Next Article in Journal
A 60-Day Green Tea Extract Supplementation Counteracts the Dysfunction of Adipose Tissue in Overweight Post-Menopausal and Class I Obese Women
Previous Article in Journal
Identifying Predictors for Minimum Dietary Diversity and Minimum Meal Frequency in Children Aged 6–23 Months in Uganda
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 14
Issue 24
10.3390/nu14245210
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements

by
Fumiya Kawase
1,2,*,
Yoshiyuki Masaki
3,4,
Hiroko Ozawa
5,
Manami Imanaka
5,
Aoi Sugiyama
5,
Hironari Wada
6,
Ryokichi Goto
6,
Shinya Kobayashi
3 and
Takayoshi Tsukahara
2
1
Department of Nutrition, Asuke Hospital Aichi Prefectural Welfare Federation of Agricultural Cooperatives, 20 Nakata, Yagami-cho, Toyota 444-2351, Aichi, Japan
2
Graduate School of Nutritional Sciences, Nagoya University of Arts and Sciences, 57, Iwasaki-cho, Nisshin 470-0196, Aichi, Japan
3
Department of Internal Medicine, Asuke Hospital Aichi Prefectural Welfare Federation of Agricultural Cooperatives, 20 Nakata, Yagami-cho, Toyota 444-2351, Aichi, Japan
4
Department of Community-Based Medical Education, Graduate School of Medical Sciences, Nagoya City University, Mizuho-ku, Nagoya 467-8601, Aichi, Japan
5
Department of Nursing, Asuke Hospital Aichi Prefectural Welfare Federation of Agricultural Cooperatives, 20 Nakata, Yagami-cho, Toyota 444-2351, Aichi, Japan
6
Department of Rehabilitation Therapy, Asuke Hospital Aichi Prefectural Welfare Federation of Agricultural Cooperatives, 20 Nakata, Yagami-cho, Toyota 444-2351, Aichi, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(24), 5210; https://doi.org/10.3390/nu14245210
Submission received: 8 November 2022 / Revised: 30 November 2022 / Accepted: 5 December 2022 / Published: 7 December 2022
(This article belongs to the Section Clinical Nutrition)
Browse Figure
Review Reports Versions Notes

Abstract

:
Determining energy requirements are an important component of nutritional support for patients with malnutrition; however, the validity of prediction equations for resting energy expenditure (REE) is disputed in older hospitalized patients. We aimed to assess the validity of these equations in older hospitalized patients in Japan. This was a single-center, cross-sectional study of 100 patients aged ≥70 years, hospitalized between January 2020 and December 2021. REE was measured using an indirect calorimeter and was compared to the predicted values calculated from five REE prediction equations. The mean (95% confidence interval) measured REE was 968.1 (931.0, 1005.3) kcal/day, and the mean predicted REE was higher for the FAO/WHO/UNU (1014.3 [987.1, 1041.6] kcal/day, p = 0.164) and Schofield (1066.0 [1045.8, 1086.2] kcal/day, p < 0.001) equations and lower for the Harris-Benedict (898.6 [873.1, 924.1] kcal/day, p = 0.011), Ganpule (830.1 [790.3, 869.9] kcal/day, p < 0.001), and body weight (kg) × 20 (857.7 [821.9, 893.5] kcal/day, p < 0.001) equations. In the age group analysis, none of the predicted values were within a 10% error for more than 80% of patients aged 70–89 years and ≥90 years. The five REE prediction equations did not provide accurate estimates. Validated REE prediction equations need to be developed for older hospitalized patients.

1. Introduction

Malnutrition is recognized as a global issue with a prevalence of 0.8–24.6% in the increasing global aging population [1]. Malnutrition is associated with adverse clinical outcomes including prolonged hospital stays [2], increased in-hospital mortality [3], and increased in-hospital falls [4]. Thus, interventions for malnutrition have the potential to improve patient care and quality of life and reduce healthcare costs [5]. The EFFORT Trials [6,7], also known as the Effect for Protein Dosing in Critically Ill Patients study, examined the effects of personalized nutrition management on hospitalized patients, and showed reduced mortality (Hazard Ratio 0.65) after providing nutrition support to 2088 patients. A key component of the positive impacts of the EFFORT trials was the assessment of energy requirements using indirect calorimetry or the Harris-Benedict equation. Energy intake is one of the most important factors to consider in providing nutritional support, and inappropriate energy intake is associated with adverse outcomes. This has been reported in critically ill patients [8], as well as older patients with diabetes [9]. Therefore, a more accurate calculation of daily energy requirements are required for all patients.
Resting energy expenditure (REE) comprises the largest component of total energy expenditure and is, therefore, the most important determinant of its value [10]. In clinical practice, energy requirements are generally calculated by multiplying the estimated REE by physical activity and disease coefficients [11] and are rarely calculated from measured values. REE can be measured using indirect calorimetry, which is a noninvasive method and is often considered the gold standard for deriving REEs. However, indirect calorimetry is not routinely available in many healthcare or primary care facilities because of the high cost of equipment and technical complexity [12]. Therefore, several prediction equations have been developed to predict REE. The Harris-Benedict equation [13] is a commonly used REE prediction equation in clinical practice [14]; however, there are conflicting reports regarding the accuracy of equations in predicting REE in older hospitalized patients [15,16]. Thus, there is no consensus on the utility of the Harris-Benedict equation for older adults. In general, most REE prediction equations have been developed for healthy adult populations, and their predictive accuracy among older adults has been questioned [10]. A systematic review of healthy older adults showed that several REE prediction equations, including the Harris-Benedict equation, predicted a larger REE value than the measured REE values, suggesting that accurate estimation using prediction equations is difficult to obtain for older adults [17]. Therefore, in nutritional management guidelines for older adults, it is not recommended to use REE prediction equations to determine energy requirements [11,18]. This guideline recommends a Total energy expenditure of approximately 30 kcal/kg, since the REE of older adults is approximately 20 kcal/kg.
The patterns in prediction accuracy and error of REE prediction equations used for older Japanese hospitalized patients are unknown. The accuracy of several predictive equations is lower in specific populations, such as older hospitalized patients, and has not been validated for use in Japanese older patients. Therefore, in this study, we aimed to investigate the predictive accuracy of REE prediction equations in older hospitalized patients by comparing the calculated predicted REE values with the measured REE values obtained using indirect calorimetry.

2. Materials and Methods

2.1. Study Design and Participants

A single-center, cross-sectional study was conducted in a 200-bed general hospital on older patients admitted to the internal medicine ward between January 2020 and December 2021. This study was approved by the Ethical Review Committee for Clinical Trials and Clinical Research, Aichi Prefectural Welfare Federation of Agricultural Cooperatives, Asuke Hospital (registration number: K19-009) and conducted in accordance with the Declaration of Helsinki and the Japanese Ethical Guidelines for Medical Research Involving Human Subjects (Ministry of Education, Culture, Sports, Science and Technology and Ministry of Health, Labour and Welfare). This study has been registered in the UMIN Clinical Trials Registration System (UMIN study ID: UMIN000040499).
Eligible patients included those who: (1) were aged ≥70 years, (2) were hospitalized at the Department of Internal Medicine, and (3) consented to participate in all procedures related to the study. The exclusion criteria were: those (1) receiving oxygen therapy or being mechanically ventilated, (2) undergoing enteral nutrition management, (3) undergoing dialysis management, (4) with the presence of respiratory leaks, and (5) with the presence of poor outcomes whose hospital stay was estimated to be less than one week by the attending physician. Patients who could not complete indirect calorimetry and the body composition tests using the multifrequency bioelectric impedance analysis (BIA) method and who developed acute illness during hospitalization and were transferred to another hospital were also excluded from the analysis. Indirect calorimetry, body composition test, and diagnosis of malnutrition were performed within approximately one week of obtaining written informed consent from the patients.

2.2. Indirect Calorimetry

REE was measured using an indirect calorimetry device (AE-300S, Minato Medical Science, Osaka, Japan). Indirect calorimetry was performed in a hospital room after an overnight (12 h) fast. Calibrations of flow rate and standard gas were performed before every measurement, and the room temperature was controlled at 23–25 °C. Patients rested for 30 min before undergoing measurements for 15 min between 7:00 and 8:00 a.m. A pump was used to suction through the hood at a constant rate. Using the Weir equation (Equation (1)) [19], the 24 h REE was calculated from the measured oxygen consumption (VO2) and carbon dioxide production (VCO2).
REE =   3.94 ×   V ˙ O 2 + 1.11 ×   V ˙ CO 2   × 1440

2.3. Measurements and Body Composition Analysis

Patients were evaluated using the Charlson Comorbidity Index [20] and Mini Nutritional Assessment-Short Form (MNA-SF) score [21] on the same day as indirect calorimetry. Body composition was evaluated by the BIA method using a body composition analyzer (Inbody S10, Inbody Co, Seoul, Republic of Korea) and calf circumference. BIA measurements were obtained from patients who had fasted overnight (12 h) and were in a supine position at rest for at least 15 min after urination. After the BIA measurements were obtained, the skeletal muscle index (SMI) was calculated. Calf circumference was measured at the thickest part of the leg opposite to the dominant leg with the right leg bent at 90° in the supine position.

2.4. Global Leadership Initiative on Malnutrition (GLIM) Criteria

The Global Leadership Initiative on Malnutrition (GLIM) criteria were used to diagnose malnutrition [22]. All patients were screened for malnutrition based on the MNA-SF score and were asked about weight change in the past 3–6 months. Body mass index (BMI) was calculated, and muscle mass was assessed by the SMI calculated using the BIA method. Dietary intake or decreased digestion and absorption were assessed based on information from just before and several days after admission. To assess malnutrition severity, a low BMI was assessed using the cutoff value of 17.8 kg/m2 for older Japanese adults [3], and severe muscle mass loss was defined as a decrease of 10% or more below the SMI value of <7.0 kg/m2 in men and <5.7 kg/m2 in women specified by the Asian Working Group for Sarcopenia 2019 consensus update [23].

2.5. Predictive Equations for REE

The five prediction equations used in this analysis were: (1) Harris-Benedict [13], (2) FAO/WHO/UNU [24], (3) Ganpule [25], (4) Schofield [26], and (5) body weight (kg) × 20. The first four are REE prediction equations discussed in the Dietary Reference Intakes for Japanese (2020) [27], and the fifth one is given in the European Society for Clinical Nutrition and Metabolism guidelines on clinical nutrition and hydration in geriatrics [11]. Characteristics of each equation are presented in Table 1.

2.6. Statistical Analysis

Previous study reported a difference of 150 ± 103 kcal/day between the Harris-benedict equation and the measured REE [28], so this value was used to calculate the sample size in advance. Therefore, using power = 0.8 and α error = 0.01 by Bonferroni correction, more than 60 patients were needed.
Statistical analysis was performed in three groups: overall, aged 70-89, and aged 90 and older.
Parametric continuous variables are expressed as means ± SD, and nonparametric continuous variables are expressed as medians. One-way analysis of variance was used to compare measured REE and predicted REE, and multiple comparison tests with measured REE as a control were conducted as post hoc tests. R 4.1.0 (R Foundation for Statistical Computing, Vienna, Austria) was used for all analyses [29].

3. Results

In this study, 100 of the 127 patients who participated completed the measurements. Table 2 shows the characteristics of the 100 patients. For all patients, the mean age was 88.1 ± 6.8 years, and 34% were male. The mean BMI was 20.1 ± 3.5 kg/m2, and the mean SMI was 4.67 ± 1.47 kg/m2. According to the GLIM criteria, the proportions of severe and moderate malnutrition were 72% and 28%, respectively.

3.1. Accuracy of the REE Prediction Equations

The results for the measured REE and calculated REE values from each of the prediction equations are shown in Table 3 and Figure 1A. For the entire study group, the mean (95% confidence interval) value of the measured REE was 968.1 (931.0, 1005.3) kcal/day. When compared with the measured REE, the Harris-Benedict, Ganpule, and body weight (kg) × 20 equations showed lower REE values (898.6 [873.1, 924.1] kcal/day, p = 0.011; 830.1 [790.3, 869.9] kcal/day, p < 0.001; 857.7 [821.9, 893.5] kcal/day, p < 0.001; respectively). The FAO/WHO/UNU and Schofield equations led to higher REE values (1014.3 [987.1, 1041.6] kcal/day, p = 0.164; 1066.0 [1045.8, 1086.2] kcal/day, p < 0.001; respectively). However, the trends for the FAO/WHO/UNU equation were not statistically significant. In the 70–89 years age group, the mean measured REE was 1041.8 (987.5, 1096.1) kcal/day; when compared to the measured REE, the Harris-Benedict, Ganpule, and body weight (kg) × 20 equations calculated lower REE values (964.9 [929.7, 1000.1] kcal/day, p = 0.068; 924.2 [870.5, 977.8] kcal/day, p < 0.001; 929.0 [880.0, 978.1] kcal/day, p = 0.002; respectively), and the FAO/WHO/UNU and Schofield equations calculated higher values (1074.5 [1034.9, 1114.0] kcal/day, p = 0.761; 1105.5 [1077.5, 1133.6] kcal/day, p = 0.174; respectively). However, the trends for the Harris-Benedict, FAO/WHO/UNU, and Schofield equations were not statistically significant. In the 90+ years age group, the measured REE was 891.4 (850.3, 932.6) kcal/day. When compared to the measured REE, the Harris-Benedict, Ganpule, and body weight (kg) × 20 equations calculated lower values (829.6 [804.3, 854.9] kcal/day, p = 0.071; 732.3 [687.1, 777.5] kcal/day, p < 0.001; 783.5 [739.7, 827.2] kcal/day, p < 0.001; respectively), and the FAO/WHO/UNU and Schofield equations calculated higher values (951.8 [923.1, 980.4] kcal/day, p = 0.081; 1024.8 [1000.3, 1049.3] kcal/day, p < 0.001; respectively). However, the trends for the Harris-Benedict and FAO/WHO/UNU equations were not statistically significant.

3.2. Individual-Level Bias of the REE Prediction Equation

During examination of the error of the predicted REE compared to the actual measured REE, we found that none of the prediction equations yielded a predicted REE within a 10% error of the measured REE for more than 80% of individuals in either age group (Figure 1B). For the entire study group, the proportion of individuals within a 10% error was 51% for the Harris-Benedict equation, 59% for the FAO/WHO/UNU equation, 30% for the Ganpule equation, 38% for the Schofield equation, and 35% for the body weight (kg) × 20 equation. For patients aged 70–89 years, the proportion of individuals within a 10% error was 53% for the Harris-Benedict equation, 65% for the FAO/WHO/UNU equation, 37% for the Ganpule equation, 45% for the Schofield equation, and 35% for the body weight (kg) × 20 equation; for patients aged 90 years or older, the proportion of individuals within a 10% error was 49% for the Harris-Benedict equation, 53% for the FAO/WHO/UNU equation, 22% for the Ganpule equation, 31% for the Schofield equation, and 35% for the body weight (kg) × 20 equation. From these findings, the error in predicted REE values was higher for patients aged 90+ years.

4. Discussion

In this study, REE was measured by indirect calorimetry, which is considered the gold-standard method of measuring REE, in 100 Japanese patients aged 70–101 years, and body composition was measured using the validated BIA method. We found that the currently available REE prediction equations had clinically relevant errors relative to the measured REE for older Japanese people. Additionally, the error was larger for patients aged over 90 years, which is an important finding to consider for the energy requirements of hospitalized older patients.
The Harris-Benedict equation is commonly used for REE prediction in clinical practice in Japan. However, our study revealed that this equation predicted a value that was, on average, 69.5 kcal/day less than the measured REE. Furthermore, compared to the measured REE, the FAO/WHO/UNU equation and Schofield equation overestimated by 46.2 kcal/day and 97.9 kcal/day, respectively, while the Ganpule equation and body weight × 20 equation underestimated by 138.0 kcal/day and 110.4 kcal/day, respectively. A previous study, that measured REE by indirect calorimetry in 68 hospitalized patients aged 74.4 ± 9.3 years, reported that the predicted REE computed by the Harris-Benedict equation was 26.7 kcal/day lower than the measured REE, displaying a smaller error than our findings [30]. Furthermore, Siervo et al. study, the FAO/WHO/UNU equation was 105 kcal/day higher than the measured REE, while the Schofield equation was 7.2 kcal/day lower, and the accuracy of the Schofield equation seems to be higher. In our findings, both prediction equations were overestimated, and thus, our study showed a different trend from previous studies. Similarly, Neelemaat et al. assessed indirect calorimetry on 194 hospitalized older patients aged 74.3 ± 9.1 years and found that the predicted REE calculated by the Harris-Benedict equation was 141 kcal/day lower than the measured REE, displaying a larger error than our findings. Furthermore, in the Neelemaat et al. study, the FAO/WHO/UNU equation and the Schofield equation showed smaller values than the measured REE. The authors also concluded that none of the analyzed equations predicted REE with sufficient accuracy in older hospitalized patients in their study [15]. Consistent with these reports, the predicted REE calculated by the Harris-Benedict equation in our study was lower than the actual value measured using indirect calorimetry in older hospitalized patients, although the magnitude of error varied. In contrast, Miyake et al. reported that, in healthy Japanese women aged ≥70 years, the predicted REE calculated by the Harris-Benedict equation was observed to be 86 ± 83 kcal greater than the actual value measured by indirect calorimetry [28]. Their study analyzed a healthy population with a maximum age of 79 years, which is a different population from that in our study and may explain the different values. The authors also concluded that the predicted REE using the Ganpule equation had the highest prediction accuracy compared to the Harris-Benedict, Schofield, and FAO/WHO/UNU equations [28]. This study examined the prediction equations presented in the Dietary Reference Intakes for Japanese (Males; body weight × 21.5, Females; body weight × 20.7), which are similar to the body weight × 20 prediction equation. The results reported values approximately 32 to 80 kcal/day greater than the measured REE. Due to the slightly larger coefficients and the different patient settings, the trends were different from our findings. however, Gaillard et al. observed that the Harris-Benedict equation had the highest estimation accuracy for older patients [31]. In our study, the Harris-Benedict and FAO/WHO/UNU equations showed relatively high estimation accuracy among the prediction equations analyzed, however, the observed errors were too large for the equations to be used in clinical practice. A recent review reported that 210 different REE prediction equations, some of which were developed specifically for older adults, had low agreement as assessed by the intraclass correlation coefficient and large heterogeneity in REE prediction value [32]. Therefore, it is desirable to find suitable prediction equations for various factors in addition to age, such as sex, physique, activity, and morbidity. In a meta-analysis of healthy older adults, the Harris-Benedict equation showed the highest accuracy at the individual level, with 69% of individuals with predicted REE values within a 10% error [17]. Therefore, the feasibility of the Harris-Benedict, FAO/WHO/UNU, Ganpule, Schofield, and body weight × 20 as predictive equations for older hospitalized patients seems to be limited, as there are conflicting reports in the literature.
Additionally, previous reports studying the estimation accuracy of REE prediction equations for cancer patients reported that none of the equations had sufficient accuracy in predicting actual REE, suggesting that accurate individual-level REE prediction is also difficult for patients with disease [33,34]. REE prediction equations have recently been developed for chronic kidney disease patients specifically [35], and recent studies suggest that REE prediction equations may need to be developed for each disease. Our findings have not been evaluated in the context of diseases; therefore, further studies are needed to clarify this relationship.
Adequate energy intake (approximately ≥ 30 kcal/kg/day) has been reported to reduce post-discharge mortality [36] and increase rehabilitation efficiency in patients with dysphagia [37]. In contrast, both overfeeding and underfeeding have been associated with adverse clinical outcomes in critically ill patients [8]. Similarly, a U-shaped relationship between energy intake and mortality was observed in older Japanese patients with diabetes [9]. As accurate estimates are needed to provide good clinical outcomes for hospitalized older patients, unvalidated predictive equations should be avoided.
In a study conducted by Schuetz et al., it was found that individualized nutrition management prevented adverse clinical outcomes in hospitalized patients, and a key factor in obtaining this result was the determination of individualized energy requirements [6]. In older adults, appetite decreases with age, and malnutrition develops rapidly [38]. Individualized nutritional management is a powerful strategy for preventing malnutrition in older patients; however, the findings of our study showed that there was a large error in the predicted REE values calculated from prediction equations. Therefore, to improve clinical outcomes in older hospitalized patients, prediction equations for REE should not be used in a generalized manner, and development of REE prediction equations specific to the disease and patient characteristics is necessary.
This study had a few limitations. First, this was a single-center cross-sectional study conducted in Japan, which limits the generalization of results due to possible selection bias of participants. Second, since the study included individuals with diverse diseases and backgrounds that were not controlled for, it is possible that our results were confounded by such factors. The impact of disease on REE is unknown even for diseases that have a significant impact on metabolism; therefore, the relationship between disease and REE should be further investigated. Third, in this study, we did not consider the impact of BMI on the accuracy of the REE prediction equation because we focused our analysis on each age group. In most REE prediction equations, BMI components such as height and weight were used as variables in the prediction equation, therefore, future studies are needed to determine the accuracy of the REE prediction equation for each BMI group.

5. Conclusions

In this study, in older patients aged 70–101 years, the large error between the actual REE measured by indirect calorimetry and the predicted REE calculated by the five prediction equations indicates that REE prediction equations may not be valid for clinical use. This error was larger in patients over 90 years of age. The results of this study showed that none of the five equations compared had predictive equations with high estimation accuracy in older Japanese hospitalized patients. There is a need to develop REE prediction equations with greater accuracy, especially for the accurate prediction of REE in older patients.

Author Contributions

Conceptualization, F.K., Y.M., S.K. and T.T.; methodology, F.K. and T.T.; formal analysis, F.K.; investigation, F.K., Y.M., H.O., M.I., A.S., H.W. and R.G.; resources, F.K.; data curation, F.K.; writing—original draft preparation, F.K.; writing—review and editing, Y.M., H.O., M.I., A.S., H.W., R.G., S.K. and T.T.; supervision, T.T.; project administration, T.T.; funding acquisition, F.K. and T.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant-in-aid from the Nakatani Suzuyo Memorial Fund for Nutrition and Dietetics, Tokyo, Japan (to Kawase).

Institutional Review Board Statement

This study was approved by the Ethical Review Committee for Clinical Trials and Clinical Research, Aichi Prefectural Welfare Federation of Agricultural Cooperatives, Asuke Hospital (registration number: K19-009). This study was registered in the UMIN Clinical Trials Registration System (UMIN study ID: UMIN000040499).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data sharing is not applicable to this article.

Acknowledgments

We would like to thank the medical staff at Asuke Hospital for their support and assistance with the data collection. We are also grateful to Satomi Oshima for the helpful discussions and comments on the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Crichton, M.; Craven, D.; Mackay, H.; Marx, W.; de van der Schueren, M.; Marshall, S. A systematic review, meta-analysis and meta-regression of the prevalence of protein-energy malnutrition: Associations with geographical region and sex. Age Ageing 2019, 48, 38–48. [Google Scholar] [CrossRef] [PubMed]
  2. O′Shea, E.; Trawley, S.; Manning, E.; Barrett, A.; Browne, V.; Timmons, S. Malnutrition in Hospitalised Older Adults: A Multicentre Observational Study of Prevalence, Associations and Outcomes. J. Nutr. Health Aging 2017, 21, 830–836. [Google Scholar] [CrossRef] [PubMed]
  3. Maeda, K.; Ishida, Y.; Nonogaki, T.; Mori, N. Reference body mass index values and the prevalence of malnutrition according to the Global Leadership Initiative on Malnutrition criteria. Clin. Nutr. 2020, 39, 180–184. [Google Scholar] [CrossRef] [PubMed]
  4. Ishida, Y.; Maeda, K.; Nonogaki, T.; Shimizu, A.; Yamanaka, Y.; Matsuyama, R.; Kato, R.; Mori, N. Malnutrition at Admission Predicts In-Hospital Falls in Hospitalized Older Adults. Nutrients 2020, 12, 541. [Google Scholar] [CrossRef] [Green Version]
  5. Khalatbari-Soltani, S.; Marques-Vidal, P. The economic cost of hospital malnutrition in Europe; a narrative review. Clin. Nutr. ESPEN 2015, 10, e89–e94. [Google Scholar] [CrossRef] [Green Version]
  6. Schuetz, P.; Fehr, R.; Baechli, V.; Geiser, M.; Deiss, M.; Gomes, F.; Kutz, A.; Tribolet, P.; Bregenzer, T.; Braun, N.; et al. Individualised nutritional support in medical inpatients at nutritional risk: A randomised clinical trial. Lancet 2019, 393, 2312–2321. [Google Scholar] [CrossRef]
  7. Lobo, D.N. Improving outcomes with a little EFFORT. Lancet 2019, 393, 2278–2280. [Google Scholar] [CrossRef]
  8. Zusman, O.; Theilla, M.; Cohen, J.; Kagan, I.; Bendavid, I.; Singer, P. Resting energy expenditure, calorie and protein consumption in critically ill patients: A retrospective cohort study. Crit. Care 2016, 20, 367. [Google Scholar] [CrossRef] [Green Version]
  9. Omura, T.; Tamura, Y.; Yamaoka, T.; Yoshimura, Y.; Sakurai, T.; Umegaki, H.; Kamada, C.; Iimuro, S.; Ohashi, Y.; Ito, H.; et al. Assessing the association between optimal energy intake and all-cause mortality in older patients with diabetes mellitus using the Japanese Elderly Diabetes Intervention Trial. Geriatr. Gerontol. Int. 2020, 20, 59–65. [Google Scholar] [CrossRef]
  10. Manini, T.M. Energy expenditure and aging. Ageing Res. Rev. 2010, 9, 1–11. [Google Scholar] [CrossRef]
  11. Volkert, D.; Beck, A.M.; Cederholm, T.; Cruz-Jentoft, A.; Goisser, S.; Hooper, L.; Kiesswetter, E.; Maggio, M.; Raynaud-Simon, A.; Sieber, C.C.; et al. ESPEN guideline on clinical nutrition and hydration in geriatrics. Clin. Nutr. 2019, 38, 10–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Achamrah, N.; Delsoglio, M.; De Waele, E.; Berger, M.M.; Pichard, C. Indirect calorimetry: The 6 main issues. Clin. Nutr. 2021, 40, 4–14. [Google Scholar] [CrossRef]
  13. Harris, J.A.; Benedict, F.G. A Biometric Study of Human Basal Metabolism. Proc. Natl. Acad. Sci. USA 1918, 4, 370–373. [Google Scholar] [CrossRef] [Green Version]
  14. Bendavid, I.; Lobo, D.N.; Barazzoni, R.; Cederholm, T.; Coëffier, M.; De Van Der Schueren, M.; Fontaine, E.; Hiesmayr, M.; Laviano, A.; Pichard, C.; et al. The centenary of the Harris–Benedict equations: How to assess energy requirements best? Recommendations from the ESPEN expert group. Clin. Nutr. 2021, 40, 690–701. [Google Scholar] [CrossRef] [PubMed]
  15. Neelemaat, F.; van Bokhorst-de van der Schueren, M.A.; Thijs, A.; Seidell, J.C.; Weijs, P.J. Resting energy expenditure in malnourished older patients at hospital admission and three months after discharge: Predictive equations versus measurements. Clin. Nutr. 2012, 31, 958–966. [Google Scholar] [CrossRef] [PubMed]
  16. Alix, E.; Berrut, G.; Boré, M.; Bouthier-Quintard, F.; Buia, J.M.; Chlala, A.; Cledat, Y.; d′Orsay, G.; Lavigne, C.; Levasseur, R.; et al. Energy requirements in hospitalized elderly people. J. Am. Geriatr. Soc. 2007, 55, 1085–1089. [Google Scholar] [CrossRef]
  17. Cioffi, I.; Marra, M.; Pasanisi, F.; Scalfi, L. Prediction of resting energy expenditure in healthy older adults: A systematic review. Clin. Nutr. 2021, 40, 3094–3103. [Google Scholar] [CrossRef]
  18. Gomes, F.; Schuetz, P.; Bounoure, L.; Austin, P.; Ballesteros-Pomar, M.; Cederholm, T.; Fletcher, J.; Laviano, A.; Norman, K.; Poulia, K.A.; et al. ESPEN guidelines on nutritional support for polymorbid internal medicine patients. Clin. Nutr. 2018, 37, 336–353. [Google Scholar] [CrossRef] [Green Version]
  19. Weir, J.B. New methods for calculating metabolic rate with special reference to protein metabolism. J. Physiol. 1949, 109, 1–9. [Google Scholar] [CrossRef]
  20. Charlson, M.E.; Pompei, P.; Ales, K.L.; MacKenzie, C.R. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J. Chronic Dis. 1987, 40, 373–383. [Google Scholar] [CrossRef]
  21. Rubenstein, L.Z.; Harker, J.O.; Salvà, A.; Guigoz, Y.; Vellas, B. Screening for undernutrition in geriatric practice: Developing the short-form mini-nutritional assessment (MNA-SF). J. Gerontol. A Biol. Sci. Med. Sci. 2001, 56, M366–M372. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Cederholm, T.; Jensen, G.L.; Correia, M.; Gonzalez, M.C.; Fukushima, R.; Higashiguchi, T.; Baptista, G.; Barazzoni, R.; Blaauw, R.; Coats, A.; et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clin. Nutr. 2019, 38, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Chen, L.K.; Woo, J.; Assantachai, P.; Auyeung, T.W.; Chou, M.Y.; Iijima, K.; Jang, H.C.; Kang, L.; Kim, M.; Kim, S.; et al. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J. Am. Med. Dir. Assoc. 2020, 21, 300–307.e302. [Google Scholar] [CrossRef] [PubMed]
  24. Joint, F.A.O.; World Health Organization. Energy and Protein Requirements: Report of a Joint FAO/WHO/UNU Expert Consultation; World Health Organization: Geneva, Switzerland, 1985; Volume 724, pp. 1–206. [Google Scholar]
  25. Ganpule, A.A.; Tanaka, S.; Ishikawa-Takata, K.; Tabata, I. Interindividual variability in sleeping metabolic rate in Japanese subjects. Eur. J. Clin. Nutr. 2007, 61, 1256–1261. [Google Scholar] [CrossRef] [Green Version]
  26. Schofield, W.N. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr. Clin. Nutr. 1985, 39, (Suppl. S1). 5–41. [Google Scholar]
  27. Ministry of Health Labour and Welfare of Japan. Dietary Reference Intakes for Japanese. Available online: https://www.mhlw.go.jp/content/10904750/000586553.pdf (accessed on 1 November 2022).
  28. Miyake, R.; Tanaka, S.; Ohkawara, K.; Ishikawa-Takata, K.; Hikihara, Y.; Taguri, E.; Kayashita, J.; Tabata, I. Validity of Predictive Equations for Basal Metabolic Rate in Japanese Adults. J. Nutr. Sci. Vitaminol. 2011, 57, 224–232. [Google Scholar] [CrossRef] [Green Version]
  29. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria; Available online: https://R-project.org/ (accessed on 22 November 2021).
  30. Siervo, M.; Bertoli, S.; Battezzati, A.; Wells, J.C.; Lara, J.; Ferraris, C.; Tagliabue, A. Accuracy of predictive equations for the measurement of resting energy expenditure in older subjects. Clin. Nutr. 2014, 33, 613–619. [Google Scholar] [CrossRef]
  31. Gaillard, C.; Alix, E.; Sallé, A.; Berrut, G.; Ritz, P. Energy requirements in frail elderly people: A review of the literature. Clin. Nutr. 2007, 26, 16–24. [Google Scholar] [CrossRef]
  32. Ocagli, H.; Lanera, C.; Azzolina, D.; Piras, G.; Soltanmohammadi, R.; Gallipoli, S.; Gafare, C.E.; Cavion, M.; Roccon, D.; Vedovelli, L.; et al. Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population. Nutrients 2021, 13, 458. [Google Scholar] [CrossRef]
  33. Mazzo, R.; Ribeiro, F.B.; Vasques, A.C.J. Accuracy of predictive equations versus indirect calorimetry for the evaluation of energy expenditure in cancer patients with solid tumors—An integrative systematic review study. Clin. Nutr. ESPEN 2020, 35, 12–19. [Google Scholar] [CrossRef]
  34. Purcell, S.A.; Elliott, S.A.; Baracos, V.E.; Chu, Q.S.C.; Sawyer, M.B.; Mourtzakis, M.; Easaw, J.C.; Spratlin, J.L.; Siervo, M.; Prado, C.M. Accuracy of Resting Energy Expenditure Predictive Equations in Patients With Cancer. Nutr. Clin. Pract. 2019, 34, 922–934. [Google Scholar] [CrossRef] [PubMed]
  35. Xu, X.; Yang, Z.; Ma, T.; Li, Z.; Chen, Y.; Zheng, Y.; Dong, J. Novel equation for estimating resting energy expenditure in patients with chronic kidney disease. Am. J. Clin. Nutr. 2021, 113, 1647–1656. [Google Scholar] [CrossRef] [PubMed]
  36. Katano, S.; Yano, T.; Kouzu, H.; Ohori, K.; Shimomura, K.; Honma, S.; Nagaoka, R.; Inoue, T.; Takamura, Y.; Ishigo, T.; et al. Energy intake during hospital stay predicts all-cause mortality after discharge independently of nutritional status in elderly heart failure patients. Clin. Res. Cardiol. 2021, 110, 1202–1220. [Google Scholar] [CrossRef] [PubMed]
  37. Shimizu, A.; Fujishima, I.; Maeda, K.; Wakabayashi, H.; Nishioka, S.; Ohno, T.; Nomoto, A.; Kayashita, J.; Mori, N.; The Japanese Working Group On Sarcopenic Dysphagia. Nutritional Management Enhances the Recovery of Swallowing Ability in Older Patients with Sarcopenic Dysphagia. Nutrients 2021, 13, 596. [Google Scholar] [CrossRef]
  38. Morley, J.E. Anorexia of ageing: A key component in the pathogenesis of both sarcopenia and cachexia. J. Cachexia Sarcopenia Muscle 2017, 8, 523–526. [Google Scholar] [CrossRef]
Nutrients 14 05210 g001 550
Figure 1. Accuracy of resting energy expenditure (REE) prediction equations by age group. (A) Percent bias of resting energy expenditure (REE) prediction equations compared to the measured REE. The mean relative bias (%) was calculated as follows: ((ΔREEPredicted)/REEMeasured) × 100. Error bars represent the 95% confidence interval; (B) Percentage of REE predictions with a mean relative bias (%) within 10% of the measured REE for each prediction equation.
Figure 1. Accuracy of resting energy expenditure (REE) prediction equations by age group. (A) Percent bias of resting energy expenditure (REE) prediction equations compared to the measured REE. The mean relative bias (%) was calculated as follows: ((ΔREEPredicted)/REEMeasured) × 100. Error bars represent the 95% confidence interval; (B) Percentage of REE predictions with a mean relative bias (%) within 10% of the measured REE for each prediction equation.
Nutrients 14 05210 g001
Table
Table 1. Characteristics of equations for the prediction of resting energy expenditure (REE).
Table 1. Characteristics of equations for the prediction of resting energy expenditure (REE).
EquationsSexFormula
Harris-BenedictMale66.4730 + 13.7516 × W (kg) + 5.0033 × H (cm) − 6.7550 × A
Female655.0955 + 9.5634 × W (kg) + 1.8496 × H (cm) − 4.6756 × A
FAO/WHO/UNUMale(36.8 × W (kg) + 4719.5 × (H (cm)/100) − 4481)/4.186
Female(38.5 ×W (kg) + 2665.2 × (H (cm)/100) − 1264)/4.186
GanpuleMale(0.0481 × W (kg) + 0.0234 × H (cm) − 0.0138 × A − 0.4235) × 1000/4.186
Female(0.0481 × W (kg) + 0.0234 × H (cm) − 0.0138 × A − 0.9708) × 1000/4.186
SchofieldMale(0.049 ×W (kg) + 2.459) × 1000/4.186
Female(0.038 ×W (kg) + 2.755) × 1000/4.186
Body weight × 20 W (kg) × 20
Abbreviations: W weight, H Height, A Age.
Table
Table 2. Clinical characteristics of the studied patients.
Table 2. Clinical characteristics of the studied patients.
CharacteristicsOverall (n = 100)Aged 70–89 (n = 51)Aged ≥ 90 (n = 49)
Age, years88.1 ± 6.882.9 ± 5.193.5 ± 3.0
Sex, male n (%)34 (34%)23 (45%)11 (22%)
Height, cm146.0 ± 10.7150.0 ± 11.6142.0 ± 7.9
Weight, kg42.9 ± 9.146.5 ± 8.939.2 ± 7.8
BMI, kg/m220.1 ± 3.520.8 ± 3.619.4 ± 3.3
SMI, kg/m24.67 ± 1.474.99 ± 1.424.34 ± 1.46
CC, cm27.3 ± 3.928.4 ± 4.126.2 ± 3.3
MNA-SF, score7 (6–10)8 (6–10)7 (6–9)
CCI, score2 (1–3)2 (1–3)2 (1–2)
GLIM criteria
Malnutrition, n (%)76 (76%)37 (73%)39 (80%)
Moderate malnutrition, n (%)21 (28%)11 (30%)10 (26%)
Severe malnutrition, n (%)55 (72%)26 (70%)29 (74%)
Data are expressed as numbers of participants (%), means ± standard deviation, or medians (interquartile range). Abbreviations: BMI Body mass index, SMI Skeletal muscle index, CC Calf circumference, MNA-SF Mini Nutritional Assessment Short Form, CCI Charlson comorbidity index, GLIM Global Leadership Initiative on Malnutrition.
Table
Table 3. Evaluation of resting energy expenditure (REE) predictive prediction equations.
Table 3. Evaluation of resting energy expenditure (REE) predictive prediction equations.
REE Predictive Prediction EquationOverall (n = 100)Aged 70–89 (n = 51)Aged ≥ 90 (n = 49)
Mean (95% CI)Mean (95% CI)Mean (95% CI)
Measured REE, kcal/day968.1 (931.0, 1005.3)1041.8 (987.5, 1096.1)891.4 (850.3, 932.6)
Harris-Benedict, kcal/day898.6 (873.1, 924.1) *964.9 (929.7, 1000.1)829.6 (804.3, 854.9)
FAO/WHO/UNU, kcal/day1014.3 (987.1, 1041.6)1074.5 (1034.9, 1114.0)951.8 (923.1, 980.4)
Ganpule, kcal/day830.1 (790.3, 869.9) ***924.2 (870.5, 977.8) **732.3 (687.1, 777.5) ***
Schofield, kcal/day1066.0 (1045.8, 1086.2) ***1105.5 (1077.5, 1133.6)1024.8 (1000.3, 1049.3) ***
Body weight × 20, kcal/day857.7 (821.9, 893.5) ***929.0 (880.0, 978.1) **783.5 (739.7, 827.2) ***
*** p < 0.001, ** p < 0.01, * p < 0.05, Dunnett tests were performed with reference to the measured REE. Abbreviations: CI confidence interval.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Kawase, F.; Masaki, Y.; Ozawa, H.; Imanaka, M.; Sugiyama, A.; Wada, H.; Goto, R.; Kobayashi, S.; Tsukahara, T. Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements. Nutrients 2022, 14, 5210. https://doi.org/10.3390/nu14245210

AMA Style

Kawase F, Masaki Y, Ozawa H, Imanaka M, Sugiyama A, Wada H, Goto R, Kobayashi S, Tsukahara T. Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements. Nutrients. 2022; 14(24):5210. https://doi.org/10.3390/nu14245210

Chicago/Turabian Style

Kawase, Fumiya, Yoshiyuki Masaki, Hiroko Ozawa, Manami Imanaka, Aoi Sugiyama, Hironari Wada, Ryokichi Goto, Shinya Kobayashi, and Takayoshi Tsukahara. 2022. "Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements" Nutrients 14, no. 24: 5210. https://doi.org/10.3390/nu14245210

APA Style

Kawase, F., Masaki, Y., Ozawa, H., Imanaka, M., Sugiyama, A., Wada, H., Goto, R., Kobayashi, S., & Tsukahara, T. (2022). Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements. Nutrients, 14(24), 5210. https://doi.org/10.3390/nu14245210

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop