict equation(HBE), 1.2×HBE, 1.5×HBE, Mifflin-St. Jeor(MSJ), Ireton-Jones 1992 and 2002, Penn State 2003(HBE and MSJ) and 2010, Swinamer 1990, Faisy, Brandi 1999, and 25 kcal/kg equation. An equation was ascertained as accurate if the calculated values fell within ±10% of the measured REEs. Spearman correlation coefficient, Bland-Altman method, and intraclass correlation coefficient were used to analysis. We obtained data from 24 ventilated patients undergoing REE measurement by IC. Fifty percent of them were male with a median age of 64.5 years, a median height of 160 cm, and a median body mass index of 22.95 kg/m2. The predictive precision of all equations was poor, with largely different accuracies from 6.7% to 48.1%. The most reliable equation was Penn State 2010. The ACCP, HBE, MSJ, and Penn State 2003(HBE) tended to underestimate calorie need. Contrastingly, the other equations tended to overestimate REEs. Despite a moderate degree of correlations, the Bland-Altman plots demonstrated clinically unacceptable discrepancies between measured REE and REE calculated by each equation. In ventilated Thai patients, there were no precise equations for determining REE.

Verification of the Theoretical Energy Requirement of Underweight, Normal Weight and Overweight Young Women Using Ergospirometry

The research aim was to verify the theoretic energy requirement of young women and to calculate correlations between body mass index (BMI) and energy requirement, on the one hand, and the parameters obtained from bioelectrical impedance analysis on the other. The study was conducted in a group of 32 women grouped based on their BMI: underweight n = 8, normal weight n = 15, and overweight n = 9. Predictive formulas were observed to underestimate the energy requirement compared to values obtained with the use of ergospirometry. The smallest difference in Resting Energy Expenditure (REE) estimates using an ergospirometry apparatus was observed for the Cunningham equation (underweight: 1507 vs 1350 kcal) and the Harris-Benedict equation (normal weight: 1641 vs 1437 kcal; overweight: 1882 vs 1609 kcal). A significant statistical correlation was recorded in the juxtaposition of BMI to weight in the normal weight and in the overweight group. REE estimation based on formulas has only limited predictive value. Indirect calorimetry is recommended for measuring the REE rather than predictive equations.

Resting energy expenditure in Korean type 2 diabetes patients: comparison between measured and predicted values.

Estimation of energy demand using resting energy expenditure (REE) is a reasonable approach for optimizing glycemic control and weight management in patients with type 2 diabetes mellitus (T2DM). This study aimed to compare REE predictions and objective measurements in patients with T2DM in Korea. This study enrolled 36 participants with T2DM (age range, 20-60 years). Anthropometric variables including height, weight, waist-hip ratio, blood pressure, body fat, body fat percentage, and total body weight were measured using bioimpedance. REE was evaluated using indirect calorimetry. The measured REE values were compared to values estimated using five predictive equations: the Harris-Benedict, Mifflin, Owen, Food and Agriculture Organization of the United Nations/World Health Organization (FAO/WHO), and Schofield equations. This study evaluated the associations between measured REE values and anthropometric/clinical data, including height, weight, and age, using multivariate linear regression. The mean measured REE value was 1891.79 ± 288.03 kcal/day (male), 1,502.00 ± 202.96 kcal/day (female). REE estimates generated from the Mifflin equation showed the largest differences from measured REE values, whereas estimates derived from the FAO/WHO equation were the closest to the measured REE values. This study also identified associations between measured REE values and various anthropometric/clinical variables. The accuracy of REE prediction equations is critically important in promoting the efficacy of dietary counseling and the effective treatment of diabetes. Our results indicate the need for additional studies informing more suitable methods for determining the energy requirements of Korean patients with T2DM.

TG/HDL Ratio Is an Independent Predictor for Estimating Resting Energy Expenditure in Adults with Normal Weight, Overweight, and Obesity

Factors that determine resting energy expenditure (REE) remain under investigation, particularly in persons with a high body mass index (BMI). The accurate estimation of energy expenditure is essential for conducting comprehensive nutrition assessments, planning menus and meals, prescribing weight and chronic disease interventions, and the prevention of malnutrition. This study aimed to: (a) determine the contribution of cardiometabolic biomarkers to the inter-individual variation in REE in persons categorized by BMI; and (b) assess the contribution of these biomarkers in the prediction of REE when persons of varying BMI status were categorized by their glycemic and metabolic syndrome status. Baseline data from 645 adults enrolled in diet intervention trials included REE measured by indirect calorimetry, body composition by dual energy X-ray absorptiometry, anthropometrics, and cardiometabolic biomarkers. Multivariate linear regression modeling was conducted to determine the most parsimonious model that significantly predicted REE by BMI category, metabolic syndrome status, and glycemic status. Modeling with the traditional predictors (age, sex, height, weight) accounted for 58-63% of the inter-individual variance in REE. When including age, sex, height, weight and fat-free mass as covariates, adding TG/HDL to regression modeling accounted for 71-87% of the variance in REE. The finding that TG/HDL is an independent predictor in estimating REE was further confirmed when participants were categorized by metabolic syndrome status and by glycemic status. The clinical utility of calculating the TG/HDL ratio not only aids health care providers in identifying patients with impaired lipid metabolism but can optimize the estimation of REE to better meet therapeutic goals for weight and disease management.

Accuracy of Resting Energy Expenditure Estimation Equations in Polio Survivors

ObjectiveTo evaluate the accuracy of 4 equations validated for the general population to determine resting energy expenditure (REE) in polio survivors. DesignA descriptive, ambispective, single-center observational cohort study of minimal risk care. SettingTertiary university care hospital. ParticipantsDATAPOL database of polio survivors followed up in a specialist department (N=298). InterventionsNone. Main Outcomes MeasuresREE measurement by indirect calorimetry and estimated REE using 4 equations and comparing the values with indirect calorimetry. Analysis of correlations between measured REE and weight, height, and body mass index (BMI) and indicators of severity of polio sequelae. ResultsOf the 298 polio cases in the database between January 2014 and May 2017, 41 were included (19 men and 22 women). Mean±SD BMI was 26.0±5.6 kg/m2 (56.1% below 25). Measured REE correlated significantly and positively with weight and weaker with BMI. Correlations between measured and estimated REE were strong (between 0.49 and 0.59); correlations were strongest for the simplified World Health Organization and the Harris and Benedict equations. However, the equations systematically overestimated REE by more than 20%, especially in men. We calculated a correction factor for the World Health Organization scale: −340.3 kcal/d for women and −618.8 kcal/d for men. ConclusionAnalysis of REE is important for polio survivors; The use of estimation equations could lead to the prescription of a nonadapted diet. We determined a correction factor that should be validated in prospective studies.

External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study.

We evaluated the validity of sixteen predictive energy expenditure equations for resting energy expenditure estimation (eREE) against measured resting energy expenditure using indirect calorimetry (REEIC) in 153 critically ill children. Predictive equations were included based on weight, height, sex, and age. The agreement between eREE and REEIC was analyzed using the Bland–Altman method. Precision was defined by the 95% limits of the agreement; differences > ±10% from REEIC were considered clinically unacceptable. The reliability was assessed by the intraclass correlation coefficient (Cronbach’s alpha). The influence of anthropometric, nutritional, and clinical variables on REEIC was also assessed. Thirty (19.6%) of the 153 enrolled patients were malnourished (19.6%), and fifty-four were overweight (10.5%) or obese (24.8%). All patients received sedation and analgesia. Mortality was 3.9%. The calculated eREE either underestimated (median 606, IQR 512; 784 kcal/day) or overestimated (1126.6, 929; 1340 kcal/day) REEIC compared with indirect calorimetry (928.3, 651; 1239 kcal/day). These differences resulted in significant biases of −342 to 592 kcal (95% limits of agreement (precision)−1107 to 1380 kcal/day) and high coefficients of variation (up to 1242%). Although predicted equations exhibited moderate reliability, the clinically acceptable ±10% accuracy rate ranged from only 6.5% to a maximum of 24.2%, with the inaccuracy varying from −31% to +71.5% of the measured patient’s energy needs. REEIC (p = 0.017) and eREE (p < 0.001) were higher in the underweight compared to overweight and obese patients. Apart from a younger age, malnutrition, clinical characteristics, temperature, vasoactive drugs, neuromuscular blockade, and energy intake did not affect REEIC and thereby predictive equations’ accuracy. Commonly used predictive equations for calculating energy needs are inaccurate for individual patients, either underestimating or overestimating REEIC compared with indirect calorimetry. Altogether these findings underscore the urgency for measuring REEIC in clinical situations where accurate knowledge of energy needs is vital.

Low prenatal resting energy expenditure and high energy intake predict high gestational weight gain in pregnant women with overweight/obesity

IntroductionRecent evidence suggests that low resting energy expenditure (REE) is associated with gestational weight gain (GWG). However, little research has examined whether REE explains GWG beyond the contributions of energy intake (EI) and physical activity (PA). This study examined the extent to which EI, PA, and REE were associated with and explained second trimester GWG in pregnant women with overweight/obesity. MethodsPregnant women with overweight/obesity (N = 26) participating in the Healthy Mom Zone study, a theoretically-based behavioral intervention that adapted the intervention dosage over time to regulate GWG completed weekly point estimates of EI (back-calculation), PA (wrist-worn activity monitor), and REE (mobile metabolism device) from 14- to 28-weeks gestation. Second trimester GWG was calculated as the weekly point estimate of weight from a Wi-Fi weight scale at gestational week 28 minus the weekly point estimate of weight at gestational week 14. ResultsPartial correlations revealed second trimester EI and PA were not significantly associated with second trimester GWG, but low second trimester REE was significantly associated with high second trimester GWG. Hierarchical regression analyses showed the model of fat-free mass, EI, PA, and REE explained 56% of the variance in second trimester GWG. Low REE was the strongest determinant followed by high EI; fat-free mass and PA were not significant predictors. ConclusionsWhile EI and PA remain important determinants of GWG, future researchers should explore the role of REE to inform individualized EI and PA goals to better regulate GWG.

Resting energy expenditure of a diverse group of South African men and women.

In South Africa, overweight/obesity is a public health concern, disproportionally affecting Black females. A contributory role of a lower resting energy expenditure (REE) is suggested for African Americans. The present study assessed the REE of Black and White South African adults aiming to better understand the underlying predictors to overweight/obesity and transform this into locally appropriate recommendations. In 328 (63% female; 39% Black) healthy South African adults, REE was measured with indirect calorimetry and body composition with multifrequency bioelectrical impedance analysis. The REE was estimated with 30 sets of published equations. Black-White differences in REE, as measured and adjusted (analysis of covariance), were determined with quantile regression. Reliability/agreement of estimated (against measured) REE was determined with intra-class correlations (ICCs) and Bland-Altman analysis. A new equation was developed by median regression followed by preliminary validation. Measured REE (adjusted for age along with fat-free mass [FFM], FFM index, FFM plus fat mass, FFM index plus fat mass index) in White subjects was significantly higher (p < 0.001) than in Black subjects for men and women alike, regardless of obesity class. None of the sets of estimation equations had good agreement with measured REE for Black, White, male and female subjects simultaneously. A new estimation equation, based on whole-body variables, had good reliability (ICC = 0.79) and agreement (mean difference: 27 kJ) and presents practical opportunities for groups at the local grass-roots level. The REE in Black South African adults is lower than in White adults. Tailored REE equations may improve REE estimation of racially/ethnically diverse South African groups and contribute to improved obesity management.

Predicted estimates of resting energy expenditure have limited clinical utility in patients with cirrhosis

Malnutrition is associated with adverse clinical outcomes in patients with cirrhosis. Accurate assessment of energy requirements is needed to optimize dietary intake. Resting energy expenditure (REE), the major component of total energy expenditure, can be measured using indirect calorimetry (mREE) or estimated using prediction equations (pREE). This study assessed the usefulness of predicted estimates of REE in this patient population. Individual mREE data were available for 900 patients with cirrhosis (mean [±1 SD] age 55.7±11.6 years-old; 70% men; 52% south-east Asian) and 282 healthy controls (mean age 36.0±12.8 years-old; 52% men; 18% south-east Asian). Metabolic status was classified using thresholds based on the mean ± 1 SD of the mREE in the healthy controls. Comparisons were made between mREE and pREE estimates obtained using the Harris-Benedict, Mifflin, Schofield and Henry equations. Stepwise regression was used to build 3 new prediction models which included sex, ethnicity, body composition measures, and model for end-stage liver disease scores. The mean mREE was significantly higher in patients than controls when referenced to dry body weight (22.4±3.8 cf. 20.8±2.6 kcal/kg/24 hr; p <0.001); there were no significant sex differences. The mean mREE was significantly higher in Caucasian than Asian patients (23.1±4.4 cf. 21.7±2.9 kcal/kg/24 hr; p <0.001). Overall, 37.1% of Caucasian and 25.3% of Asian patients were classified as hypermetabolic. The differences between mREE and pREE were both statistically and clinically relevant; in the total patient population, pREE estimates ranged from 501 kcal/24 hr less to 548 kcal/24 hr more than the mREE. Newly derived prediction equations provided better estimates of mREE but still had limited clinical utility. Prediction equations do not provide useful estimates of REE in patients with cirrhosis. REE should be directly measured. People with cirrhosis are often malnourished and this has a detrimental effect on outcome. Provision of an adequate diet is very important and is best achieved by measuring daily energy requirements and adjusting dietary intake accordingly. Prediction equations, which use information on age, sex, weight, and height can be used to estimate energy requirements; however, the results they provide are not accurate enough for clinical use, particularly as they vary according to sex and ethnicity.

A Comparison between Continuous Indirect Calorimetry and Single Weight-Based Formula in Estimating Resting Energy Expenditure in Nutritional Therapy: A Prospective Randomized Controlled Study in Critically Ill Patients

Optimal nutritional therapy is important to improve outcome in critically ill population in an intensive care unit (ICU). Although indirect calorimetry (IC) is currently a gold standard for resting energy expenditure (REE) measurement, yet it is still not routinely used in the ICU. A total of 146 mechanically ventilated patients were randomised to receive enteral nutrition (EN) with energy targeted based on continuous indirect calorimetry (IC) measurements (IC group, n=73) or according to 25 kcal/kg/day (SWB group, n=73). Patient characteristics were equally distributed and the IC group showed lower mean measured REE (1668.1 + 231.7 vs 1512.0 + 177.1 kcal, p&lt;0.001). Results also showed a significant deficiency in the daily (-148.8 + 105.1 vs. -4.99 + 44.0 kcal, p&lt;0.001) and total cumulative energy balances (-1165.3 + 958.1 vs. 46.5 + 369.5 kcal, p&lt;0.001) in the SWB group as compared to the IC group. From the Kaplan-Meier survival analysis, we found that ICU mortality was significantly lower in the IC group with better survival probability compared to the SWB group (log-rank test, p = 0.03). However, both groups showed comparable results in terms of ICU length of stay, duration of mechanical ventilation and incidence of feeding intolerance. In conclusion, this study showed that tightly supervised nutritional therapy based on continuous IC measurement provides significantly less mean daily and cumulative energy deficits as well as significantly reduced ICU mortality rate.

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

BackgroundPredictive 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.MethodsUsing 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.ResultsMean 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].ConclusionsSeparate 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 registrationClinicalTrials.gov NCT03319329.

Development of a Resting Energy Expenditure Estimation in Patients Undergoing Targeted Temperature Management with a Surface Gel Pad Temperature Modulating Device.

Targeted temperature management (TTM) directly impacts energy expenditure via temperature modulation and shivering associated with temperature modulating devices. We hypothesized that resting energy expenditure (REE) can be accurately estimated utilizing data obtained from a surface gel pad temperature modulating device (TMD) and demographic factors. Baseline demographic data, along with concurrent temperature, sedation, and shivering data, and indirect calorimetry (IDC) were collected from patients undergoing TTM. The data from the IDC and temperature modulation device (TMD) were synchronized and averaged over 60-second intervals to provide simultaneous comparisons. Heat transfer (calories) was calculated from the TMD by an equation that assessed water temperature from the TMD to the patient, water temperature returning to the TMD, water flow rates, and device mode. A linear regression model was used to determine factors associated with REE as measured by IDC. A difference in the mean between REE and estimated REE was used to assess accuracy. There were 48 assessments conducted in 40 subjects [mean (standard deviation)] age: 58 (14) years, 60% female, body surface area (BSA): 2.0 ± 0.3 who underwent simultaneous assessments. Target temperature was 36-37°C in 75%, with a median Bedside Shivering Assessment Score of 0 (range 0-2). Factors associated with REE on multivariable linear regression included older age (p < 0.001), male sex (p = 0.004), higher BSA (p < 0.001), higher patient temperature (p < 0.001), and lower heat transfer (p = 0.003). Adjusted prediction coefficients from this model were then tested against REE by a Bland-Altman analysis. The difference between difference in resting energy estimation (REEdiff) and measured REE by IDC was 6.2 calories/min (REEdiff: 95% confidence interval: -14.1 calories, 26.5 calories, p = 0.5). We believe that the heat transfer data from the TMD coupled with clinical characteristics of patients can be utilized to calculate the REE for every minute of TTM. These data can be utilized to mitigate the consequences of shivering and malnutrition during TTM.

Novel Equations to Estimate Resting Energy Expenditure during Sitting and Sleeping

Introduction: Young and early middle-aged office workers spend most of the day sitting or sleeping. Few studies have used a metabolic chamber to report sitting resting energy expenditure (REE) or sleeping metabolic rate (SMR) estimation equations. This study aimed to develop novel equations for estimating sitting REE and SMR, and previously published equations for SMR were compared against measured values. Methods: The relationships among sitting REE, SMR, and body composition measured in clinical trials were analyzed. The body composition (fat-free mass [FFM] and fat mass) and energy metabolism of 85 healthy young and early middle-aged Japanese individuals were measured using dual-energy X-ray absorptiometry and a metabolic chamber, respectively. Novel estimate equations were developed using stepwise multiple regression analysis. Estimates of SMR using a new equation and 2 published equations were compared against measured SMR. Results: The sitting mREE and mSMR were highly correlated (r = 0.756, p < 0.01). The new FFM-based estimate accounted for 50.4% of the variance in measured sitting REE (mREE) and 82.3% of the variance in measured SMR (mSMR). The new body weight-based estimate accounted for 49.3% of the variance in sitting mREE and 82.2% of the variance in mSMR. Compared with mSMR, the SMR estimate using an FFM-based published equation was slightly underestimated. Conclusion: These novel body weight- and FFM-based equations may help estimate sitting REE and SMR in young and early middle-aged adults. Previous SMR estimated FFM-based equations were slightly underestimated against measured SMR; however, we confirmed the previous SMR estimate equations could be useful. This finding suggests that sitting REE and SMR can be easily estimated from individual characteristics and applied in clinical settings.

Low Resting Energy Expenditure Is Associated with High Gestational Weight Gain Only When Resting Energy Expenditure Fluctuates.

Resting energy expenditure (REE) may be useful for individualizing energy intake (EI) and physical activity (PA) goals, and in turn, regulating gestational weight gain (GWG). Limited research, however, has examined the association between REE and GWG. This study examined (1) change in REE from 14 to 28 gestation, (2) time-varying associations between REE and GWG, and (3) EI and PA patterns during the weeks when REE and GWG were significantly associated. Pregnant women with overweight/obesity (N = 27) participating in the Healthy Mom Zone study completed weekly point estimates of EI (back-calculation), PA (wrist-worn activity monitor), REE (mobile metabolism device), and weight (Wi-Fi scale) from 14 to 28 weeks gestation. Analyses included descriptives and time-varying effect modeling. REE fluctuated, increasing on average from 14 to 28 weeks gestation, but decreased at gestational weeks 17, 20, 21, 23, 26, and 28. Most women increased in REE; however there was large between-person variability in the amount of change. Associations between REE and GWG were small but time-varying; low REE was associated with high GWG between gestational weeks 25 to 28 when there was observably larger fluctuation in REE. Moreover, over half of the women were categorized as having excessive EI and most as low active during this time. EI needs may be overestimated and PA needs may be underestimated when REE is fluctuating, which may increase the risk for high second trimester GWG. Researchers should consider the role of REE to inform EI and PA goals to regulate GWG.