Harris-Benedict Research Articles

Preparing for future pandemics: Automated intensive care electronic health record data extraction to accelerate clinical insights

BackgroundManual data abstraction from electronic health records (EHRs) for research on intensive care patients is time-intensive and challenging, especially during high-pressure periods such as pandemics. Automated data extraction is a potential alternative but may raise quality concerns. This study assessed the feasibility and credibility of automated data extraction during the coronavirus disease 2019 (COVID-19) pandemic. MethodsWe retrieved routinely collected data from the COVID-Predict Dutch Data Warehouse, a multicenter database containing the following data on intensive care patients with COVID-19: demographic, medication, laboratory results, and data from monitoring and life support devices. These data were sourced from EHRs using automated data extraction. We used these data to determine indices of wasted ventilation and their prognostic value and compared our findings to a previously published original study that relied on manual data abstraction largely from the same hospitals. ResultsUsing automatically extracted data, we replicated the original study. Among 1515 patients intubated for over 2 days, Harris–Benedict (HB) estimates of dead space fraction increased over time and were higher in non-survivors at each time point: at the start of ventilation (0.70±0.13 vs. 0.67±0.15, P <0.001), day 1 (0.74±0.10 vs. 0.71±0.11, P<0.001), day 2 (0.77±0.09 vs. 0.73±0.11, P<0.001), and day 3 (0.78±0.09 vs. 0.74±0.10, P<0.001). Patients with HB dead space fraction above the median had an increased mortality rate of 13.5%, compared to 10.1% in those with values below the median (P<0.005). Ventilatory ratio showed similar trends, with mortality increasing from 10.8% to 12.9% (P=0.040). Conversely, the end-tidal-to-arterial partial pressure of carbon dioxide (PaCO₂) ratio was inversely related to mortality, with a lower 28-day mortality in the higher than median group (8.5% vs. 15.1%, P<0.001). After adjusting for base risk, impaired ventilation markers showed no significant association with 28-day mortality. ConclusionManual data abstraction from EHRs may be unnecessary for reliable research on intensive care patients, highlighting the feasibility and credibility of automated data extraction as a trustworthy and scalable solution to accelerate clinical insights, especially during future pandemics.

Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris-Benedict Equation with Indirect Calorimetry.

Background: The main objective of the work was the analysis and description of data on body composition and resting energy expenditure (REE) values of selected groups of patients with obesity whose REE measurement results using indirect calorimetry reached a level below 95% of the predicted REE calculated using the Harris-Benedict (H-B) equation. The sub-goals were to describe the dependence of body composition on the size of the REE and to find out if the deviations between the number of the total measured REE and the REE calculated using H-B in the adapted group (patients with altered REE values, lower than expected caused by long caloric restriction) are significant. Methods: For the research, 71 (39 women and 32 men) patients treated in obesitology were selected. Patients underwent the measurement of resting metabolism using indirect calorimetry (IC) and body composition measurement on the bioimpedance device and, at the same time, the value of resting metabolism was calculated for everyone using the H-B equation. The whole group was divided into five groups according to the deviation of the measurement using IC and the calculation of the H-B equation. Results: In the total set of examined individuals, there were 32.4% with a reduced REE value compared to the REE calculation according to the H-B equation, which corresponds to 23 individuals. In the adapted group, the average measured REE was 2242 ± 616 kcal compared to the H-B calculation of 2638 ± 713 kcal. Statistically, these results were not significant, but a high case-to-case variation was found. The highest deviation from the H-B predictive calculation was -42% and +43% in the whole research group. The amount of muscle tissue in the adapted group averaged 44.3 ± 11.9 kg and the amount of fat-free mass (FFM) 77.9 ± 20.1 kg. When statistically testing the dependence of REE on FFM and muscle tissue in the adapted group, a strong correlation was found. Conclusions: The H-B equation alone is not suitable for setting a suitable diet therapy for an individual with obesity. In order to select and characterize a group of adapted individuals, it will be necessary to use other methods or a larger research sample, and preferably examine and divide patients with specific comorbidities or include their health status.

Determination of energy requirements after minor burns using indirect calorimetry: A descriptive cohort study.

Minor burns could be associated with moderate hypermetabolism. In this study, the primary outcome was measured energy expenditure (mEE) determined by indirect calorimetry in patients with minor burns. We also compared mEE with predictive values and actual energy intakes. Adults with minor burns exclusively treated on an outpatient basis were included. During the week following injury, a dietitian performed indirect calorimetry (Q-NRG in canopy mode), calculated the estimated energy expenditure (eEE) based on the Harris-Benedict (HB) and Henry formulas, and evaluated daily energy intakes using a food anamnesis. Forty-nine patients (59.2% male; median age: 35 [interquartile range: 29-46.5] years; body mass index [BMI]: 26.2 [22.3-29.6] kg/m2; burn surface area [BSA]: 1.5% [1%-2%]) were included 4 (2-6) days after injury. The mEE was 1863 (1568-2199) kcal or 25 (22.4-28.5) kcal/kg and 1838 (1686-2026) kcal or 26.1 (23.7-27.7) kcal/kg in patients who were respectively fasting for >10 h or not (P = 0.991 or P = 0.805). The total mEE was 104% (95%-116%) and 108% (99%-122%) of the total eEE using the HB and Henry formulas, respectively, with diet-induced thermogenesis and physical activity level. Hypermetabolism (ie, oxygen consumption at rest ≥3.5 ml/kg/min) was observed in 21/49 (42.9%) patients. Energy intakes corresponded to 71% (60%-86%) of the total mEE. Performing indirect calorimetry in adults with minor burns revealed that ≥40% of the tested adults presented a hypermetabolism and that their mEE was not covered by their energy intakes.

Predictive Equation for Basal Metabolic Rate in Normal-Weight Chinese Adults.

This study aimed to develop a predictive equation for basal metabolic rate (BMR) in normal-weight Chinese adults and provide a reference for establishing the national recommended dietary energy intake. A new equation for BMR was derived from a sample of 516 normal-weight Chinese adults (men = 253, women = 263), and this sample was collected from two previous studies. Furthermore, the accuracy of this new equation and eight other previous predictive equations was reviewed. The agreement and reliability were compared in terms of bias, accuracy, the intraclass correlation coefficient, and Bland-Altman plots between predictive equations. In addition, the newly developed equation was further verified using a small independent sample, which contained 41 healthy Chinese adults (men = 21, women = 20). The measured BMR (mBMR) of all participants, measured using indirect calorimetry, was 1346.2 ± 358.0 kcal/d. Thirty participants were excluded based on Cook's distance criteria (Cook's distance of ≥0.008). Previous equations developed by Henry, Schofield, Harris-Benedict (H-B), Yang, and Hong overestimated the BMR of healthy Chinese adults. The present equation displayed the smallest average bias (0.2 kcal/d) between the mBMR and predicted basal metabolic rate (pBMR). The limits of agreement of the present equation from Bland-Altman plots were -514.3 kcal/d and 513.9 kcal/d, which is the most narrow and balanced limit of agreement. Moreover, in the verification of the testing database, the pBMR of the new equation was not significantly different from the mBMR, and the accuracy was 75.6%. Compared with pre-existing equations, the present equation is more applicable to the prediction of BMR in healthy Chinese adults. However, further studies are required to verify the accuracy of this new equation.

A critical view of the use of predictive energy equations for the identification of hypermetabolism in motor neuron disease: A pilot study

People living with motor neuron disease (MND) frequently struggle to consume an optimal caloric intake. Often compounded by hypermetabolism, this can lead to dysregulated energy homeostasis, prompting the onset of malnutrition and associated weight loss. This is associated with a poorer prognosis and reduced survival. It is therefore important to establish appropriate nutritional goals to ensure adequate energy intake. This is best done by measuring resting energy expenditure (mREE) using indirect calorimetry. However, indirect calorimetry is not widely available in clinical practice, thus dietitians caring for people living with MND frequently use energy equations to predict resting energy expenditure (pREE) and estimate caloric requirements. Energy prediction equations have previously been shown to underestimate resting energy expenditure in over two-thirds of people living with MND. Hypermetabolism has previously been identified using the metabolic index. The metabolic index is a ratio of mREE to pREE, whereby an increase of mREE by≥110% indicates hypermetabolism. We aim to critically reflect on the use of the Harris-Benedict (1919) and Henry (2005) energy prediction equations to inform a metabolic index to indicate hypermetabolism in people living with MND. mREE was derived using VO₂ and VCO₂ measurements from a GEMNutrition indirect calorimeter. pREE was estimated by Harris-Benedict (HB) (1919), Henry (2005) and kcal/kg/day predictive energy equations. The REE variation, described as the percentage difference between mREE and pREE, determined the accuracy of pREE ([pREE-mREE]/mREE) x 100), with accuracy defined as≤± 10%. A metabolic index threshold of ≥110% was used to classify hypermetabolism. All resting energy expenditure data are presented as kcal/24hr. Sixteen people living with MND were included in the analysis. The mean mREE was 1642 kcal/24hr ranging between 1110 and 2015 kcal/24hr. When REE variation was analysed for the entire cohort, the HB, Henry and kcal/kg/day equations all overestimated REE, but remained within the accuracy threshold (mean values were 2.81% for HB, 4.51% for Henry and 8.00% for kcal/kg/day). Conversely, inter-individual REE variation within the cohort revealed HB and Henry equations both inaccurately reflected mREE for 68.7% of participants, with kcal/kg/day inaccurately reflecting 41.7% of participants. Whilst the overall cohort was not classified as hypermetabolic (mean values were 101.04% for HB, 98.62% for Henry and 95.64% for kcal/kg/day), the metabolic index ranges within the cohort were 70.75%-141.58% for HB, 72.82%-127.69% for Henry and 66.09%-131.58% for kcal/kg/day, indicating both over- and under-estimation of REE by these equations. We have shown that pREE correlates with body weight (kg), whereby the lighter the individual, the greater the underprediction of REE. When applied to the metabolic index, this underprediction biases towards the classification of hypermetabolism in lighter individuals. Whilst predicting resting energy expenditure using the HB, Henry or kcal/kg/day equations accurately reflects derived mREE at group level, these equations are not suitable for informing resting energy expenditure and classification of hypermetabolism when applied to individuals in clinical practice.

Measured Energy Expenditure Using Indirect Calorimetry in Post-Intensive Care Unit Hospitalized Survivors: A Comparison with Predictive Equations.

Actual energy needs after a stay in intensive care units (ICUs) are unknown. The aims of this observational study were to measure the energy expenditure (mEE) of ICU survivors during their post-ICU hospitalization period, and to compare this to the estimations of predictive equations (eEE). Survivors of an ICU stay ≥ 7 days were enrolled in the general ward during the first 7 days after ICU discharge. EE was measured using the Q-NRG calorimeter in canopy mode. This measure was compared to the estimated EE using the Harris–Benedict (HB) equation multiplied by a 1.3 stress factor, the Penn–State (PS) equation or the 30 kcal weight-based (WB) equation. A total of 55 adults were included (67.3% male, age 60 (52–67) y, body mass index 26.1 (22.2–29.7) kg/m2). Indirect calorimetry was performed 4 (3–6) d after an ICU stay of 12 (7–16) d. The mEE was 1682 (1328–1975) kcal/d, corresponding to 22.9 (19.1–24.2) kcal/kg/day. The eEE values derived using HB and WB equations were significantly higher than mEE: 3048 (1805–3332) and 2220 (1890–2640) kcal/d, respectively (both p < 0.001). There was no significant difference between mEE and eEE using the PS equation: 1589 (1443–1809) kcal/d (p = 0.145). The PS equation tended to underestimate mEE with a bias of −61.88 kcal and a wide 95% limit of agreement (−717.8 to 594 kcal). Using the PS equation, agreement within 15% of the mEE was found in 32/55 (58.2%) of the patients. In the present cohort of patients who survived a prolonged ICU stay, mEE was around 22–23 kcal/kg/day. In this post-ICU hospitalization context, none of the tested equations were accurate in predicting the EE measured by indirect calorimetry.

Energy expenditure and intake in COPD: The extent of unnoticed unbalance by predicting REE

Undernourishment is promoted by an unbalance between energy expenditure and intake. Resting energy expenditure (REE) in chronic obstructive pulmonary disease (COPD) is commonly predicted using the Harris-Benedict (HB) and the Angelillo-Moore (AM) formulas, however no study has investigated to which extent COPD patients with an energy unbalance go unnoticed when REE is predicted rather than measured with indirect calorimetry. This study demonstrates that 66% and 25% of negatively unbalanced patients go unnoticed when using HB and AM, respectively, urging to discourage the use of REE predicting formulas in clinical practice, at least in cases at risk of undernourishment.

A251 USE OF INDIRECT CALORIMETRY TESTING TO DIRECT NUTRITION SUPPORT IN CRITICALLY ILL PATIENTS WITH GASTROINTESTINAL CONDITIONS

BackgroundIndirect calorimetry (IC), which measures oxygen uptake and carbon dioxide output, determines energy expenditure (EE) more precisely than predictive equations in critically ill patients. It is unknown whether the use of IC affects energy provision in critically ill patients with gastrointestinal (GI) conditions that affect absorption and digestionAimsTo (1) compare IC and predictive equations for determining energy needs and (2) evaluate whether IC results affect changes in nutrition support in critically ill patients with GI conditions.MethodsIn a prospective, observational study, IC was performed for 25 to 55 mins in critically ill patients admitted to intensive care or clinical wards at 2 tertiary-care hospitals in Hamilton, Ontario between Feb 2018 to Sept 2021. EE measured by IC was compared to EE determined by a predictive equation (25 kcal/kg) or the Harris-Benedict (HB) formula. A change in energy provision was defined as a change of >10% directed by IC. Continuous data are expressed as means and standard deviation (SD), and categorical data as a proportion of patients. The Mann Whitney U Test (SPSSv26) was used to compare GI and non-GI populations.ResultsOf 296 IC tests in 229 patients, 39 of them were in 30 GI patients (11 female; mean age 62 yrs; SD 19). Admission GI diagnoses were pancreatitis (33%), liver disease (20%), Crohn’s disease/ autoimmune enteropathy (20%), post-bowel resection (10%), chronic abdominal pain (10%), and cholangitis (7%). The predictive formula underestimated EE in 67% of GI patients (mean deficit 503 kcal/day) compared to IC, corresponding to a mean deficit of 25% of patients’ energy needs. The HB formula underestimated EE in 73% of patients (mean deficit 652 kcal/day), a mean deficit of 28% of patients’ energy needs compared to IC. Pancreatitis was the majority diagnosis (75% of the predictive equation; 50% HB) among patients with the highest deficit (>30%) in energy needs when compared to IC. There were no significant differences in the rates of underestimation of energy needs based on predictive and HB formulas between the GI and non-GI patients or between luminal GI and non-luminal GI conditions. After IC, 63% of tests led to changes in energy provisions in GI patients; most requiring an increase in energy provisions (53%).ConclusionsThe use of IC to accurately measure EE led to changes in energy provisions in critically ill GI patients. Preventing over- and underfeeding with the implementation of IC to guide nutrition has the potential to improve outcomes in critically ill patients with gastrointestinal conditions.Funding AgenciesNone

Caloric adequacy of parenteral nutrition and its influence on the clinical outcome of hospitalised patients.

Background: The adaptation of Parenteral Nutrition (PN) to actual energy requirements of hospitalised patients is essential, since excessive and insufficient nutritional intake have been associated with poor clinical outcomes. Aim: To evaluate the adaptation of prescribed PN to the estimated nutritional requirements using three predictive equations and the influence of excessive/insufficient nutrient intake on patient clinical outcomes (nutritional parameters, metabolic and infectious complications). Methods: Prospective, observational study in hospitalised patients nutritionally assessed. Data was collected the first and fifth/sixth day of PN with clinical (infection, length of hospital stay), biochemical (visceral proteins, cholesterol, glucose, triglycerides, lymphocytes, CRP) and anthropometric parameters (skin folds, height, weight). Theoretical requirements were calculated using Harris-Benedict (HB), Mifflin-St Jeor (MF) and 25 Kcal/Kg/day formulas. The HB formula was used to compare estimated and provided requirements. Results: A total of 94 patients (mean: 72 ± 13.7 years old) were included with initial mean weight and height of 69.2 Kg and 162.8 cm, respectively (mean BMI: 26.1 Kg/m2). No statistically significant differences were found between the actual (1620 Kcal/day) and estimated caloric mean calculated with HB (1643 Kcal/day) and MF (1628 Kcal/day). When comparing with the caloric estimation, 31.9% of patients were underfed, while 14.9% were overfed. Intergroup analysis demonstrated significant variations in albumin, prealbumin, glucose, cholesterol, triglycerides and MUAC, with a significant increase of hyperglycaemia (+37.86; p < 0.05) and hypertriglyceridemia (+63.10; p < 0.05), being higher in overfed patients. Conclusion: In our study, inadequate nutrient intake was associated with a higher degree of hyperglycaemia and hypertriglyceridemia, without positive impact on anthropometric parameters.

IMPELEMENTASI METODE HARRIS BENEDICT PADA SISTEM INFORMASI PENGHITUNGAN GIZI REMAJA BERBASIS WEBSITE

The teenage period is the peak stage of the growth period of a person's weight and height. This growth process requires sufficient nutritional support. Teenagers who have adequate nutritional intake will have a healthy body condition, rarely experience pain so that activities at home and at school will run smoothly. Lack of adolescent knowledge to know the nutritional needs in the body, can result in inhibition of the growth process. There needs to be a nutrition education for teenagers. Adolescents need knowledge about the calculation of nutritional adequacy. Utilization of information technology is widely used as a tool of convenience and aids in daily activities. With the above problems, an information system is needed in providing knowledge to adolescents about nutritional needs in their bodies. Information systems are built with PHP and Mysql and counting nutritional needs using harris benedict methods. The Harris Benedict method is a way to count the number of calories a person needs. The programming languages used for the creation of this application are PHP and MySQL for databases. Based on the results of the study, this application can help parents to find out the nutritional needs of their children who are teenagers online.

Resting Energy Expenditure Early after Cardiac Surgery and Validity of Predictive Equations: A Prospective Observational Study

Objective: Several predictive equations have been used to estimate patients’ energy expenditure. The study aimed to describe the characteristics of resting energy expenditure (REE) in patients undergoing mechanical ventilation during early postoperative stage after cardiac surgery and evaluate the validity of 9 REE predictive equations. Methods: This was a prospective observational study. Patients aged 18–80 years old, undergone open-heart surgery, were enrolled between January 2017 and 2018. The measured REE (mREE) was evaluated via indirect calorimetry (IC). The predictive resting energy expenditure (pREE) was suggested by 9 predictive equations, including Harris-Benedict (HB), HB coefficient method, Ireton-Jones, Owen, Mifflin, Liu, 25 × body weight (BW), 30 × BW, and 35 × BW. The association between mREE and pREE was assessed by Pearson’s correlation, paired t test, Bland-Altman method, and the limits of agreement (LOA). Results: mREE was related to gender, BMI, age, and body temperature. mREE was significantly correlated with pREE, as calculated by 9 equations (all p < 0.05). There was no significant difference between pREE and mREE, as calculated by 30 × BW kcal/kg/day (t = 0.782, p = 0.435), while significant differences were noted between mREE and pREE calculated by other equations (all p < 0.05). Taking the 30 × BW equation as a suitable candidate, most of the data points were within LOA, and the percentage was 95.6% (129/135). Considering the rationality of clinical use, accurate predictions (%) were calculated, and only 40.74% was acceptable. Conclusions: The 30 × BW equation is relatively acceptable for estimating REE in 9 predictive equations in the early stage after heart surgery. However, the IC method should be the first choice if it is feasible.

Prolonged progressive hypermetabolism during COVID-19 hospitalization undetected by common predictive energy equations.

Background & aimsIndirect calorimetry (IC) is the gold-standard for determining measured resting energy expenditure (mREE) in critical illness. When IC is not available, predicted resting energy expenditure (pREE) equations are commonly utilized, which often inaccurately predict metabolic demands leading to over- or under-feeding. This study aims to longitudinally assess mREE via IC in critically ill patients with SARS-CoV-2 (COVID-19) infection throughout the entirety of, often prolonged, intensive care unit (ICU) stays and compare mREE to commonly utilized pREE equations.MethodsThis single-center prospective cohort study of 38 mechanically ventilated COVID-19 patients from April 1, 2020 to February 1, 2021. The Q-NRG® Metabolic Monitor was used to obtain IC data. The Harris-Benedict (HB), Mifflin St-Jeor (MSJ), Penn State University (PSU), and weight-based equations from the American Society of Parenteral and Enteral Nutrition – Society of Critical Care Medicine (ASPEN-SCCM) Clinical Guidelines were utilized to assess the accuracy of common pREE equations and their ability to predict hypo/hypermetabolism in COVID-19 ICU patients.ResultsThe IC measures collected revealed a relatively normometabolic or minimally hypermetabolic mREE at 21.3 kcal/kg/d or 110% of predicted by the HB equation over the first week of mechanical ventilation (MV). This progressed to significant and uniquely prolonged hypermetabolism over successive weeks to 28.1 kcal/kg/d or 143% of HB predicted by MV week 3, with hypermetabolism persisting to MV week 7. Obese individuals displayed a more truncated response with significantly lower mREE versus non-obese patients in MV week 1 (19.5 ± 1.0 kcal/kg/d vs 25.1 ± 1.8 kcal/kg/d, respectively; p < 0.01), with little change in weeks 2–3 (19.5 ± 1.5 kcal/kg/d vs 28.0 ± 2.0 kcal/kg/d; p < 0.01). Both ASPEN-SCCM upper range and PSU pREE equations provided close approximations of mREE yet, like all pREE equations, occasionally over- and under-predicted energy needs and typically did not predict late hypermetabolism.ConclusionsStudy results show a truly unique metabolic response in COVID-19 ICU patients, characterized by significant and prolonged, progressive hypermetabolism peaking at 3 weeks’ post-intubation, persisting for up to 7 weeks in ICU. This pattern was more clearly demonstrated in non-obese versus obese patients. This response is unique and distinct from any previously described model of ICU stress response in its prolonged hypermetabolic nature. This data reaffirms the need for routine, longitudinal IC measures to provide accurate energy targets in COVID-19 ICU patients. The PSU and ASPEN-SCCM equations appear to yield the most reasonable estimation to IC-derived mREE in COVID-19 ICU patients, yet still often over-/under-predict energy needs. These findings provide a practical guide for caloric prescription in COVID-19 ICU patients in the absence of IC.

Predictive equations for resting metabolic rate are not appropriate to use in Brazilian male adolescent football athletes.

High accuracy in estimating energy expenditure is essential for enhancing sports performance. The resting metabolic rate (RMR), as a primary component of total energy expenditure (TEE), is commonly estimated using predictive equations. However, these references may not be applicable to adolescent athletes. The purpose of this cross-sectional study was to analyse the differences between predicted RMR in relation to energy expenditure measured by indirect calorimetry (IC) among 45 Brazilian male adolescent football athletes. Indirect calorimetry (IC) and anthropometric (bioimpedance) measurements were recorded at a single visit to the laboratory after fasting overnight. The mean age was 15.6 ± 1.14 years, body mass was 63.05 ± 7.8 kg, and height was 172 ± 7.5 cm. The RMR values predicted by equations proposed by the Food and Agriculture Organization (FAO) (United Nations), Henry and Rees (HR), Harris Benedict (HB), and Cunningham (CUN) were compared with IC RMR values, by correlation analysis. The FAO and HR predictive equations yielded different values from IC (IC: 1716.26 ± 202.58, HR: 1864.87 ± 147.78, FAO: 1854.28 ± 130.19, p = 0.001). A moderate correlation of 0.504 was found between the results of HB and IC. In the survival-agreement model, the CUN equation showed low disagreement with the IC RMR, with error values between 200 and 300 kcal/day. The results showed that HB and CUN yielded similar values as IC, with the CUN equation showing low disagreement with IC; hence, adolescent athletes should undergo evaluation with precise laboratory methods to ensure that accurate information about RMR is recorded.

Comparison of Equations to Predict Energy Requirements With Indirect Calorimetry in Hospitalized Patients.

The accuracy of methods to determine resting energy expenditure (REE) contributes toward the adequate provision of nutrition support to hospitalized patients. Indirect calorimetry (IC) is considered the gold-standard method to determine REE. The aim of this study is to evaluate the degree of agreement between the REE measured by IC (REE-IC) and REE estimated by predictive equations in intensive care unit patients. The sample is made up of intensive care unit patients aged >18 years, both male and female, undergoing nutrition therapy. The predictive equations to estimate REE were the Harris Benedict (HB), Ireton Jones (IJ), and practical method (PM). Degree of agreement between REE-predictive equations and REE-IC was analyzed by the interclass correlation coefficient (ICC) and the Bland-Altman test. Average energy obtained by IC was significantly different from HB and IJ equations (P < .001). The HB equation significantly underestimated the REE-IC for body mass index (BMI) classification. Significant concordance was observed between the REE-IC and all estimate equations (P < .05). The IJ equation showed the greatest degree of concordance for BMI classification of underweight (ICC = 0.674; P = .011) and presented the least difference between the averages of the energy when compared with REE-IC (107.8 kcal/d; P < .05). The IJ equation showed better results with IC, with the greatest degree of concordance for BMI classification of underweight. Further research should develop others equations and validate tools to measure energy expenditure for accurate dietary recommendations for hospitalized patients undergoing nutrition therapy.

Age-related decline of resting energy expenditure in humans with similar body weight: First results of the indirect-calorimetry (IC-) Basarot project

Rationale: It is well established that resting energy expenditure (REE) decreases with age. Data derived from indirect calorimetry (IC) are still limited with respect to the number of high aged individuals, BMI groups and health conditions. Therefore, IC generated REE of the BASAROT sample and those calculated according to the Harris-Benedict (HB) equation were used to re-evaluate the proposed association between REE and age.

Basal Energy Expenditure of Chinese Healthy Adults: Comparison of Measured and Predicted Values

ObjectiveThis study aimed to measure the basal energy expenditure (BEE) of Chinese healthy adults and establish an accurate predictive equation for this population. MethodsIn total, 470 Chinese healthy adults had their BEE measured using the Cosmed K4b2 portable metabolic system. Multiple linear regression analysis was applied to develop new optimal equations for predicting BEE. The bias, accuracy rate, concordance correlation coefficient (CCC), and root mean square error (RMSE) were used to evaluate the accuracy of the predictive equations. ResultsThere was a significant difference in BEE between males and females, with 5,954 kJ/d and 5,089 kJ/d, respectively. People living in rural areas expended significantly higher BEE (5,885 kJ/d) than those in urban areas (5,279 kJ/d). Previous equations developed by Henry, Schofield, Harris-Benedict (H-B), and Liu overestimated the BEE of Chinese healthy adults. The new equations derived from the present study displayed the smallest average bias and RMSE from the measured basal energy expenditure (mBEE). The CCC of the new equations was higher than other predictive equations, but it was lower than 0.8. There was no significant difference in the accuracy rate among all predictive equations. ConclusionsSex and regional differences in BEE were observed in Chinese healthy adults. Neither the widely used previous predictive equations nor the one derived in the present study were accurate enough for estimating the BEE of Chinese healthy adults. Further study is required to develop more accurate equations for predicting the BEE of Chinese healthy adults aged between 20–45 years.

Energy Expenditure in Critically Ill Adult Patients With Acute Brain Injury: Indirect Calorimetry vs. Predictive Equations.

Introduction: Predictive equations (PE) are used in lieu of indirect calorimetry (IC) due to cost and limited resources; however, these equations may not be as accurate as IC in estimating resting energy expenditure (REE) in critically ill patients, putting them at risk of malnutrition. The purpose of this study is to compare predicted and measured energy expenditure (MEE) in critically ill adults with acute brain injury.Materials and Methods: This was a retrospective review of adult patients admitted to the Neurosciences ICU with acute brain injury between May 1st, 2014 and April 1st, 2016 who had IC performed. The Harris Benedict (HBE), Penn State University, and Mifflin St Jeor (MSJ) PE were used in comparison to IC results. Subgroup analyses stratified patients based on BMI and type of acute brain injury.Results: One hundred and forty-four patients met inclusion criteria. Comparing predicted and MEE found no significant difference (p = 0.1). High degrees of interpatient variability were discovered, with standard deviations ranging from 17 to 29% of each PE. Pearson's correlations indicated weak associations when HBE, Penn State, and MSJ were individually compared to MEE (r = 0.372, 0.409, and 0.372, respectively). A significant difference was found between predicted and MEE in patients with a BMI < 30 kg/m2 (p < 0.01) and in those with aneurysmal subarachnoid hemorrhage (p < 0.01).Discussion: Due to interpatient variability that exists among REE of critically ill patients with acute brain injury, IC should be used when feasible.

Changing laws of rest energy expenditure in critically ill patients and the intervention effect for nutritional support: a prospective study

To investigate the changing laws of rest energy expenditure (REE) in intensive care unit (ICU) patients and the intervention effect for nutritional support. A prospective randomized control trial was conducted. Fifty-eight critically ill patients who were expected to be able to receive sustained enteral and (or) parenteral nutrition for more than 7 days admitted to ICU of the First Affiliated Hospital of Bengbu Medical College from December 2016 to June 2017 were enrolled. The patients were divided into REE group (n = 29) and HBREE group (n = 29) according to the random number table. On the 1st to 7th day after ICU admission, the indirect calorimetry and the Harris-Benedict (HB) formula were used to obtain the REE and HBREE values, and nutritional support was given according to REE and HBREE values respectively. The data of hemoglobin (Hb), albumin (Alb), prealbumin (PA), C-reactive protein (CRP), oxygenation index (OI) on 1st, 3rd, 5th, 7th and discharged day, and insulin dosage, vasopressor time, mechanical ventilation time, the length of ICU stay, and 28-day mortality were collected. (1) At the beginning, the REE level was high, and then decreased gradually with the extension of hospitalization, and the decline was obvious on the 2nd to 3rd day (kJ/d: 7 088.38±559.41, 6 751.34±558.72 vs. 7 553.44±645.55, both P < 0.05), and was stable from the 5th day, the changing laws showed high at first, then the low, the first rapid decline, then the slow decline, and then reached the steady, there was a 2-day plateau in the middle. During the first 2 days, the REE value was significantly higher than the HBREE value (kJ/d: 7 553.44±645.55 vs. 6 759.21±668.14, 7 088.38±559.41 vs. 6 759.21±668.14, both P < 0.01); on the 3rd, 4th day, the REE value was almost the same as the HBREE value (kJ/d: 6 751.34±558.72 vs. 6 759.21±668.14, 6 568.03±760.19 vs. 6 759.21±668.14, both P > 0.05). After that, the REE value was significantly lower than the HBREE value (kJ/d: 6 089.55±560.70 vs. 6 759.21±668.14, 5 992.55±501.82 vs. 6 759.21±668.14, 5 860.84±577.59 vs. 6 759.21±668.14, all P < 0.01). (2) After the initiation of nutritional support, Hb in the REE group (the first 3 days) and HBREE group (the first 7 days) all increased slowly in the early stage. It increased obviously on the 5th day in the REE group. Compared with the REE group, Hb increased more slowly in the HBREE group, however, there was no difference between the two groups at the time of discharge (g/L: 113.75±17.28 vs. 110.86±15.35, P > 0.05). PA and OI all enhanced significantly on the 3rd day since the nutritional support was initiated, but the daily increase of the REE group was significantly higher than that of the HBREE group [3rd day, PA (mg/L): 110.38±27.65 vs. 96.28±18.06, OI (mmHg, 1 mmHg = 0.133 kPa): 259.29±49.36 vs. 231.74±28.02, both P < 0.05]. The Alb and CRP in the REE group began to improve on the 3rd day, while the index in the HBREE group was delayed on the 5th day, overall, at the time of discharge, the PA, CRP and OI were lower in the HBREE group than in the REE group [PA (mg/L): 252.28±56.94 vs. 295.86±57.26, CRP (mg/L): 73.14±17.63 vs. 56.52±14.91, OI (mmHg): 353.59±70.36 vs. 417.52±71.58, all P < 0.01]. (3) The vasopressor was used in both groups for less than 3 days, but the REE group was shorter (days: 2.26±0.82 vs. 2.95±1.22, P < 0.05), the insulin dosage in the HBREE group was much more than that in the REE group (U: 101.97±21.05 vs. 84.59±22.21, P < 0.01); compared with the REE group, the time of mechanical ventilation and the length of ICU stay in the HBREE group were longer (hours: 113.07±25.96 vs. 93.41±27.25, days: 10.41±3.11 vs. 8.45±2.44, both P < 0.01). There was no significant difference in the 28-day mortality between the REE group and HBREE group (17.24% vs. 24.14%, P > 0.05). Indirect calorimetry can more accurately grasp the changing laws of REE in critically ill patients. Nutritional support with REE value can make relevant nutritional indicators as good as possible, and reduce insulin dosage, shorten vasopressor use time, the length of ICU stay and mechanical ventilation time, but does not change the 28-day mortality.

Comparison of basal metabolic rate in individuals with a spinal cord injury and Harris-Benedict equation: a systematic review

Tables for calculating the energy expenditure of the physical activities of the general population cannot be used due to the different paralysis of the upper or lower limbs in people with spinal cord injury (SCI). The purpose of this review is to compare the differences in the values of basal metabolic rate (BMR), basal energy expenditure (BEE), resting energy expenditure (REE) and resting metabolic rate (RMR) the values evidenced in the literature, observed values vs predicted values using the Harris-Benedict equation. We realized the background research from the time period from 1985 to 2018. We searched in PubMed, Web of Science and Scopus databases for articles addressing the relationship between BMR and people with SCI. We compared the parameters of BMR, BEE, REE and RMR according to Harris-Benedict (HB) equation for persons with SCI. The study confirmed that the energy expenditure of persons with SCI could not be evaluated correctly by the Harris-Benedict equation.

Do we need race-specific resting metabolic rate prediction equations?

BackgroundResting metabolic rate (RMR) is a key determinant of daily caloric needs. Respirometry, a form of indirect calorimetry (IC), is considered one of the most accurate methods to measure RMR in clinical and research settings. It is impractical to measure RMR by IC in routine clinical practice; therefore, several formulas are used to predict RMR. In this study, we sought to determine the accuracy of these formulas in determining RMR and assess additional factors that may determine RMR.MethodsWe measured RMR in 114 subjects (67% female, 30% African American [AA]) using IC. Along with standard anthropometrics, dual-energy X-ray absorptiometry was used to obtain fat-free mass(FFM) and total fat mass. Measured RMR (mRMR) by respirometry was compared with predicted RMR (pRMR) generated by Mifflin–St.Joer, Cunningham, and Harris–Benedict (HB) equations. Linear regression models were used to determine factors affecting mRMR.ResultsMean age, BMI, and mRMR of subjects were 46 ± 16 years (mean ± SD), 35 ± 10 kg/m2, and 1658 ± 391 kcal/day, respectively. After adjusting for age, gender, and anthropometrics, the two largest predictors of mRMR were race (p < 0.0001) and FFM (p < 0.0001). For every kg increase in FFM, RMR increased by 28 kcal/day (p < 0.0001). AA race was associated with 144 kcal/day (p < 0.0001) decrease in mRMR. The impact of race on mRMR was mitigated by adding in truncal FFM to the model. When using only clinically measured variables to predict mRMR, we found race, hip circumference, age, gender, and weight to be significant predictors of mRMR (p < 0.005). Mifflin–St.Joer and HB equations that use just age, gender, height, and weight overestimated kcal expenditure in AA by 138 ± 148 and 242 ± 164 (p < 0.0001), respectively.ConclusionWe found that formulas utilizing height, weight, gender, and age systematically overestimate mRMR and hence predict higher calorie needs among AA. The lower mRMR in AA could be related to truncal fat-free mass representing the activity of metabolically active intraabdominal organs.