Resting energy expenditure in chronic cardiac failure

Riley, M.; Elborn, J. Stuart; McKane, W R; Bell, N.; Stanford, C. F.; Nicholls, D. P.

doi:10.1042/cs0800633

Cited by 75 publications

(47 citation statements)

References 16 publications

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“…Recent reports from our laboratory (Poehlman et al, 1994;Obisesan et al, 1996) and others (Riley et al, 1991) showed elevations in resting energy expenditure in stabilized heart failure patients. Additionally, we reported that resting energy needs increased with symptom severity (Obisesan et al, 1996).…”

Section: Discussionmentioning

confidence: 84%

Prediction of resting energy needs in older men with heart failure

1997

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Objectives: Patients with congestive heart failure are often undernourished. The measurement of resting energy expenditure has served as the basis upon which estimates of daily caloric needs have been developed. Resting energy needs, however, in heart failure patients are unknown. We have developed a new equation to predict resting energy needs in heart failure patients that takes into account easily measured clinical variables and symptom severity. Design: Observational. Setting: Baltimore VA Medical Center. Subjects: Forty male patients with heart failure aged, 57±85 y; (27 Class II; 13 Class IV). Measurements: Resting metabolic rate was measured by indirect calorimetry, fat-free mass and fat mass by dual energy X-ray absorptiometry, peak VO 2 by a treadmill test. Symptom severity was measured by the New York Heart Association classi®cation. Ejection fraction, plasma albumin and plasma glucose were also assessed. Results: Stepwise regression analysis showed that body weight, fasting glucose, plasma albumin and New York Heart Association classi®cation accounted for 83% of the variation in resting energy needs. The regression equation had a root mean square error of 130 kcal (544 kJ) per day. The equation is: RMR (kcal/d) 12.2 (wt, kg) 1.6 (glucose, gm/dl) 103 (NYHA; III, IV) 7 144 (albumin, mg/dl) 755. Moreover, prediction equations based on observations in healthy individuals signi®cantly underestimated resting energy needs in heart failure patients. Conclusion: We offer a new equation to predict resting energy needs in heart failure patients based upon readily available clinical measurements. Further studies are needed to cross-validate our equation.

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Section: Discussionmentioning

confidence: 84%

Prediction of resting energy needs in older men with heart failure

1997

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“…The determination of REE is important for the establishment of energy recommendations and maintenance of nutritional balance of the individuals . Elevated REE has been pointed out as a contributory factor for cachexia in several disease conditions (Riley et al, 1991;Falconer et al, 1994) and an association with mortality has been suggested (Wang et al, 2004). However, little is known about REE and its determinants in chronic kidney disease (CKD) patients undergoing hemodialysis (HD).…”

Section: Introductionmentioning

confidence: 99%

Resting energy expenditure and its determinants in hemodialysis patients

et al. 2006

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Objective: Chronic kidney disease is associated with several metabolic disturbances that can affect energy metabolism. As resting energy expenditure (REE) is scarcely investigated in patients on hemodialysis (HD) therapy, we aimed to evaluate the REE and its determinants in HD patients. Design: Cross-sectional study. Setting: Dialysis Unit of the Nephrology Division, Federal University of São Paulo, Brazil. Subjects: The study included 55 patients (28 male, 41.4712.6 years old) undergoing HD therapy thrice weekly for at least 2 months, and 55 healthy individuals pair matched for age and gender. Subjects underwent fasting blood tests, as well as nutritional assessment, and the REE was assessed by indirect calorimetry. Results: REE of HD patients was similar to that of pair-matched controls (13797272 and 14407259 kcal/day, respectively), even when adjusted for fat-free mass (P ¼ 0.24). REE of HD patients correlated positively with fat-free mass (r ¼ 0.74; Po0.001) and body mass index (r ¼ 0.37; Po0.01), and negatively with dialysis adequacy (r ¼ À0.46; Po0.001). No significant univariate correlation was found between REE and age, dialysis vintage, serum creatinine, urea, albumin, bicarbonate, parathyroid hormone (PTH) or high-sensitivity C-reactive protein (CRP). In the multiple linear regression analysis, using REE as dependent variable, the final model showed that besides the well-recognized determinants of REE such as fat-free mass and age, PTH and CRP were the independent determinants of REE in HD patients (R 2 ¼ 0.64).

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“…Peak oxygen consumption (peak VO 2 ) was defined as the highest VO 2 value averaged over 15 seconds during the final minute of exercise. [17][18][19][20] Statistics: All date are were expressed as the mean±standard deviation (SD). A value of p<0.05 was considered statistically significant.…”

Section: )mentioning

confidence: 99%

<b>Plasma Natriuretic Peptide Levels and Daily Physical Activity in Patients with Pacemaker Implantation</b>

Seino

Ogura

et al. 2001

Jpn Heart J

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SUMMARYTo determine whether plasma ANP and/or BNP levels can be used to detect limitations in daily physical activity after pacemaker implantation, we measured plasma ANP and BNP levels at a pacemaker follow-up clinic in 56 patients (62±15 yrs, 2AAI, 9VVI, 34DDD, 7VDD and 4 rate-responsive modes). daily physical activity evaluated by a specific activity scale questionnaire (METs) and VO 2 max obtained by expired gas analysis during ergometer exercise.A very clouse correlation (n=6, r=0.89, p<0.05) was obseved between. ANP in patients with daily physical activity class III (2-4 METs, n=21) was significantly higher than class II (5-6 METs, n=23, p<0.01) and class I (>7 METs, n=8, p<0.01), while BNP in class III patients was significantly higher than in class II (p<0.0001) and class I (p<0.0001) patients. Significant correlations between daily physical activity and BNP (r=-0.64, p<0.0001) and ANP (r=-0.43, p<0.001) were observed. Physiological pacing mode did not necessarily offer a better profile for BNP levels compared with non-physiological pacing modes. Patients with ventricular pacing (wide QRS: VDD, RR-VVI and VVI) showed significantly high ANP (p<0.01) and BNP (p<0.01) levels compared with those in patients with atrial pacing (narrow QRS: AAI and RR-AAI). During exercise, plasma catecholamines and ANP levels were significantly elevated, however, BNP levels, which were already elevated at rest, did not change significantly, and reflected a limitation of daily physical activity. The present study revealed that 37.5% of the patients displayed an elevation in BNP and this was judged to be a limitation of physical activity class III being equivalent to NYHA II or more. Elevated resting BNP levels reflected a limitation in daily physical activity in these patients. These findings suggested a third condition for physiological pacing-synchronization of ventricular contraction (narrow QRS pacing) -in addition to the two conventional conditions of atrioventricular synchrony and rate-responsiveness. (Jpn Heart J 2001; 42: 471-482) Key words: Physiological pacing, Brain natriuretic peptide, Congestive heart failure, Physical activity, Dyssynchronization WE have reported that the electrophysiological consequences of the heart following pacemaker implantation are closely related to the cardiodynamic and neuFrom

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Resting energy expenditure in chronic cardiac failure

Cited by 75 publications

References 16 publications

Prediction of resting energy needs in older men with heart failure

Prediction of resting energy needs in older men with heart failure

Resting energy expenditure and its determinants in hemodialysis patients

<b>Plasma Natriuretic Peptide Levels and Daily Physical Activity in Patients with Pacemaker Implantation</b>

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