Study designCross-sectional study.ObjectivesThe aims of this study were (1) to validate the two recently developed SCI-specific REE equations; (2) to develop new prediction equations to predict REE in a general population with SCI.SettingUniversity, the Netherlands.MethodsForty-eight community-dwelling men and women with SCI were recruited (age: 18–75 years, time since injury: ≥12 months). Body composition was measured by dual-energy X-ray absorptiometry (DXA), single-frequency bioelectrical impedance analysis (SF-BIA) and skinfold thickness. REE was measured by indirect calorimetry. Personal and lesion characteristics were collected. SCI-specific REE equations by Chun et al. [1] and by Nightingale and Gorgey [2] were validated. New equations for predicting REE were developed using multivariate regression analysis.ResultsPrediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE (Chun et al.: −11%; Nightingale and Gorgey: −11%). New equations were developed for predicting REE in the general population of people with SCI using FFM measured by SF-BIA and Goosey-Tolfrey et al. skinfold equation (R2 = 0.45–0.47; SEE = 200 kcal/day). The new equations showed proportional bias (p < 0.001) and wide limits of agreement (LoA, ±23%).ConclusionsPrediction equations by Chun et al. [1] and by Nightingale and Gorgey [2] significantly underestimated REE and showed large individual variations in a general population with SCI. The newly developed REE equations showed proportional bias and a wide LoA (±23%) which limit the predictive power and accuracy to predict REE in the general population with SCI. Alternative methods for measuring REE need to be investigated.
BACKGROUND: When indirect calorimetry is not available, predictive equations are used to estimate resing energy expenditure (REE). There is no consensus about which equation to use in hospitalized patients. The objective of this study is to examine the validity of REE predictive equations for underweight, normal weight, overweight, and obese inpatients and outpatients by comparison with indirect calorimetry.METHODS: Equations were included when based on weight, height, age, and/or gender. REE was measured with indirect calorimetry. A prediction between 90 and 110% of the measured REE was considered accurate. The bias and root-mean-square error (RMSE) were used to evaluate how well the equations fitted the REE measurement. Subgroup analysis was performed for BMI. A new equation was developed based on regression analysis and tested.RESULTS: 513 general hospital patients were included, (253 F, 260 M), 237 inpatients and 276 outpatients. Fifteen predictive equations were used. The most used fixed factors (25 kcal/kg/day, 30 kcal/kg/day and 2000 kcal for female and 2500 kcal for male) were added. The percentage of accurate predicted REE was low in all equations, ranging from 8 to 49%. Overall the new equation performed equal to the best performing Korth equation and slightly better than the well-known WHO equation based on weight and height (49% vs 45% accurate). Categorized by BMI subgroups, the new equation, Korth and the WHO equation based on weight and height performed best in all categories except from the obese subgroup. The original Harris and Benedict (HB) equation was best for obese patients.CONCLUSIONS: REE predictive equations are only accurate in about half the patients. The WHO equation is advised up to BMI 30, and HB equation is advised for obese (over BMI 30). Measuring REE with indirect calorimetry is preferred, and should be used when available and feasible in order to optimize nutritional support in hospital inpatients and outpatients with different degrees of malnutrition.
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Rationale: Predictive equations for resting energy expenditure (REE) are used in the treatment of overweight and obesity, but the validity of these equations in overweight older adults is unknown. This study evaluates which predictive REE equation is the best alternative to indirect calorimetry in overweight older adults with and without diabetes. Methods: In total 273 adults aged ≥55 years with a BMI of ≥25 kg/m2 were included. REE (by indirect calorimetry), body weight, body height, age, gender, and fat-free and fat mass (from air-displacement plethysmography) were measured. The measured REE was used as a reference and compared with 28 existing REE equations. The accuracy of the equations was evaluated by the percentage accurate predictions (within 10% of REE measured), the root mean squared error (RMSE), and the mean percentage difference (bias) between predicted and measured REE. Subgroup analyses were performed for type 2 diabetics (T2D) and non-T2D. Results: Mean age was 64 ± (SD 6) years, 42% had T2D (n = 116), and mean BMI was 32.8 ± (SD 4.5) with range 25–54 kg/m2. The adjusted Harris & Benedict (1984) provided the highest percentage accurate predictions in all adults (70%) and in T2D (74%), and second best in non-T2D (67%). RMSE was 184, 175 and 191 kcal/day, and bias −1.2%, −1.5% and −1.0% for all adults, T2D and non-T2D, respectively. Conclusion: For Dutch overweight older adults with and without diabetes the adjusted Harris–Benedict (1984) predictive equation for REE seems to be the best alternative to indirect calorimetry.
