957 resultados para indirect calorimetry


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Background & aims: Little is known about energy requirements in brain injured (TBI) patients, despite evidence suggesting adequate nutritional support can improve clinical outcomes. The study aim was to compare predicted energy requirements with measured resting energy expenditure (REE) values, in patients recovering from TBI.

Methods: Indirect calorimetry (IC) was used to measure REE in 45 patients with TBI. Predicted energy requirements were determined using FAO/WHO/UNU and Harris–Benedict (HB) equations. Bland– Altman and regression analysis were used for analysis.

Results: One-hundred and sixty-seven successful measurements were recorded in patients with TBI. At an individual level, both equations predicted REE poorly. The mean of the differences of standardised areas of measured REE and FAO/WHO/UNU was near zero (9 kcal) but the variation in both directions was substantial (range 591 to þ573 kcal). Similarly, the differences of areas of measured REE and HB demonstrated a mean of 1.9 kcal and range 568 to þ571 kcal. Glasgow coma score, patient status, weight and body temperature were signi?cant predictors of measured REE (p < 0.001; R2= 0.47).

Conclusions: Clinical equations are poor predictors of measured REE in patients with TBI. The variability in REE is substantial. Clinicians should be aware of the limitations of prediction equations when estimating energy requirements in TBI patients.

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There is currently no universally recommended and accepted method of data processing within the science of indirect calorimetry for either mixing chamber or breath-by-breath systems of expired gas analysis. Exercise physiologists were first surveyed to determine methods used to process oxygen consumption ([OV0312]O 2) data, and current attitudes to data processing within the science of indirect calorimetry. Breath-by-breath datasets obtained from indirect calorimetry during incremental exercise were then used to demonstrate the consequences of commonly used time, breath and digital filter post-acquisition data processing strategies. Assessment of the variability in breath-by-breath data was determined using multiple regression based on the independent variables ventilation (VE), and the expired gas fractions for oxygen and carbon dioxide, FEO 2 and FECO2, respectively. Based on the results of explanation of variance of the breath-by-breath [OV0312]O2 data, methods of processing to remove variability were proposed for time-averaged, breath-averaged and digital filter applications. Among exercise physiologists, the strategy used to remove the variability in sequential [OV0312]O2 measurements varied widely, and consisted of time averages (30 sec [38%], 60 sec [18%], 20 sec [11%], 15 sec [8%]), a moving average of five to 11 breaths (10%), and the middle five of seven breaths (7%). Most respondents indicated that they used multiple criteria to establish maximum [OV0312]O 2 ([OV0312]O2max) including: the attainment of age-predicted maximum heart rate (HRmax) [53%], respiratory exchange ratio (RER) >1.10 (49%) or RER >1.15 (27%) and a rating of perceived exertion (RPE) of >17, 18 or 19 (20%). The reasons stated for these strategies included their own beliefs (32%), what they were taught (26%), what they read in research articles (22%), tradition (13%) and the influence of their colleagues (7%). The combination of VE, FEO 2 and FECO2 removed 96-98% of [OV0312]O2 breath-by-breath variability in incremental and steady-state exercise [OV0312]O2 data sets, respectively. Correction of residual error in [OV0312]O2 datasets to 10% of the raw variability results from application of a 30-second time average, 15-breath running average, or a 0.04 Hz low cut-off digital filter. Thus, we recommend that once these data processing strategies are used, the peak or maximal value becomes the highest processed datapoint. Exercise physiologists need to agree on, and continually refine through empirical research, a consistent process for analysing data from indirect calorimetry.

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Objective: This study aimed to determine the energy expenditure (EE) in terms of caloric cost and metabolic equivalents (METs) of two sessions of an exercise protocol. Methods: Fifteen subjects (51.0 ± 5.5years) performed the exercise sessions (80min), which were composed by (warming, walking and flexibility exercises; Session A) and (warming, walking and local muscular endurance exercises; Session B). Heart hate (HR) was measured during each part of the sessions. In laboratory environment, maximal oxygen consumption (VO2max) and oxygen uptake in rest and exercise conditions (using mean HR obtained in classes) were measured on different days, using indirect calorimetry. Exercise METs were obtained by dividing VO2 in exercise (mL.kg-1.min-1) by VO2 in rest (mL.kg-1.min-1). The EE of the exercises was calculated by the formula: MET x Weight(kg) x Time(min)/60. The results were analyzed by ANOVA with Tuckey post hoc test (p < 0.05). Results: One MET for this group was 2.7 ± 0.1mL.kg-1.min-1. The mean METs of exercises were 4,7 ± 0,8 (warming), 5,8 ± 0,9 (walking) and 3,6 ± 0,7 (flexibility) on session A, and 4,6 ± 1,2 (warming), 5,6 ± 1,0 (walking) and 4.8 ± 1,0 (local muscular endurance exercises) on Session B. The training sessions showed similar energy cost (A: 398 ± 86.72 kcal and B: 404 ± 38.85 kcal; p > 0,05). None of activities were classified into vigorous intensity (> 7 METs). There were no differences on VO2 between walking (15,6 ± 2,8 or 15,4 ± 2,6 mL.kg-1.min-1) and local muscular endurance exercises (13,2 ± 2,9 mL.kg-1.min-1), although both were higher (p > 0.05) than flexibility exercises (10.1 ± 2.2 mL.kg-1.min-1). Conclusion: The proposed protocol achieves the physical activity needed by healthy adults to improve and maintain health, by their structure, moderate intensity, duration, frequency and caloric expenditure.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The ActiGraph accelerometer is commonly used to measure physical activity in children. Count cut-off points are needed when using accelerometer data to determine the time a person spent in moderate or vigorous physical activity. For the GT3X accelerometer no cut-off points for young children have been published yet. The aim of the current study was thus to develop and validate count cut-off points for young children. Thirty-two children aged 5 to 9 years performed four locomotor and four play activities. Activity classification into the light-, moderate- or vigorous-intensity category was based on energy expenditure measurements with indirect calorimetry. Vertical axis as well as vector magnitude cut-off points were determined through receiver operating characteristic curve analyses with the data of two thirds of the study group and validated with the data of the remaining third. The vertical axis cut-off points were 133 counts per 5 sec for moderate to vigorous physical activity (MVPA), 193 counts for vigorous activity (VPA) corresponding to a metabolic threshold of 5 MET and 233 for VPA corresponding to 6 MET. The vector magnitude cut-off points were 246 counts per 5 sec for MVPA, 316 counts for VPA - 5 MET and 381 counts for VPA - 6 MET. When validated, the current cut-off points generally showed high recognition rates for each category, high sensitivity and specificity values and moderate agreement in terms of the Kappa statistic. These results were similar for vertical axis and vector magnitude cut-off points. The current cut-off points adequately reflect MVPA and VPA in young children. Cut-off points based on vector magnitude counts did not appear to reflect the intensity categories better than cut-off points based on vertical axis counts alone.

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Achievement of steady state during indirect calorimetry measurements of resting energy expenditure (REE) is necessary to reduce error and ensure accuracy in the measurement. Steady state is often defined as 5 consecutive min (5-min SS) during which oxygen consumption and carbon dioxide production vary by +/-10%. These criteria, however, are stringent and often difficult to satisfy. This study aimed to assess whether reducing the time period for steady state (4-min SS or 3-min SS) produced measurements of REE that were significantly different from 5-min SS. REE was measured with the use of open-circuit indirect calorimetry in 39 subjects, of whom only 21 (54%) met the 5-min SS criteria. In these 21 subjects, median biases in REE between 5-min SS and 4-min SS and between 5-min SS and 3-min SS were 0.1 and 0.01%, respectively. For individuals, 4-min SS measured REE within a clinically acceptable range of +/-2% of 5-min SS, whereas 3-min SS measured REE within a range of -2-3% of 5-min SS. Harris-Benedict prediction equations estimated REE for individuals within +/-20-30% of 5-min SS. Reducing the time period of steady state to 4 min produced measurements of REE for individuals that were within clinically acceptable, predetermined limits. The limits of agreement for 3-min SS fell outside the predefined limits of +/-2%; however, both 4-min SS and 3-min SS criteria greatly increased the proportion of subjects who satisfied steady state within smaller limits than would be achieved if relying on prediction equations.

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Mitochondrial diseases (MD) are the most frequent inborn errors of metabolism. In affected tissues, MD can alter cellular oxygen consumption rate leading to potential decreases in whole-body resting energy expenditure (REE), but data on pediatric children are absent. We determined, using indirect calorimetry (IC), whole-body oxygen consumption (VO2), carbon dioxide production (VCO2), respiratory quotient (RQ) and REE in pediatric patients with MD and healthy controls. Another goal was to assess the accuracy of available predictive equations for REE estimation in this patient population. IC data were obtained under fasting and resting conditions in 20 MD patients and 27 age and gender-matched healthy peers. We determined the agreement between REE measured with IC and REE estimated with Schofield weight and FAO/WHO/UNU equations. Mean values of VO2, VCO2 (mL·min-1·kg-1) or RQ did not differ significantly between patients and controls (P = 0.085, P = 0.055 and P = 0.626 respectively). Accordingly, no significant differences (P = 0.086) were found for REE (kcal·day-1 kg-1) either. On the other hand, although we found no significant differences between IC-measured REE and Schofield or FAO/WHO/UNU-estimated REE, Bland-Altman analysis revealed wide limits of agreement and there were some important individual differences between IC and equation-derived REE. VO2, VCO2, RQ and REE are not significantly altered in pediatric patients with MD compared with healthy controls. The energy demands of pediatric patients with MD should be determined based on IC data in order to provide the best possible personalized nutritional management for these children.

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OBJECTIVE: To compare, in patients with cancer and in healthy subjects, measured resting energy expenditure (REE) from traditional indirect calorimetry to a new portable device (MedGem) and predicted REE. DESIGN: Cross-sectional clinical validation study. SETTING: Private radiation oncology centre, Brisbane, Australia. SUBJECTS: Cancer patients (n = 18) and healthy subjects (n = 17) aged 37-86 y, with body mass indices ranging from 18 to 42 kg/m(2). INTERVENTIONS: Oxygen consumption (VO(2)) and REE were measured by VMax229 (VM) and MedGem (MG) indirect calorimeters in random order after a 12-h fast and 30-min rest. REE was also calculated from the MG without adjustment for nitrogen excretion (MGN) and estimated from Harris-Benedict prediction equations. Data were analysed using the Bland and Altman approach, based on a clinically acceptable difference between methods of 5%. RESULTS: The mean bias (MGN-VM) was 10% and limits of agreement were -42 to 21% for cancer patients; mean bias -5% with limits of -45 to 35% for healthy subjects. Less than half of the cancer patients (n = 7, 46.7%) and only a third (n = 5, 33.3%) of healthy subjects had measured REE by MGN within clinically acceptable limits of VM. Predicted REE showed a mean bias (HB-VM) of -5% for cancer patients and 4% for healthy subjects, with limits of agreement of -30 to 20% and -27 to 34%, respectively. CONCLUSIONS: Limits of agreement for the MG and Harris Benedict equations compared to traditional indirect calorimetry were similar but wide, indicating poor clinical accuracy for determining the REE of individual cancer patients and healthy subjects.

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Objective: Obesity associated with atypical antipsychotic medications is an important clinical issue for people with schizophrenia. The purpose of this project was to determine whether there were any differences in resting energy expenditure (REE) and respiratory quotient (RQ) between men with schizophrenia and controls. Method: Thirty-one men with schizophrenia were individually matched for age and relative body weight with healthy, sedentary controls. Deuterium dilution was used to determine total body water and subsequently fat-free mass (FFM). Indirect calorimetry using a Deltatrac metabolic cart was used to determine REE and RQ. Results: When corrected for FFM, there was no significant difference in REE between the groups. However, fasting RQ was significantly higher in the men with schizophrenia than the controls. Conclusion: Men with schizophrenia oxidised proportionally less fat and more carbohydrate under resting conditions than healthy controls. These differences in substrate utilisation at rest may be an important consideration in obesity in this clinical group.

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Objective: To assess the effect of graded increases in exercised-induced energy expenditure (EE) on appetite, energy intake (EI), total daily EE and body weight in men living in their normal environment and consuming their usual diets. Design: Within-subject, repeated measures design. Six men (mean (s.d.) age 31.0 (5.0) y; weight 75.1 (15.96) kg; height 1.79 (0.10) m; body mass index (BMI) 23.3(2.4) kg/m2), were each studied three times during a 9 day protocol, corresponding to prescriptions of no exercise, (control) (Nex; 0 MJ/day), medium exercise level (Mex; ~1.6 MJ/day) and high exercise level (Hex; ~3.2 MJ/day). On days 1-2 subjects were given a medium fat (MF) maintenance diet (1.6 ´ resting metabolic rate (RMR)). Measurements: On days 3-9 subjects self-recorded dietary intake using a food diary and self-weighed intake. EE was assessed by continual heart rate monitoring, using the modified FLEX method. Subjects' HR (heart rate) was individually calibrated against submaximal VO2 during incremental exercise tests at the beginning and end of each 9 day study period. Respiratory exchange was measured by indirect calorimetry. Subjects completed hourly hunger ratings during waking hours to record subjective sensations of hunger and appetite. Body weight was measured daily. Results: EE amounted to 11.7, 12.9 and 16.8 MJ/day (F(2,10)=48.26; P<0.001 (s.e.d=0.55)) on the Nex, Mex and Hex treatments, respectively. The corresponding values for EI were 11.6, 11.8 and 11.8 MJ/day (F(2,10)=0.10; P=0.910 (s.e.d.=0.10)), respectively. There were no treatment effects on hunger, appetite or body weight, but there was evidence of weight loss on the Hex treatment. Conclusion: Increasing EE did not lead to compensation of EI over 7 days. However, total daily EE tended to decrease over time on the two exercise treatments. Lean men appear able to tolerate a considerable negative energy balance, induced by exercise, over 7 days without invoking compensatory increases in EI.