Original Article| Volume 24, ISSUE 8, P1879-1885, August 2015

Comparisons of Predictive Equations for Resting Energy Expenditure in Patients with Cerebral Infarct during Acute Care


      Estimation of resting energy expenditure (REE) is essential in planning nutrition support. Several equations are used for this estimation in the clinical setting. The purpose of this study was to compare the predictive accuracy of existing equations for REE in patients with cerebral infarct during acute care.


      We assessed the Harris–Benedict, Mifflin, Owen, Japanese simplified, Wang, and Cunningham equations. The Owen and Japanese simplified equations use sex and weight as explanatory variables, the Harris–Benedict and Mifflin equations include sex, weight, age, and height, and the Wang and Cunningham equations use fat-free mass (FFM) measured using bioelectrical impedance technology. Actual REE values were measured by indirect calorimetry on days 2 and 7 and were then averaged. Applying analysis of variance, predictive accuracy was assessed by comparing the predicted and actual values.


      A total of 30 patients were analyzed. Actual REE values ranged from 796 to 1637 kcal (mean, 1109). The standard deviation of these values was the smallest with the Harris–Benedict equation (99), followed by the Cunningham (165), and Wang (181) equations. The Mifflin equation underestimated REE in females, whereas the Owen and Japanese simplified equations tended to overestimate it.


      Based on our results, the Harris–Benedict equation provides the most accurate prediction of REE. In addition, the Cunningham and Wang equations may be useful in long-term care settings involving patients at risk of malnutrition resulting in uneven loss of FFM relative to weight.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Journal of Stroke and Cerebrovascular Diseases
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Davalos A.
        • Ricart W.
        • Gonzalez-Huix F.
        • et al.
        Effect of malnutrition after acute stroke on clinical outcome.
        Stroke. 1996; 27: 1028-1032
        • Gariballa S.E.
        • Parker S.G.
        • Taub N.
        • et al.
        Influence of nutritional status on clinical outcome after acute stroke.
        Am J Clin Nutr. 1998; 68: 275-281
        • Dennis M.
        Nutrition after stroke.
        Br Med Bull. 2000; 56: 466-475
        • Dennis M.S.
        • Lewis S.C.
        • Warlow C.
        Effect of timing and method of enteral tube feeding for dysphagic stroke patients (FOOD): a multicentre randomised controlled trial.
        Lancet. 2005; 365: 764-772
        • Kawakami M.
        • Liu M.
        • Wada A.
        • et al.
        Resting energy expenditure in patients with stroke during the subacute phases - relationships with stroke types, location, severity of paresis, and activities of daily living.
        Cerebrovasc Dis. 2015; 39: 170-175
        • Jequier E.
        • Acheson K.
        • Schutz Y.
        Assessment of energy expenditure and fuel utilization in man.
        Annu Rev Nutr. 1987; 7: 187-208
        • Ferrannini E.
        The theoretical bases of indirect calorimetry: a review.
        Metabolism. 1988; 37: 287-301
        • Haugen H.A.
        • Chan L.N.
        • Li F.
        Indirect calorimetry: a practical guide for clinicians.
        Nutr Clin Pract. 2007; 22: 377-388
        • Guttormsen A.B.
        • Pichard C.
        Determining energy requirements in the ICU.
        Curr Opin Clin Nutr Metab Care. 2014; 17: 171-176
        • Frankenfield D.C.
        • Ashcraft C.M.
        Estimating energy needs in nutrition support patients.
        JPEN J Parenter Enteral Nutr. 2011; 35: 563-570
        • Ohtani Y.
        A device of calculating board for simple calculation of individual energy allowance.
        Jpn J Nutr. 1982; 40 ([in Japanese]): 275-279
        • Roza A.M.
        • Shizgal H.M.
        The Harris Benedict equation reevaluated: resting energy requirements and the body cell mass.
        Am J Clin Nutr. 1984; 40: 168-182
        • Owen O.E.
        • Holup J.L.
        • D'Alessio D.A.
        • et al.
        A reappraisal of the caloric requirements of men.
        Am J Clin Nutr. 1987; 46: 875-885
        • Mifflin M.D.
        • St Jeor S.T.
        • Hill L.A.
        • et al.
        A new predictive equation for resting energy expenditure in healthy individuals.
        Am J Clin Nutr. 1990; 51: 241-247
        • Cunningham J.J.
        Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation.
        Am J Clin Nutr. 1991; 54: 963-969
        • Wang Z.
        • Heshka S.
        • Gallagher D.
        • et al.
        Resting energy expenditure-fat-free mass relationship: new insights provided by body composition modeling.
        Am J Physiol Endocrinol Metab. 2000; 279: E539-545
        • Takizawa S.
        • Shibata T.
        • Takagi S.
        • et al.
        Seasonal variation of stroke incidence in Japan for 35631 stroke patients in the Japanese Standard Stroke Registry, 1998-2007.
        J Stroke Cerebrovasc Dis. 2013; 22: 36-41
        • Koyama T.
        • Marumoto K.
        • Miyake H.
        • et al.
        Relationship between diffusion tensor fractional anisotropy and long-term motor outcome in patients with hemiparesis after middle cerebral artery infarction.
        J Stroke Cerebrovasc Dis. 2014; 23: 2397-2404
        • Lyden P.
        • Brott T.
        • Tilley B.
        • et al.
        Improved reliability of the NIH Stroke Scale using video training. NINDS TPA Stroke Study Group.
        Stroke. 1994; 25: 2220-2226
        • van Swieten J.C.
        • Koudstaal P.J.
        • Visser M.C.
        • et al.
        Interobserver agreement for the assessment of handicap in stroke patients.
        Stroke. 1988; 19: 604-607
        • Shinohara Y.
        • Yanagihara T.
        • Abe K.
        • et al.
        II. Cerebral infarction/transient ischemic attack (TIA).
        J Stroke Cerebrovasc Dis. 2011; 20: S31-73
        • Nieman D.C.
        • Austin M.D.
        • Benezra L.
        • et al.
        Validation of Cosmed's FitMate in measuring oxygen consumption and estimating resting metabolic rate.
        Res Sports Med. 2006; 14: 89-96
        • Vandarakis D.
        • Salacinski A.J.
        • Broeder C.E.
        A comparison of COSMED metabolic systems for the determination of resting metabolic rate.
        Res Sports Med. 2013; 21: 187-194
        • Brisswalter J.
        • Tartaruga M.P.
        Comparison of COSMED'S FitMate and K4b2 metabolic systems reliability during graded cycling exercise.
        Scand J Clin Lab Invest. 2014; 74: 722-724
        • Lupinsky L.
        • Singer P.
        • Theilla M.
        • et al.
        Comparison between two metabolic monitors in the measurement of resting energy expenditure and oxygen consumption in diabetic and non-diabetic ambulatory and hospitalized patients.
        Nutrition. 2015; 31: 176-179
        • Cha K.
        • Chertow G.M.
        • Gonzalez J.
        • et al.
        Multifrequency bioelectrical impedance estimates the distribution of body water.
        J Appl Physiol (1985). 1995; 79: 1316-1319
        • Malavolti M.
        • Mussi C.
        • Poli M.
        • et al.
        Cross-calibration of eight-polar bioelectrical impedance analysis versus dual-energy X-ray absorptiometry for the assessment of total and appendicular body composition in healthy subjects aged 21-82 years.
        Ann Hum Biol. 2003; 30: 380-391
        • Sartorio A.
        • Malavolti M.
        • Agosti F.
        • et al.
        Body water distribution in severe obesity and its assessment from eight-polar bioelectrical impedance analysis.
        Eur J Clin Nutr. 2005; 59: 155-160
        • Jensky-Squires N.E.
        • Dieli-Conwright C.M.
        • Rossuello A.
        • et al.
        Validity and reliability of body composition analysers in children and adults.
        Br J Nutr. 2008; 100: 859-865
        • Foley N.C.
        • Martin R.E.
        • Salter K.L.
        • et al.
        A review of the relationship between dysphagia and malnutrition following stroke.
        J Rehabil Med. 2009; 41: 707-713
        • Corrigan M.L.
        • Escuro A.A.
        • Celestin J.
        • et al.
        Nutrition in the stroke patient.
        Nutr Clin Pract. 2011; 26: 242-252
        • Stewart C.L.
        • Goody C.M.
        • Branson R.
        Comparison of two systems of measuring energy expenditure.
        JPEN J Parenter Enteral Nutr. 2005; 29: 212-217
        • Schadewaldt P.
        • Nowotny B.
        • Strassburger K.
        • et al.
        Indirect calorimetry in humans: a postcalorimetric evaluation procedure for correction of metabolic monitor variability.
        Am J Clin Nutr. 2013; 97: 763-773
        • Finestone H.M.
        • Greene-Finestone L.S.
        • Foley N.C.
        • et al.
        Measuring longitudinally the metabolic demands of stroke patients: resting energy expenditure is not elevated.
        Stroke. 2003; 34: 502-507
        • Frankenfield D.C.
        • Ashcraft C.M.
        Description and prediction of resting metabolic rate after stroke and traumatic brain injury.
        Nutrition. 2012; 28: 906-911