Measured versus estimated energy requirement in hospitalized patients.

BACKGROUND & AIM: Failure to identify a patient's energy requirement has a variety of consequences both physiological and economical. Previous studies have shown that predictive formulas, including the Harris Benedict equation (HB), both over- and underestimates energy requirement in severely ill patients and healthy younger adults, compared to the golden standard, indirect calorimetry (IC). The comparison between measured and estimated energy requirements in hospitalized patients in regular wards is underreported. The aim of this study was to assess the agreement between measured energy requirements and requirements estimated by HB in the individual hospitalized patients, and to investigate whether those findings were associated with other specific patient characteristics. METHODS: IC (n = 86) was used to measure resting energy expenditure (REE) and bioimpedance analysis (BIA) (n = 67) was used for body composition in patients admitted to Aalborg University Hospital. Furthermore, height, weight, body mass index, calf circumference, while information regarding hospital ward, vital values, dieticians estimated energy requirements and blood samples were collected in the patients' electronic medical records. Bland-Altman plots, multiple linear regression analysis, and Chi2 tests were performed. RESULTS: On average a difference between IC compared with the HB (6.2%), dietitians' estimation (7.8%) and BIA (4.50%) was observed (p < 0.05). Association between REE and skeletal muscle mass (SMM) (R2 = 0.58, ß = 149.0 kJ), body fat mass (BFM) (R2 = 0.51, ß = 59.1 kJ), and weight (R2 = 0.62, ß = 45.6 kJ) were found (p < 0.05). A positive association between measured REE and HB were found in the following variables (p < 0.05): CRP, age, surgical patients, and respiratory rate. CONCLUSION: This study found a general underestimation of estimated energy expenditure compared to measured REE. A positive correlation between measured REE and SMM, BRM and weight was found. Lastly, the study found a greater association between CRP, age, surgical patients, and respiratory rate and a general greater than ±10% difference between measured and estimation of energy requirements.

Low Energy Intake Diagnosed Using the Harris-Benedict Equation Is Associated with Poor Prognosis in Elderly Heart Failure Patients.

INTRODUCTION: Insufficient nutrient intake is a strong independent predictor of mortality in elderly patients with heart failure. However, it is unclear to what extent energy intake affects their prognosis. This study investigated the association between patient outcomes and actual measured energy intake in elderly patients (≥65 years) with heart failure. METHODS: This study enrolled 139 elderly patients who were hospitalized with worsening heart failure at Shingu Municipal Medical Center, Shingu, Japan, between May 2017 and April 2018. Energy intake was evaluated for three days (from three days prior to the day of discharge until the day of discharge). Based on basal energy expenditure calculated using the Harris-Benedict equation, the patients were classified into a low-energy group (n = 38) and a high-energy group (n = 101). We assessed the prognosis in terms of both all-cause mortality and readmission due to worsening heart failure as a primary outcome. RESULTS: Compared to the patients in the high-energy group, the patients in the low-energy group were predominantly female, less frequently had smoking habits and ischemic heart diseases, and had a higher left ventricular ejection fraction. The low-energy group had higher mortality than the high-energy group (p = 0.028), although the two groups showed equivalent event rates of the primary outcome (p = 0.569). CONCLUSION: Calculations based on the Harris-Benedict equation revealed no significant difference in the primary outcome between the two groups, with a secondary outcome that showed worse mortality in the low-energy group. Given this result, energy requirement-based assessments using the Harris-Benedict equation might help in the management of elderly heart failure patients in terms of improved life outcomes.

Comparison of resting metabolic rate prediction equations in college-aged adults.

Prediction equations have been considered an accurate method for estimating resting metabolic rate (RMR) across multiple populations, but their accuracy for college-aged individuals not on an athletics team remains to be determined. Sixty-two college-aged (18-30 yrs) males (n = 31) and females (n = 31) had their RMR measured (RMRm), using indirect calorimetry, and body composition assessed via air-displacement plethysmography. The World Health Organization (WHO), Mifflin-St Jeor (Mifflin), Harris-Benedict (HB), Cunningham, and Nelson equations were used to estimate RMR. No difference was observed between the Cunningham and RMRm regardless of sex (p ≥ 0.05). All other prediction equations estimated a significantly lower RMR for males (p < 0.05). The Mifflin and Nelson equations predicted an RMR that was significantly lower than RMRm for females (p < 0.05). When compared with RMRm, no difference was detected for females using the WHO, HB, or Cunningham (p ≥ 0.05). Only the Nelson equation predicted an RMR that was outside of the clinically acceptable range (±10% of RMRm) regardless of sex. The Cunningham, WHO, and HB equations can accurately predict RMR for college-aged males and females. RMR prediction equations used in this study are less accurate for those with greater RMRs. Novelty: For adults 18-30 years old that are not on an athletics team, the Cunningham equation can accurately predict RMR. The Nelson equation should not be used to predict RMR for this population. There is a systematic bias for RMR prediction equations to underestimate higher measured RMR values.

Measured and Predicted Resting Energy Expenditure in Malnourished Older Hospitalized Patients: A Cross-Sectional and Longitudinal Comparison.

A number of equations have been proposed to predict resting energy expenditure (REE). The role of nutritional status in the accuracy and validity of the REE predicted in older patients has been paid less attention. We aimed to compare REE measured by indirect calorimetry (IC) and REE predicted by the Harris-Benedict formula in malnourished older hospitalized patients. Twenty-three malnourished older patients (age range 67-93 years, 65% women) participated in this prospective longitudinal observational study. Malnutrition was defined as Mini Nutritional Assessment Long Form (MNA-SF) score of less than 17. REE was measured (REEmeasured) and predicted (REEpredicted) on admission and at discharge. REEpredicted within ±10% of the REEmeasured was considered as accuracy. Nutritional support was provided to all malnourished patients during hospitalization. All patients were malnourished with a median MNA-LF score of 14. REEmeasured and REEpredicted increased significantly during 2-week nutritional therapy (+212.6 kcal and +19.5 kcal, respectively). Mean REEpredicted (1190.4 kcal) was significantly higher than REEmeasured (967.5 kcal) on admission (p < 0.001). This difference disappeared at discharge (p = 0.713). The average REEpredicted exceeded the REEmeasured on admission and at discharge by 29% and 11%, respectively. The magnitude of difference between REEmeasured and REEpredicted increased along with the degree of malnutrition (r = 0.42, p = 0.042) as deviations ranged from -582 to +310 kcal/day in severe to mildly malnourished patients, respectively. REEpredicted by the Harris-Benedict formula is not accurate in malnourished older hospitalized patients. REE measured by IC is considered precise, but it may not represent the true energy requirements to recover from malnutrition. Therefore, the effect of malnutrition on measured REE must be taken into account when estimating energy needs in these patients.

Validity of predictive equations to estimate RMR in females with varying BMI.

Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris-Benedict, Schofield, Henry, Mifflin-St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17-44 kg/m2). Agreement between methods was assessed by Bland-Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin-St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin-St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly (P < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin-St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.

Energy intake during the acute phase and changes in femoral muscle thickness in older hemiplegic inpatients with stroke.

OBJECTIVE: The purpose of this study was to clarify the relationship between changes in lower limb muscle mass and energy intake during the acute phase in older hemiplegic inpatients with stroke. METHODS: A prospective cohort study was performed in 157 consecutive older patients with stroke. Patients were categorized into two groups of energy sufficiency/non-sufficiency based on their daily energy intake during the first week after admission, and compared with regard to change in femur muscle thickness (ΔFMT) between admission and after the four-week period in paralysis/non-paralysis limbs. FMT was determined using B-mode ultrasound imaging with an 8-MHz transducer. RESULTS: The study included 42 men and 54 women (mean age 81 T 6 y). At one week after admission, 57 patients were classified into the energy sufficiency group, and 39 were in the energy shortage group. ΔFMT in each group: -3.7 ± 5.1 mm in the paralysis/sufficiency group, -5.2 ± 5.2 mm in the paralysis/shortage group, -1.5 ± 3.9 mm in the non-paralysis/sufficiency group and -3.9 ± 3.2 mm in the non-paralysis/shortage group. No significant difference was observed in the ΔFMT between the sufficiency group and the non-sufficiency group in the paralysis limb (P = 0.159); a significant difference was observed in the non-paralysis limb (P = 0.002). The multivariate regression analysis showed that energy sufficiency were independently associated with ΔFMT in the non-paralysis limb (unadjusted coefficient = 1.592; 95% confidence interval = 0.072 to 3.112, P = 0.040). CONCLUSIONS: Energy intake could affect ΔFMT on the non-paralysis side in older stroke inpatients.

The Validity of Resting Metabolic Rate-Prediction Equations and Reliability of Measured RMR in Female Athletes.

The aim of the current study was to; 1) assess the test-retest reliability of an indirect calorimetry analyzer (Parvo Medics TrueOne), and 2) compare measured RMR with three RMR-predictive (pRMR) equations in female athletes. In part one, 12 recreationally-exercising women (mean ± SD; age 27.5 ± 12.3 y) performed two RMR assessments, on separate days, utilising the Parvo Medics TrueOne analyser. In part two, 25 recreationally-exercising women to sub-elite athletes (mean ± SD; age 30.1 ± 10.2 y) underwent an RMR assessment using the Parvo Medics TrueOne analyser, which was compared to three calculated pRMR equations (Harris-Benedict (H-B), Mifflin-St Jeor (M), World Health Organisation (WHO)). eTest-retest reliability for the TrueOne analyser was deemed acceptable (CV = 5.3%, ICC = 0.92). The validity of pRMR when compared to measured RMR showed low levels of agreement in all 3 predictive equations (M: CV = 21.4%, TEE = 269 kcal.day-1, r = 0.16, WHO: CV = 21.5%, TEE = 270 kcal.day-1, r = 0.13 H-B: CV = 21.6%, TEE = 270 kcal.day-1, r = 0.13). The Parvo Medics TrueOne analyser is a reliable tool for measuring RMR. Caution should be taken when using pRMR equations in female athletes as they do not take into account the likely differences in fat free mass in these populations.

Are Predictive Energy Expenditure Equations in Ventilated Surgery Patients Accurate?

BACKGROUND:: While indirect calorimetry (IC) is the gold standard used to calculate specific calorie needs in the critically ill, predictive equations are frequently utilized at many institutions for various reasons. Prior studies suggest these equations frequently misjudge actual resting energy expenditure (REE) in medical and mixed intensive care unit (ICU) patients; however, their utility for surgical ICU (SICU) patients has not been fully evaluated. Therefore, the objective of this study was to compare the REE measured by IC with REE calculated using specific calorie goals or predictive equations for nutritional support in ventilated adult SICU patients. MATERIALS AND METHODS:: A retrospective review of prospectively collected data was performed on all adults (n = 419, 18-91 years) mechanically ventilated for >24 hours, with an Fio2 ≤ 60%, who met IC screening criteria. Caloric needs were estimated using Harris-Benedict equations (HBEs), and 20, 25, and 30 kcal/kg/d with actual (ABW), adjusted (ADJ), and ideal body (IBW) weights. The REE was measured using IC. RESULTS:: The estimated REE was considered accurate when within ±10% of the measured REE by IC. The HBE, 20, 25, and 30 kcal/kg/d estimates of REE were found to be inaccurate regardless of age, gender, or weight. The HBE and 20 kcal/kg/d underestimated REE, while 25 and 30 kcal/kg/d overestimated REE. Of the methods studied, those found to most often accurately estimate REE were the HBE using ABW, which was accurate 35% of the time, and 25 kcal/kg/d ADJ, which was accurate 34% of the time. This difference was not statistically significant. CONCLUSION:: Using HBE, 20, 25, or 30 kcal/kg/d to estimate daily caloric requirements in critically ill surgical patients is inaccurate compared to REE measured by IC. In SICU patients with nutrition requirements essential to recovery, IC measurement should be performed to guide clinicians in determining goal caloric requirements.

Resting metabolic rate in muscular physique athletes: validity of existing methods and development of new prediction equations.

Estimation of resting metabolic rate (RMR) is an important step for prescribing an individual's energy intake. The purpose of this study was to evaluate the validity of portable indirect calorimeters and RMR prediction equations in muscular physique athletes. Twenty-seven males (n = 17; body mass index (BMI): 28.8 ± 2.0 kg/m2; body fat: 12.5% ± 2.7%) and females (n = 10; BMI: 22.8 ± 1.6 kg/m2; body fat: 19.2% ± 3.4%) were evaluated. The reference RMR value was obtained from the ParvoMedics TrueOne 2400 indirect calorimeter, and the Cosmed Fitmate and Breezing Metabolism Tracker provided additional RMR estimates. Existing RMR prediction equations based on body weight (BW) or dual-energy X-ray absorptiometry fat-free mass (FFM) were also evaluated. Errors in RMR estimates were assessed using validity statistics, including t tests with Bonferroni correction, linear regression, and calculation of the standard error of the estimate, total error, and 95% limits of agreement. Additionally, new prediction equations based on BW (RMR (kcal/day) = 24.8 × BW (kg) + 10) and FFM (RMR (kcal/day) = 25.9 × FFM (kg) + 284) were developed using stepwise linear regression and evaluated using leave-one-out cross-validation. Nearly all existing BW- and FFM-based prediction equations, as well as the Breezing Tracker, did not exhibit acceptable validity and typically underestimated RMR. The ten Haaf and Weijs (PLoS ONE, 9: e1084602014 (2014)) and Cunningham (1980) (Am. J. Clin. Nutr. 33: 2372-2374 (1980)) FFM-based equations may produce acceptable RMR estimates, although the Cosmed Fitmate and newly developed BW- and FFM-based equations may be most suitable for RMR estimation in male and female physique athletes. Future research should provide additional external cross-validation of the newly developed equations to refine the ability to predict RMR in physique athletes.

Changing laws of rest energy expenditure in critically ill patients and the intervention effect for nutritional support: a prospective study / 中华危重病急救医学

Objective@#To investigate the changing laws of rest energy expenditure (REE) in intensive care unit (ICU) patients and the intervention effect for nutritional support.@*Methods@#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.@*Results@#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. 6759.21±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).@*Conclusions@#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.

Changing laws of rest energy expenditure in critically ill patients and the intervention effect for nutritional support: a prospective study / 中华危重病急救医学

Objective To investigate the changing laws of rest energy expenditure (REE) in intensive care unit (ICU) patients and the intervention effect for nutritional support. Methods 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. Results 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: 7088.38±559.41, 6751.34±558.72 vs. 7553.44±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: 7553.44±645.55 vs. 6759.21±668.14, 7088.38± 559.41 vs. 6759.21±668.14, both P < 0.01); on the 3rd, 4th day, the REE value was almost the same as the HBREE value (kJ/d: 6751.34±558.72 vs. 6759.21±668.14, 6568.03±760.19 vs. 6759.21±668.14, both P > 0.05). After that, the REE value was significantly lower than the HBREE value (kJ/d: 6089.55±560.70 vs. 6759.21±668.14, 5992.55±501.82 vs. 6759.21±668.14, 5860.84±577.59 vs. 6759.21±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). Conclusions 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.

The effect of resting energy expenditure on the prognosis of patients with severe coma determined by the indirect energy metabolism measurement and Harris-Benedict budget formula / 中国中西医结合急救杂志

Objective Under 2 methods of guidance: indirect energy metabolism measurement and classical Harris-Benedict (HB) formula to carry out enteral nutritional support program to observe the difference in influence on nutritional status and prognosis of the patients. Methods Sixty patients with critical coma admitted to the Department of Critical Care Medicine of the 903rd Hospital of PLA from November 2015 to April 2017 were enrolled, and they were divided into a metabolic vehicle group (30 cases) and a formula group (30 cases) according to the random number digital table. All the patients were treated with stage nutritional therapy according to the daily energy consumption, which was measured by indirect energy metabolism measurement or HB formula. The change of serum albulmin (Alb), blood hemoglobin (Hb) and peripheral blood total lymphocyte count (TLC) before treatment and after 1 week and 2 weeks of nutritional support and the incidence of complications and prognosis after 2 weeks of nutritional support were observed; the difference of mid-upper arm circumference (MAC) was compared between before nutritional support. Results After the nutrritional therapy, serum Alb, Hb, and TLC in two groups were obviously higher than those before treatment, the degrees of elevation in metabolic group were more significant than those in the formula group [Alb (g/L): 36.34±4.09 vs. 35.26±3.82; Hb (g/L): 131.6±13.8 vs. 128.8±12.6; TLC (×109/L): 1.63±0.51 vs. 1.50±0.48, all P < 0.05]. The incidence of complications and mortality of the patients in metabolic vehicle group were lower than those of the formula group [40.0% (12/30) vs. 56.7% (17/30), 13.3% (4/30) vs. 16.7% (5/30) respectively, both P < 0.05]. After 2 weeks of nutrition support, the MAC of both groups increased compared with that before treatment, but there was no statistical significant difference in MAC in two groups (all P > 0.05). Conclusion Compared with the HB formula, the metabolic vehicle method can guide the daily energy intake of patients with severe coma more accurately. It is worthy to promote nutrition support program clinically guided by the metabolic vehicle one.

Application value of resting energy monitoring in nutritional support therapy for mechanical ventilation patients / 中华危重病急救医学

Objective To investigate the value of resting energy expenditure (REE) monitoring in nutritional support therapy of critical patients on mechanical ventilation.Methods A prospective randomized controlled trial was conducted.Sixty critical patients [acute physiology and chronic health evaluation Ⅱ score (APACHE Ⅱ) ＞ 15] on ventilation admitted to intensive care unit (ICU) of Dalian Friendship Hospital from September 2016 to October 2018 were enrolled.The enrolled patients were randomly divided into Harris-Benedict formula (HB formula) group and indirect energy measurement (metabolic vehicle) group with 30 patients in each group.The HB formula group was used traditional HB formula to determine the energy supply and ratio of nutritional support therapy,and the metabolic vehicle group was regularly measured the energy supply and proportion of nutritional support therapy.Serum albumin (ALB),total protein (TP),lymphocyte ratio,blood glucose,blood gas analysis parameters and REE value were determined at 3,5,7,9,and 11 days of nutritional support therapy.Results The value of REE at 3 days of nutritional support therapy in metabolic vehicle group was significantly higher than that in HB formula group (kJ/d:7 850.4±947.3 vs.6 915.3±875.7,P ＜ 0.05).With the time of nutritional support treatment prolonged,the REE value of metabolic vehicle group was decreased gradually,and after 7 days,the patient's condition was stable and improved,and the REE value tended to be stable gradually,it was significantly lower than that of HB formula group at 11 days (kJ/d:5 046.3 ± 493.3 vs.6 915.3 ± 875.7,P ＜ 0.05).There was no significant difference in blood gas analysis or plasma protein before nutritional support therapy between the two groups.After 5 days of nutritional support therapy,the respiratory function of critical patients in both groups was improved,and the lymphocyte ratio and plasma protein parameters were alleviated.After 11 days of nutrition support therapy,the respiratory function of critical patients in both groups was further improved,the ventilator model was adjusted to continuous positive airway pressure (CPAP) mode,the lymphocyte ratio and plasma protein parameters were improved,and the skin color and elasticity were improved,the granulation of the wound was fresh and healed well,and the plasma protein level was increased obviously,ALB level in metabolic vehicle group was significantly higher than that in HB formula group (g/L:31.8 ± 2.5 vs.26.7 ± 2.3,P ＜ 0.05).In the metabolic vehicle group,REE value was decreased from the maximum level on the 3rd day (k J/d:7 850.4 ± 947.3) to a stable level after 11 days (k J/d:5 046.3 ± 493.3),and its energy ratio changed significantly,from carbohydrate:fat of 77％ ∶ 21％ with protein consumption gradually transition in the early (3 days) to carbohydrates:fat of 56％ ∶ 44％ without protein consumption in the later stage (11 days),which showed the tendency of energy consumption was reasonable.Conclusion The energy metabolism rule of critical patients on ventilation could be determined by using the accurate metabolic vehicle and dynamic monitoring of REE value,which could be used for the implementation of nutritional support therapy.

Nutritional intake is associated with activities of daily living and complications in older inpatients with stroke.

AIM: There is no consensus on energy intake in the acute stage and how it relates to improvement in activities of daily living (ADL) in geriatric stroke patients. We examined whether energy intake after admission is related to improvement in ADL and complications. METHODS: This retrospective cohort study evaluated the data from stroke inpatients at five acute care hospitals, including the mean daily energy intake during the first week after admission. Patients were categorized into two groups based on their daily energy intake; cases where the energy intake per day reached the basal energy expenditure calculated by the Harris-Benedict equation were designated as the intake energy sufficiency group. Patient characteristics were compared between groups. We assessed ADL using the Functional Independence Measure; a secondary outcome was the presence of complications. RESULTS: Of the 192 participants (mean age 79.6 ± 7.6 years) included in the study, 131 patients were admitted for cerebral infarction, 58 for intracerebral hemorrhage and three for subarachnoid hemorrhage. Of the two groups, that with an energy sufficiency had a greater gain in total Functional Independence Measure (median 27 and 9, respectively; P = 0.001) and a lower complication rate (6.7% and 26.5%, respectively; P ≤ 0.001). Multivariate analyses showed that energy sufficiency was independently associated with the total Functional Independence Measure gain and complication rate. CONCLUSIONS: Energy intake during the first week after admission affected improvement of ADL and onset of complications during hospitalization in geriatric stroke inpatients. Geriatr Gerontol Int 2018; 18: 1334-1339.

Resting energy expenditure and body composition in patients with head and neck cancer: An observational study leading to a new predictive equation.

OBJECTIVES: Patients with head and neck cancer have changes in body composition and resting energy expenditure (REE) related to significant inflammatory processes. We investigated REE and body composition in a population of patients with head and neck cancer, comparing the measured REE with predicted energy expenditure and deriving an equation of anthropometric values and body composition. METHODS: This retrospective, observational, descriptive study of a single center included patients with head and neck cancer. We evaluated nutritional status by body mass index (BMI) and Patient-Generated Subjective Global Assessment (PG-SGA), body composition by electric bioimpedance, and REE by indirect calorimetry (IC). RESULTS: We included 140 patients, most of whom were men (80.7%), 60 y or older (58.6%), and had advanced disease (77.9%). Most were malnourished by BMI standards (77.9%) and severely malnourished according to the PG-SGA (49.3%), with a fat-free mass below the ideal values (82.9%) associated with sarcopenia (92.1%). Hypermetabolism was 57%. When comparing REE with the Harris-Benedict formula, we found the agreement limits from -546 613 to 240 708, the mean difference was -152 953 (95% confidence interval [CI], -185 844 to -120 062) and Pitman's variance test was r = -0.294 (P = 0.001). When we included the activity factor and the thermogenesis factor in REE and compared with Harris-Benedict, we found the agreement limits from -764.423 to 337.087, a mean difference of -213.668 (95% CI -259.684 to -167.652), and the Pitman's variance text at r = -0.292 (P = 0.001). CONCLUSION: Predictive equations, generally recommended by guidelines, are imprecise when compared with IC measures. Therefore, we suggest a new predictive equation.

Determination of the energy requirements in mechanically ventilated critically ill elderly patients in different BMI groups using the Harris-Benedict equation.

BACKGROUND: Due to studies on calorie requirement in mechanically ventilated critically ill elderly patients are few, and indirect calorimetry (IC) is not available in every intensive care unit (ICU). The aim of this study was to compare IC and Harris-Benedict (HB) predictive equation in different BMI groups. METHODS: A total of 177 mechanically ventilated critically ill elderly patients (≧65 years old) underwent IC for measured resting energy expenditure (MREE). Estimated calorie requirement was calculated by the HB equation, using actual body weight (ABW) and ideal body weight (IBW) separately. Patients were divided into four BMI groups. One-way ANOVA and Pearson's correlation coefficient were used for statistical analyses. RESULTS: The mean MREE was 1443.6 ± 318.2 kcal/day, HB(ABW) was 1110.9 ± 177.0 kcal/day and HB(IBW) was 1101.5 ± 113.1 kcal/day. The stress factor (SFA = MREE ÷ HB(ABW)) was 1.43 ± 0.26 for the underweight, 1.30 ± 0.27 for the normal weight, 1.20 ± 0.19 for the overweight, and 1.20 ± 0.31 for the obese. The SFI (SFI = MREE ÷ HB(IBW)) was 1.24 ± 0.24 for the underweight, 1.31 ± 0.26 for the normal weight, 1.36 ± 0.21 for the overweight, and 1.52 ± 0.39 for the obese. MREE had significant correlation both with REE(ABW) = HB(ABW) × SFA (r = 0.46; P < 0.0001) and REE(IBW) = HB(IBW) × SFI (r = 0.43; P < 0.0001). CONCLUSION: IC is the best accurate method for assessing calorie requirement of mechanically ventilated critically ill elderly patients. When IC is not available, using the predictive HB equation is an alternative choice. Calorie requirement can be predicted by HB(ABW) × 1.20-1.43 for critically ill elderly patients according to different BMI groups, or using HB(IBW) × 1.24-1.52 for patients with edema, ascites or no available body weight data.

Comparison of the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry for Measurement of Basal Metabolic Rate among Adult Obese Filipino Patients with Prediabetes or Type 2 Diabetes Mellitus.

OBJECTIVES: To compare mean basal metabolic rate (BMR) estimated using Harris-Benedict equation (HB) and Bioelectrical Impedance Analysis (BIA) and the BMR measured using Indirect Calorimetry (IC) among adult obese Filipino patients with prediabetes or type 2 diabetes mellitus (T2DM). METHODOLOGY: This was a multi-center, cross-sectional study based on review of outpatient medical records of adult, obese Filipino patients with pre-diabetes or type 2 diabetes mellitus who were seen prior to weight loss intervention at the Outpatient Clinic of St. Luke's Medical Center-Quezon City and the Metabolic and Diabetes Center of Providence Hospital from August 2017 to January 2018. BMR was derived using three methods: Harris-Benedict equation, Bioelectrical Impedance Analysis and Indirect Calorimetry. RESULTS: A total of 153 subjects were included in the study. Eighty subjects (52%) have pre-diabetes while 73 subjects (48%) were diagnosed with T2DM. The mean BMR measured using IC is 1299±252 kcal/day while estimated mean BMR predicted using HB equation and BIA were 1628±251 kcal/day and 1635±260 kcal/day, respectively. Compared to measurement by IC, HBE and BIA significantly overestimated the mean BMR by 329 and 336 kcal/day, respectively (p-value=<0.0001). IC measured BMR showed strong positive correlation with weight and moderate positive correlation with height. Multiple stepwise regression analysis yielded the BMR prediction equation: BMR (kcal/day) = -780.806 + (11.108 x weight in kg) + (7.164 x height in cm). CONCLUSION: Among obese Filipinos with T2DM or prediabetes, HB equation and BIA tend to overestimate the BMR measured using IC.

Routine resting energy expenditure measurement increases effectiveness of dietary intervention in obesity.

AIMS: Primary outcome of this observational study was to compare weight changes in two groups of overweight and obese individuals: subjects who had a diet prescribed on the base of resting energy expenditure (REE) measured by indirect calorimetry and subjects whose REE was estimated by a predictive equation. In addition, we analyzed differences in weight and metabolic parameter variation in subjects with and without an adequate to predicted REE. METHODS: We retrospectively analyzed data of 355 overweight and obese patients: 215 on a diet based on REE measured by indirect calorimetry and 140 following a diet based on REE estimated by the Harris-Benedict equation. Anthropometric and metabolic parameters were evaluated for 18 months from baseline. Propensity score adjustment was used to adjust for known differences between the groups being compared. RESULTS: A significant greater decrease in body weight was observed in the group that underwent indirect calorimetry compared to the group that did not undergo it (p < 0.001). No significant differences were observed between patients with not adequate to predicted REE compared to patients with adequate to predicted REE. CONCLUSIONS: A weight reduction program based on REE measurement appears more effective than a dietary program based on predictive formulas. This study suggests the routine use of indirect calorimetry in all weight reduction procedures.

Comparison of the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry for Measurement of Basal Metabolic Rate among adult obese Filipino patients with prediabetes or Type 2 Diabetes Mellitus / Journal of the ASEAN Federation of Endocrine Societies

Objectives@#To compare mean basal metabolic rate (BMR) estimated using Harris-Benedict equation (HB) and Bioelectrical Impedance Analysis (BIA) and the BMR measured using Indirect Calorimetry (IC) among adult obese Filipino patients with prediabetes or type 2 diabetes mellitus (T2DM).@*Methodology@#This was a multi-center, cross-sectional study based on review of outpatient medical records of adult, obese Filipino patients with pre-diabetes or type 2 diabetes mellitus who were seen prior to weight loss intervention at the Outpatient Clinic of St. Luke’s Medical Center-Quezon City and the Metabolic and Diabetes Center of Providence Hospital from August 2017 to January 2018. BMR was derived using three methods: Harris-Benedict equation, Bioelectrical Impedance Analysis and Indirect Calorimetry.@*Results@#A total of 153 subjects were included in the study. Eighty subjects (52%) have pre-diabetes while 73 subjects (48%) were diagnosed with T2DM. The mean BMR measured using IC is 1299±252 kcal/day while estimated mean BMR predicted using HB equation and BIA were 1628±251 kcal/day and 1635±260 kcal/day, respectively. Compared to measurement by IC, HBE and BIA significantly overestimated the mean BMR by 329 and 336 kcal/day, respectively (p-value=<0.0001). IC measured BMR showed strong positive correlation with weight and moderate positive correlation with height. Multiple stepwise regression analysis yielded the BMR prediction equation: BMR (kcal/day) = -780.806 + (11.108 x weight in kg) + (7.164 x height in cm).@*Conclusion@#Among obese Filipinos with T2DM or prediabetes, HB equation and BIA tend to overestimate the BMR measured using IC.

Consistency study of indirect calorimetry and HB equation for measuring energy expenditure of patients with multiple injury receiving mechanical ventilation / 中华危重病急救医学

Objective To evaluate the difference and correlation of 24-hour energy expenditure in patients with multiple trauma receiving mechanical ventilation predicted by indirect calorimetry (IC) and HB formula. Methods 140 patients with multiple trauma receiving mechanical ventilation admitted to intensive care unit (ICU) of the General Hospital of Ningxia Medical University from December 1st, 2016 to August 31st, 2017 were enrolled. The 24-hour energy expenditure of patients was repeatedly measured at 1, 3, 5, and 7 days after ICU admission by IC method, and the 24-hour energy expenditure measured by IC method was used as the "gold standard" to calculate the 24-hour kilogram body weight energy expenditure. The 24-hour energy expenditure value measured by IC method was compared with the 24-hour energy expenditure predicted value calculated by HB formula method, the consistency of the two measurement methods was detected by Bland-Altman method, and the correlation between the two measurement methods was analyzed by Pearson method and the linear equation was fitted. Results The 24-hour energy expenditure of patients at 1, 3, 5 and 7 days after ICU was repeatedly measured by IC method for 501 times, and there was no significant difference in 24-hour energy expenditure (kJ/d: 8 163.1±1 599.4, 8 221.1±1 694.7, 8 445.8±1 756.4, 8 707.0±1 820.7, respectively, F = 2.451, P = 0.063) and 24-hour kilogram body weight energy expenditure (kJ·kg-1·d-1: 120.5±18.9, 121.4±19.7, 122.7±19.3, 121.4±19.3, respectively, F = 0.252, P = 0.860) at each time point, indicating that the first week of multi-injury patients had no significant changes in energy metabolism. The consistency between the 24-hour energy expenditure measured by IC method on the first day [(8 163.1±1 599.4) kJ/d] and that predicted by HB formula method [(6 568.8±782.0) kJ/d] was analyzed. The results showed that there was significant bias between the two methods, with an average bias of -(1 591.8±121.4) kJ/d, but the correlation analysis showed that there was a linear correlation between them (r = 0.439, P = 0.000), using one-way regression, the fitted linear equation was Y = 2 270.5+0.897X (X was 24-hour energy expenditure predicted by the HB formula). Conclusions The energy metabolism of patients with multiple trauma receiving mechanical ventilation is not obvious within 1 week. The HB formula method underestimates the 24-hour energy expenditure of patients. The prediction formula can be used to correct the HB formula and further to improve the accuracy of predicting the 24-hour energy consumption of patients.