目的:无法确定患者的能量需求会产生多种生理和经济后果。以前的研究表明,预测公式,包括哈里斯·本尼迪克特方程(HB),在重症患者和健康的年轻成年人中,过度和低估了能量需求,与黄金标准相比,间接量热法(IC)。在普通病房中住院患者的测量和估计能量需求之间的比较被低估了。这项研究的目的是评估在个别住院患者中测量的能量需求和HB估计的需求之间的一致性。并调查这些发现是否与其他特定患者特征相关。
方法:使用IC(n=86)测量奥尔堡大学医院收治的患者的静息能量消耗(REE)和生物阻抗分析(BIA)(n=67)用于测量身体成分。此外,高度,体重,身体质量指数,小腿周长,虽然有关医院病房的信息,重要的价值观,营养师估计,在患者的电子病历中收集了能量需求和血液样本。Bland-Altman阴谋,多元线性回归分析,和Chi2测试进行。
结果:IC与HB之间的平均差异(6.2%),营养师估计(7.8%)和BIA(4.50%)(p<0.05)。REE与骨骼肌质量(SMM)之间的关联(R2=0.58,β=149.0kJ),体脂质量(BFM)(R2=0.51,β=59.1kJ),发现体重(R2=0.62,β=45.6kJ)(p<0.05)。在以下变量中发现测量的REE和HB之间的正相关(p<0.05):CRP,年龄,手术病人,和呼吸频率。
结论:这项研究发现,与测量的REE相比,估计的能量消耗总体上被低估了。测得的稀土和SMM之间呈正相关,发现了BRM和体重。最后,研究发现CRP之间有更大的关联,年龄,手术病人,和呼吸频率以及测量和估计能量需求之间的一般差异大于±10%。
Failure to identify a patient\'s energy requirement has a variety of consequences both physiological and economical. Previous studies have shown that predictive formulas, including the Harris Benedict equation (HB), both over- and underestimates energy requirement in severely ill patients and healthy younger adults, compared to the golden standard, indirect calorimetry (IC). The comparison between measured and estimated energy requirements in hospitalized patients in regular wards is underreported. The aim of this study was to assess the agreement between measured energy requirements and requirements estimated by HB in the individual hospitalized patients, and to investigate whether those findings were associated with other specific patient characteristics.
IC (n = 86) was used to measure resting energy expenditure (REE) and bioimpedance analysis (BIA) (n = 67) was used for body composition in patients admitted to Aalborg University Hospital. Furthermore, height, weight, body mass index, calf circumference, while information regarding hospital ward, vital values, dieticians estimated energy requirements and blood samples were collected in the patients\' electronic medical records. Bland-Altman plots, multiple linear regression analysis, and Chi2 tests were performed.
On average a difference between IC compared with the HB (6.2%), dietitians\' estimation (7.8%) and BIA (4.50%) was observed (p < 0.05). Association between REE and skeletal muscle mass (SMM) (R2 = 0.58, β = 149.0 kJ), body fat mass (BFM) (R2 = 0.51, β = 59.1 kJ), and weight (R2 = 0.62, β = 45.6 kJ) were found (p < 0.05). A positive association between measured REE and HB were found in the following variables (p < 0.05): CRP, age, surgical patients, and respiratory rate.
This study found a general underestimation of estimated energy expenditure compared to measured REE. A positive correlation between measured REE and SMM, BRM and weight was found. Lastly, the study found a greater association between CRP, age, surgical patients, and respiratory rate and a general greater than ±10% difference between measured and estimation of energy requirements.