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Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
A retrospective study of resting energy expenditure in children hospitalized with different nutritional status
Wen-Li Yang1, Lu-Lu Xia1, Dong-Dan Li1
1Department of Clinical Nutrition, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Insights
Resting energy expenditure (REE) in children varies by nutritional status. Predictive equations often deviate from measured values, necessitating careful selection based on individual nutritional status.
Area of Science:
- Pediatric critical care
- Nutritional science
- Metabolic research
Background:
- Resting energy expenditure (REE) is crucial for metabolic assessment in hospitalized children.
- Nutritional status significantly impacts energy requirements.
- Accurate REE assessment is vital for effective clinical management.
Purpose of the Study:
- To investigate resting energy expenditure (REE) in hospitalized children across different nutritional statuses.
- To compare measured REE (mREE) with predicted REE (pREE) using five common energy equations.
- To evaluate the accuracy of predictive equations in pediatric populations with malnutrition and obesity.
Main Methods:
- Retrospective analysis of 109 pediatric patients undergoing indirect calorimetry (IC).
- Patients categorized into mild, moderate, severe malnutrition, and obesity groups.
- Comparison of mREE from IC with pREE from five equations using t-tests, Pearson correlation, and Bland-Altman analysis.
Main Results:
- No significant difference in mREE among mild, moderate, and severe malnutrition groups; all differed from the obesity group.
- All equations showed significant correlation with mREE, but predictive accuracy was limited (Schofield: 47.7%, Liu in severe malnutrition: 34.4%).
- Bland-Altman analysis revealed deviations greater than ±10% between predicted and measured REE across all nutritional statuses.
Conclusions:
- Significant differences in REE exist among children with varying nutritional statuses.
- Current predictive energy equations demonstrate substantial deviation from IC-measured REE.
- Clinical practice requires careful selection of predictive REE equations based on individual pediatric nutritional status when IC is unavailable.
Background:
Resting energy expenditure (REE) refers to the energy consumption of the body in a resting state without skeletal muscle activity. This study aimed to examine the REE among children hospitalized with varying nutritional status.
Methods:
This was a retrospective study. We enrolled 109 pediatric cases that underwent indirect calorimetry (IC) and divided into four groups: mild malnutrition group (15 cases), moderate malnutrition group (30 cases), severe malnutrition group (32 cases), and obesity group (32 cases). We compared and analyzed the measured REE (mREE) using IC with the predicted REE (pREE) using five energy equations. The paired t-test was used to compare the results of two samples. Pearson analysis was used to assess the correlation between two values. The agreement analysis was performed using the Bland-Altman method.
Results:
There was no significant difference in mREE between the mild, moderate, and severe malnutrition groups, but each differed significantly from the obesity group. All populations exhibited significant correlation between the mREEs and all five energy equations, and the equation with the highest predictive accuracy was the Schofield equation, which achieved an accuracy of 47.7%. In subgroup analysis, there was no significant difference between mREE and pREE for each of the five equations in the mild, moderate malnutrition groups. Only the prediction result of the Liu equation was not significantly different from the mREE in the severe malnutrition group. The prediction accuracy of the Liu equation was relatively the highest (34.4%). However, in the obese group, there were significant differences in pREE and mREE between the Liu equation and Mifflin equation. Under different nutritional statuses, the results of the Bland-Altman analysis suggested that deviation values between REEs predicted by each equation and mREE were greater than ±10%.
Conclusions:
There were differences in REE among children with different nutritional status. The results obtained from the five predictive energy equations deviated from the IC results. When REE cannot be measured by IC, it is essential to choose an appropriate predictive energy equation based on the nutritional status of the individual.
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