COPD: Assessment of Energy Needs 2019
Click here to see the explanation of recommendation ratings (Strong, Fair, Weak, Consensus, Insufficient Evidence) and labels (Imperative or Conditional). To see more detail on the evidence from which the following recommendations were drawn, use the hyperlinks in the Supporting Evidence Section below.
COPD: Estimating Resting Metabolic Rate (RMR)
To calculate RMR in adults with COPD, the RDN may use either the World Health Organization [WHO (including height)] equation or the Harris-Benedict equation (HBE). If body composition is known (fat-free mass, body fat), the RDN may use the Westerterp equation. Limited evidence showed that the Westerterp equation has a prediction accuracy rate of 68%, followed by the WHO (including height; 63%) and Harris-Benedict (61%) equations.
COPD: Estimating Total Energy Expenditure (TEE)
To calculate TEE in non-obese adults with COPD, the RDN may use 30kcal per kg body weight (BW) to estimate energy needs. Limited evidence suggests that 30kcal per kg body weight, in non-obese adults with COPD, produced an estimate that was not different from measured values on average, but whose variability was wide, indicating that estimation errors might be common and large.
Risks/Harms of Implementing This Recommendation
Predictive equations may under- or over-estimate energy needs in adults with COPD.
Conditions of Application
- The Westerterp equation requires body composition measurements, fat-free mass (FFM) and body fat (BF), for calculation of RMR. Thus, its utility in clinical care may be limited.
- The COPD: Estimating Total Energy Expenditure (TEE) recommendation applies to non-obese adults with COPD. Clinical judgement should be used in applying the recommendation to individuals with a BMI at or above 30kg/m2.
- The RDN should use clinical judgement in determining the body weight value used in calculations. Use of adjustments to body weight for obesity or volume status were not mentioned anywhere in the available studies.
- For both RMR and TEE predictive equations, errors in individual estimates may be high. Calculation of energy needs (estimation) using a predictive equation is a starting point to determine energy requirements. Changes in body weight should be monitored as an indicator that energy needs should be re-evaluated. Over time, the monitoring of body weight and composition against energy intake is probably the most meaningful expression of energy requirements in COPD. It is important to remember in this type of assessment that if adverse changes in body weight or composition are occurring, equal attention should be paid to the possibility that the patient is not consuming the target intake or that the target intake is not correct.
Potential Costs Associated with Application
Costs may include expenses related to medical nutrition therapy (MNT) visits from an RDN.
A total of five studies were included in the evidence analysis supporting the recommendations:
- Three cross-sectional studies: One positive-quality (Slinde et al, 2011); one neutral-quality (Farooqi et al, 2015); one negative-quality (Ramos et al, 2016)]
- Two diagnostic, validity or reliability studies: Both neutral-quality (Nordenson et al, 2010; Slinde et al, 2008).
Predictive Equations for Estimating RMR: A total of 11 equations were tested for validity in predicting RMR in adults with COPD. Two of these [Moore & Angelillo (MAE); Nordenson] were equations developed specifically for COPD patients, while the other equations were developed for healthy adults [Harris-Benedict (HBE); Mifflin St. Jeor (MSJE); Westerterp; de Oliveira; Owen; four variations of Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University (FAO/WHO/UNU)], which were WHO (including height), WHO (omitting height), Nordic Nutrition Recommendation (NNRE) and the Schofield equation. In two of the studies (Farooqi et al, 2015; Slinde et al, 2011) these equations were evaluated as a starting point for estimating total energy expenditure (TEE).
- Accuracy: Four of the 11 equations for predicting RMR were tested for accuracy, but only in one study (Slinde et al, 2008). Slinde et al, 2008, found that the Westerterp equation yielded an accuracy rate of 68%, followed by the WHO (including height) equation (63%) and HBE (61%). The MAE had the lowest accuracy rate (51%).
- Limit of Agreement (LOA): LOA was reported for nine equations. LOA as a percentage of the mean between measured and predicted RMR were -23% to +18% for the Westerterp equation (Slinde et al, 2008), about -28% to +10% for the MAE (in this case, the negative value is an overestimation) (Slinde et al, 2008), less than 25% for WHO (including height) equation and HBE, -45% to +40% for the de Oliveira equation (Ramos et al, 2016), -53% to +33% for the Owen equation (Ramos et al, 2016), -65% to +13% for the MSJE (Ramos et al, 2016), ±19% for the Nordenson equation (Nordenson et al, 2010) and for WHO (omitting height) equation, +18% (Slinde et al, 2011), to as wide as -66% to +24% (Ramos et al, 2016).
- Bias: Evidence suggests that the only unbiased estimator of RMR in adults with COPD was the de Oliveira equation. Four other equations were probably1 unbiased. These included the HBE and Westerterp equations, which might overestimate RMR and the Nordenson and WHO (including height) equations, which might underestimate RMR. Two equations (Owen and MSJE) were biased toward underestimation of RMR. The remaining four equations were probably1 biased toward overestimation of RMR [MAE, NNRE, Schofield and WHO (omitting height) equations]. Evidence for the WHO (omitting height) equation suggests it might also underestimate RMR in adults with COPD.
1Bias was not reported directly in these studies but is inferred from mean predicted RMR compared to mean measured RMR.
- Thus, the WHO (including height) equation and HBE seem to be equivalent to one another by the parameters of accuracy rate and LOA. If body composition measurements are known, then Westerterp is a better choice for calculation of RMR, because it yields a higher accuracy rate.
Predictive Equations for Estimating TEE: Two studies (Farooqi et al, 2015; Slinde et al, 2011) tested three methods for calculating TEE in adults with COPD. In the first method, a pedometer was used to estimate physical activity to compare against doubly labeled water (DLW) (Farooqi et al, 2015). In this method, a multiplier to RMR was assigned based on the number of steps taken and the multiplier was applied to six RMR equations [WHO (omitting height); Schofield; HBE; MAE; NNRE; Nordenson)]. In the other study, motion and position sensors were used as the criterion method to measure TEE. For prediction purposes, two methods were used: The first was a simple ratio of 30kcal per kg body weight (BW); the second was to compute RMR using the WHO (omitting height) equation and then multiplying by 1.7 to calculate TEE (Slinde et al, 2011).
- Accuracy: Only one of the two studies reported accuracy rate (Farooqi et al, 2015). Accuracy rate for the WHO (omitting height) equation x PAL was of 67%, compared to 56% for the Schofield equation x PAL, 50% for HBE x PAL, MAE x PAL and NNRE x PAL, and 21% for the Nordenson equation x PAL.
- LOA: The only estimation methods for which LOA was computed were 30kcal per kg and WHO (omitting height) x 1.7 (Slinde et al, 2011). LOA for both of these methods was 956kcal per day (-48; +48% of the mean between predicted and measured TEE)
- Bias: The WHO (omitting height) x PAL and the MAE x PAL were probably2 unbiased, while the Schofield x PAL, HBE x PAL, NNRE x PAL and Nordenson x PAL equations were probably2 biased (toward underestimation) (Farooqi et al, 2016). An estimate of 30kcal per kg BW yielded a mean difference from measured TEE of 71kcal per day and so was probably unbiased2, whereas another predictive method of WHO (omitting height) x 1.7 probably2 was biased toward overestimation, based on a mean difference from measured TEE of 319kcal per day (Slinde et al, 2011).
2Bias was not reported directly in these studies, but is inferred from mean predicted TEE compared to mean measured TEE.
Recommendation Strength Rationale
- Conclusion statement supporting the recommendations is Grade III (Limited/Weak)
- None of the studies evaluating TEE methods were tested more than once
- Synthesis of results for RMR and TEE was challenging because large gaps exist in the available evidence (small numbers of studies with small sample sizes, inconsistency in the types of statistical treatments from study to study making data difficult to aggregate).
- Risks/Harms of Implementing This Recommendation
The recommendations were created from the evidence analysis on the following questions. To see detail of the evidence analysis, click the blue hyperlinks below (recommendations rated consensus will not have supporting evidence linked).
Farooqi N, Slinde F, Carlsson M, Håglin L, Sandström T. Predicting energy requirement with pedometer-determined physical-activity level in women with chronic obstructive pulmonary disease. International Journal of Chronic Obstructive Pulmonary Disease 2015; 10:1129-37
Nordenson A, Grönberg A, Hulthén L, Larsson S, Slinde F. A validated disease specific prediction equation for resting metabolic rate in underweight patients with COPD. International Journal of Chronic Obstructive Pulmonary Disease 2010; 5:271-6
Ramos F, Rossato L, Ramires B, Pimentel G, Venâncio L, Orsatti F, de Oliveira E. Comparison of predictive equations of resting energy expenditure in older adults with chronic obstructive pulmonary disease. Revista Portuguesa de Pneumologia 2016; 23:40-42
Slinde F, Svensson A, Gronberg AM, Nordenson N, Hulthen L, Larsson SC. Reproducibility of indirect calorimetry in underweight patients with chronic obstructive pulmonary disease. European e-Journal of Clinical Nutrition and Metabolism 2008; 3:40-45
Slinde F, Gronberg A, Svantesson U, Hulthen L, Larsson S. Energy expenditure in chronic obstructive pulmonary disease-evaluation of simple measures. European Journal of Clinical Nutrition 2011; 65:1309-13
References not graded in Academy of Nutrition and Dietetics Evidence Analysis Process