肥胖儿童静息能量消耗的初步研究

目的　应用能量代谢仪测定肥胖儿童的静息能量消耗情况,以探讨安静状态下代谢水平与儿童肥胖的关系,同时比较各预测公式的准确性。方法　选取2014年7月至2015年9月在本院儿童保健科就诊的年龄在7～14周岁的儿童。应用间接测热法测静息能量消耗,应用生物电阻抗法测体脂、去脂体质量等体成分。以预测公式值与实测值误差在±10%之内为可以接受的“准确”,超过10%为“高估”,低于10%为“低估”。结果　共纳入研究对象72例,其中超重/肥胖组42例,平均年龄(10.4±1.7)岁,体质量指数(24.76±3.16)kg/m2;对照组共30例,平均年龄(10.4±2.0)岁,体质量指数(15.07±2.11)kg/m2。静息能量消耗以kJ/(kg·d)表示,校正性别、年龄的影响因素后肥胖组儿童每天静息能量消耗［(116.45±20.46)kJ/(kg·d)］明显低于对照组［(138.49±38.77) kJ/(kg·d)］(P=0.000)。各预测公式准确性尚无一致性定论,研究对象实测值与Harris-Benedict、Schofield、WHO、Mifflin、Cunningham、Liu、Jia公式完全符合的准确率分别为50%、47.6%、42.9%、47.6%、33.3%、42.9%、40.5%。结论　肥胖儿童静息能量消耗低于正常儿童,与间接测热法比较,各预测公式准确性均较差。     

重型颅脑外伤并发高钠血症的原因分析

目的 探讨重度脑损伤并发高钠血症的原因。方法 分析2003年6月-2006年6月收治的32例重型颅脑损伤并发高钠血症患者的临床资料，监测血钠测定值，回顾性总结高钠血症形成的危险因素。结果 9例血钠恢复正常前死亡，23例6—9d血钠恢复正常，血钠正常后患者尿钠检测仍低，最早直到血钠恢复正常1周后尿钠才正常。结论 重型颅脑外伤、甘露醇高渗利尿剂的应用、显性或隐性失水增多、饮水受限等因素是高钠血症发生的外部条件，而中线结构损伤、下丘脑-垂体轴受损，抗利尿激素分泌减少和促肾上腺皮质激素增高是高钠血症发生的内在机制，通过纠正外部条件后多数能纠正高钠血症。     

颅脑损伤病人静息能量消耗的观测

目的 研究不同颅脑损伤病人伤后静息能量的变化。方法 用开放式间接测热法测定40例不同程度颅脑损伤病人伤后前5天的氧耗量(VO2)、二氧化碳生成量(VCO2)、呼吸商(RQ)、静息能量消耗(REE)和24小时尿氮排出量。结果 有脑损伤组患者伤后氧耗量、静息能量消耗、24小时尿氮排出量比无脑损伤组明显增加，且与脑损伤程度成正比。结论 颅脑损伤患者脑组织和全身处于高能量代谢状态，对氧和能量的需求急剧增加，应根据损伤程度合理、适时地补充营养，以利康复。     

重型颅脑外伤并发高钠血症的原因分析

目的探讨重度脑损伤并发高钠血症的原因。方法分析2003年6月～2006年6月收治的32例重型颅脑损伤并发高钠血症患者的临床资料,监测血钠测定值,回顾性总结高钠血症形成的危险因素。结果9例血钠恢复正常前死亡,23例6～9d血钠恢复正常,血钠正常后患者尿钠检测仍低,最早直到血钠恢复正常1周后尿钠才正常。结论重型颅脑外伤、甘露醇高渗利尿剂的应用、显性或隐性失水增多、饮水受限等因素是高钠血症发生的外部条件,而中线结构损伤、下丘脑-垂体轴受损,抗利尿激素分泌减少和促肾上腺皮质激素增高是高钠血症发生的内在机制,通过纠正外部条件后多数能纠正高钠血症。     

重型颅脑外伤病人早期呼吸困难临床研究

目的 探讨重型颅脑外伤早期呼吸困难临床意义与治疗效果.方法 回顾性分析本院261例重型颅脑外伤病人住院治疗的临床资料.结果 261例重型颅脑外伤病人中,早期呼吸困难70例,占26.8%.70例呼吸困难的病人中,呼吸道阻塞48例,中枢性呼吸困难8例,胸部外伤致呼吸困难5例,贫血性呼吸困难2例,上颈段脊髓损伤致呼吸困难1例,多种因素致呼吸困难6例.非呼吸困难组患者治疗的清醒人数及昏迷人数优于呼吸困难组(P＜0.01),其死亡率低于呼吸困难组,但差异无统计学意义(P＞0.05).结论 重型颅脑外伤病人中引起呼吸困难的主要原因是外伤因素,呼吸道阻塞,咳嗽,吞咽反射减弱消失,伴分泌物误吸或坠积于肺内,早期诊治能改善其预后.     

重型颅脑外伤临床及预后因素分析

目的：了解重型颅脑外伤合并多发伤患者的临床特点、治疗情况、并发症与预后的关系.方法：回顾性分析本院 129 例重型颅脑外伤患者的临床及预后特点.结果：交通伤 126 例,撞击伤 2 例,挤压伤 1 例 ;生存 116 例,死亡 13 例 ;按格拉斯哥预后评分 （GOS） 评定：Ⅴ级 110 例,Ⅳ级 7 例,Ⅲ级 6 例,Ⅱ级 1 例,Ⅰ级 5 例.结论：重型颅脑外伤应及时发现及时治疗,掌握最佳治疗时机,防治严重并发症.     

先天性心脏病患儿术后静息能量消耗

目的　应用间接测热法（IC）测定先天性心脏病术后机械通气患儿的静息能量消耗（REE）,探究先天性心脏病患儿术后静息能量代谢规律及可能影响因素。方法　纳入2015年2至6月入住上海儿童医学中心心胸外科重症监护室的先天性心脏病术后患儿共150例,于术后4 h应用代谢车测定REE。收集患儿一般人口学和人体测量学资料、临床资料,分析临床因素与REE的相关性。比较患儿术后营养摄入与REE的关系。结果　入组患儿150例,男104例、女46例,中位年龄14（8.3~36.0）个月。IC测得非蛋白呼吸商为0.79±0.20,REE实测值（MREE)（264.76±61.74）kJ/(kg·d),与Schofield公式估算值（278.51±93.42）kJ/(kg·d)比较,差异无统计学意义（P=0.096）,但相关性较低（R2=0.119）;多因素逐步回归分析显示先天性心脏病风险校正评分（RACHS-1）与MREE呈显著正相关（P=0.012）、年龄与MREE呈显著负相关（P=0.010）。术后97.33%（146/150）患儿第1天摄入热量低于MREE。结论　先天性心脏病术后并未出现明显高代谢状态,但影响底物代谢。RACHS-1评分、年龄是影响患儿术后REE的因素。先天性心脏病患儿术后第1天摄入热量普遍低于REE。     

重型颅脑外伤的基础护理

目的探讨基础护理在重型颅脑外伤患者诊疗中的作用。方法回顾性分析和总结50例重型颅脑外伤患者护理经验。结果 (GCS)评定结果,Ⅰ级(痊愈)25例(50%),Ⅱ级(中残)10例(20%),Ⅲ级(重残)4例(8%),Ⅳ级(植物生存)6例(12%),Ⅴ级(死亡)5例(10%)。结论重型颅脑损伤患者的基础护理对早期诊断、治疗和恢复有重要意义。     

颅脑外伤病人的护理体会

目的 探讨颅脑外伤病人的护理措施.方法 回顾性分析2008年7月-2011年12月该院收治的颅脑外伤80例患者的临床护理资料.结果 恢复良好出院71例,死亡9例.结论 颅脑外伤病人的护理必须密切观察病情变化,及时做出准确判断,确保病人生命安全,同时还要注重呼吸道护理,做好一般护理、眼与口腔护理和并发症的预防与诊治,促进患者早日康复,提高患者生存质量.     

代谢监测下早期营养支持对重型颅脑损伤病人预后的影响

目的:比较在能量代谢测定系统(简称:代谢车)监测指导下,早期采用不同营养方式及能量组合治疗重型颅脑损伤病人的临床疗效以及并发症的发生率。方法:将符合研究标准的重型颅脑损伤病人共184例随机分为肠内营养组(EN,n=61)、肠外营养组(PN,n=62)和混合营养组(PN+EN,n=61)。采用代谢车每天清晨测定病人静息能量消耗值(REE,kcal/d)连续2周,根据REE提供适宜的能量供给病人。监测病人第1、7和第14天的血清清蛋白(ALB),血红蛋白(Hb),前清蛋白(PA)和氮平衡(NB)的变化以及伤后1个月时GOS评分;同时观察病人在营养治疗期间肺炎、呕吐、误吸和消化道出血等并发症的发生率。结果:PN+EN组病人ALB、Hb、PA和NB变化、早期肺炎与入住ICU时间等指标均优于EN组或PN组,差异有显著性统计学意义(P0.05)。PN组病人消化道出血、呕吐、误吸的发生率最高,与其他两组比有显著性差异(P0.05);2周后病人病死率比较,PN组发生率最高,且与其他两组比较有显著性统计学差异(P0.05)。结论:伤后早期采用代谢车测定重型颅脑损伤病人的静息能量消耗,指导营养供给的模式更为科学准确。采用PN+EN混合营养供给模式更适合重型颅脑损伤病人早期营养支持。     

Comparison between measured and predicted resting energy expenditure in mechanically ventilated patients with COPD

The aim of this study was to compare resting energy expenditure (REE) obtained by indirect calorimetry (IC) and Harris-Benedict (H-B) equations, and to examine whether hypocaloric nutrition support could improve protein nutritional status in mechanically ventilated patients with chronic obstructive pulmonary disease (COPD). Thirtythree COPD patients (20 males, 13 females) were recruited and REE was measured by IC. Measured REE (REEm) was compared to predictive REE by H-B equations (REEH-B) and its corrected values. Correlation between REEm and APACHE II score was also analyzed. Patients were randomly divided into hypocaloric energy group (50%-90% of REEm, En-low) and general energy group (90%-130% of REEm, En-gen) for nutrition support. The differences of albumin, prealbumin, transferrin, hemoglobin, and lymphocyte count before and after 7 days nutrition support were observed. Results show that REEH-B and REEH-B×1.2 were significantly lower than REEm (p<0.01). REEm positively correlated with APACHE II score (p<0.05 or p<0.01). After nutrition support, hemoglobin decreased significantly in En-gen group (p<0.05); lymphocyte count in both groups, and transferrin and prealbumin in the En-low group increased significantly (p<0.05 or p<0.01). Our data suggest that 1) these patients' REE were increased; 2) since IC is the best method to determine REE, in the absence of IC, H-B equations (with standard body weight) can be used to calculate REE, but the value should be adjusted by correction coefficients derived from APACHE II; 3) low energy nutrition support during mechanical ventilation in COPD patients might have better effects on improving protein nutritional status than high energy support.     

Comparison of energy prediction equations with measured resting energy expenditure in children with sickle cell anemia

OBJECTIVE: To determine the accuracy of energy prediction equations when compared with measured resting energy expenditure (REE) in children with sickle cell anemia. To develop a modified equation that more accurately estimates the energy needs of children with sickle cell anemia and to cross-validate these on a different set of patients (test patients). DESIGN: REE was measured in children using indirect calorimetry and compared with predicted values using the Harris-Benedict and the Food and Agriculture Organization/World Health Organization/United Nations University equations (WHO). SUBJECTS/SETTING: Eighteen patients participated in the original sample that compared predicted with measured energy expenditure. The modified equations were developed using the original 18 patients. A test population of 20 different patients was used to validate the modified equations. STATISTICAL ANALYSIS: Wilcoxon signed-rank test was performed to compare measured with predicted REE. The correlation analysis method and multiple linear regression method were used to develop 2 modified versions for the Harris-Benedict and WHO prediction equations. RESULTS: When compared with the mean predicted REE using the Harris-Benedict and WHO equations, the mean measured REE was 14% and 12% greater than both (P=.005 and P=.014, respectively). Two modified equations were developed from the Harris-Benedict and WHO equations. Based on the data from the test patients, the mean measured REE was 15% greater than the mean predicted REE based on the Harris-Benedict and WHO equations (P=.0001 for both). When the modified Harris-Benedict and WHO equations were used, there was almost no difference in the mean measured REE and the mean predicted REE (mean difference using Harris-Benedict = 14, P = .9273; mean difference using WHO = -13, P = .6215). CONCLUSION: Both energy prediction equations underestimated REE in children with sickle cell anemia. The 2 modified versions of the energy prediction equations that we propose predicted the energy needs of these children much more accurately; however, the modified equations need to be validated through application to other children with sickle cell anemia.     

Comparison of resting energy expenditure in bronchopulmonary dysplasia to predicted equation

OBJECTIVE: Children with bronchopulmonary dysplasia (BPD) often suffer from growth failure because of disturbances in energy balance with an increase of resting energy expenditure (REE). Evaluation of REE is a useful tool for nutritional management. Indirect calorimetry is an elective method for measuring REE, but it is time consuming and requires rigorous procedure. The objective of this study was to test accuracy of prediction equation to evaluate REE in BPD children. PATIENTS AND METHODS: Fifty-two children aged 4-10 years with BPD (30 boys and 22 girls) and 30 healthy lean children (20 boys and 10 girls) were enrolled. In this study, indirect calorimetry was compared to four prediction equations (Schoffield-W, Schoffield-HW, Harris-Benedict and Food and Agriculture Organization equation) using Bland-Altman pair wise comparison. RESULTS: The Harris-Benedict equation was the best equation to predict REE in children with BPD, and Schoffield-W was the best in healthy children. For the children with chronic lung disease of prematurity the Harris-Benedict equation showed the lowest mean predicted REE-REE measured by indirect calorimetry difference (difference = 15 kcal/day; limits of agreement -266 and 236 kcal/day; 95% confidence interval for the bias -207 to 177 kcal/day), and graphically, the best agreement. For the group of healthy children, it was the Schofield-W equation (-2.9 kcal/day; limits of agreement -275 and 269 kcal/day; 95% confidence interval for the bias -171 to 165 kcal/day), and graphically, the best agreement. CONCLUSION: Differences in prediction equation are minimal compared to calorimetry. Prediction equation could be useful in the management of children with BPD.     

Variability in results from predicted resting energy needs as compared to measured resting energy expenditure in Korean children

Energy needs are influenced by many factors, including ethnicity. Multiple studies have shown that the accuracy of an energy prediction equation varies with the ethnic background of the study population. Therefore, it is crucial to identify the most accurate energy prediction equation to use for a given population. This study compared measured resting energy expenditure to results from commonly-used energy prediction equations to identify the most accurate equation to use for Korean children. Based on previous literature showing wide variation in accuracy of energy prediction equations in different ethnic groups, we hypothesized that results from measured- vs. predicted energy needs would be significantly different in this population. Subjects were 92 South Korean children (38 boys, 54 girls) age 7.7 ± 2.7 years (mean ± SD). Measurements included: resting metabolic rate (TrueOne 2400 metabolic cart), weight/height (digital scale/stadiometer); body fat (BIA, Inbody720), blood pressure (sphingomanometer), triceps skinfold thickness (MD-500 skinfold calipers), muscle mass (Heymsfield's formula) and body surface area (Dubois formula) calculations. Resting energy needs were predicted using the Harris-Benedict, WHO/NAO/FAO, Altman and Dittmer, Maffeis, and Schofield-HW equations, and the Dietary Reference Intake recommendations. Measured and predicted energy needs were significantly correlated (P < .001 for all; range R² = 0.54-0.56), yet significantly different for all equations studied (P < .05) except the Maffeis and Schofield-HW equations. Differences (means ± SD) between measured vs. predicted energy needs ranged from 9.5 ± 123.2 (Schofield-HW) to 199.6 ± 132.7 (WHO/NAO/FAO) kcal/day, where a value closer to zero indicates increased accuracy of the prediction equation to correspond to measured energy needs. Although results from equations studied were significantly correlated with measured resting energy needs, notable discrepancies existed which, over time, could produce undesirable weight changes in Korean children.     

Measured and predicted resting energy expenditure in clinically stable patients

Resting energy expenditure (REE), measured by bedside indirect calorimetry, was compared to estimated REE by the Harris-Benedict and Kleiber predictors in 200 clinically stable hospitalized patients (100 males, 100 females) and 72 healthy control subjects (20 males, 52 females).Mean predicted values were not significantly different from measured REE for the male patients and control subjects, but measured REE was significantly overestimated by the Kleiber formula in female patients and controls (p<0.01). In comparison to control subjects, a substantially larger range of individual differences between measured REE and resting energy expenditure as estimated by the Harris-Benedict and Kleiber formulae existed among the male and female patient samples. Measured REE was over or underestimated by greater than 10% via the Harris-Benedict predictors in 40% of the patients but only 20% of the healthy controls. The Kleiber formulae were inappropriate for 46% of the individual patients and 33% of the normal subjects.Since no method exists for identifying the clinically stable patient for whom REE cannot be estimated via these commonly employed predictors, the bedside measurement of resting energy expenditure is the most appropriate method for deriving caloric expenditure and designing subsequent caloric provision regimens for adequate and safe nutritional repletion or maintenance.     

用公式计算与实测静息能量消耗的2型糖尿病患者对比观察

目的　研究预测公式用于评估老年2型糖尿病患者能量消耗的准确性。方法　纳入2014年8月至2015年8月的住院老年患者75例,以间接测热法获得的实际测量值为标准,与6种公式预测值进行比较,统计分析采用配对t检验、吻合比例、Bland-Altman分析法。结果　患者实测能量消耗值为（1 513.2±240.6）kcal/d,Harris-Benedict公式、Owen公式、Mifflin公式、Liu公式预测值与实测值间差异有统计学意义（均P＜0.05）。FAO/WHO/UNU公式、Schofield公式计算值与实测值间差异无统计学意义（均P＞0.05）,Bland-Altman分析发现FAO/WHO/UNU公式计算值与实测值差值的均数与0最相近（－18.9 kcal/d）,但其95%一致性界限为（－363.3,325.5）kcal/d,仍超出了临床可接受范围。结论　预测公式用于评估老年2型糖尿病患者的能量消耗准确性不佳。     

Predicted and measured resting energy expenditure in healthy young women

Basal energy expenditure (BEE) was either measured by indirect calorimetry or predicted by different formulae in 104 young women: 74 lean and overweight subjects (normal weight, NWt) and 30 obese subjects. The predictive equations were based on weight alone (Owen, FAO-1, Schofield-1) or on weight and height (Harris-Benedict, Mifflin, Kleiber, and again FAO-2 and Schofield-2). With the exception of the Owen equation all the equations over-estimated measured BEE in both study groups. The ratio between measured and predicted value (% MP) varied between 102.3 (Owen) and 87.7 (Kleiber) in the NWt subjects and between 113.2 (Owen) and 89.3 (Schofield-1) in the obese subjects. The range including 95% of the predicted-measured differences (PMdiff) was larger than 1700 kJ/d in the NWt group and 2300 kJ/d in the obese group. In both study groups most of the equations showed a significant relationship between PMdiff and/or % MP with body weight and the magnitude of BEE. In conclusion, these equations are of little help in predicting BEE in a single subject and should be used with caution when assessing energy requirements in populations or groups of subjects.     

Predicting resting energy expenditure in patients with musculoskeletal deformities

The aim of this study was to assess the validity of the commonly used equations (Harris-Benedict (HB), Schofield (S) and equations based on midarm circumference (MAC) and midarm muscle circumference (MAMC) in predicting resting energy expenditure (REE) in a population of patients with musculoskeletal deformities. 20 kyphoscoliotic patients (15 female (F); 5 male (M); mean age 59.6 years) and 10 controls (7 F; 3M; 59.8 years) were studied. REE measured by indirect calorimetry (IC) with a ventilated canopy system (Deltatrac metabolic monitor) was not significantly different between patients and controls (Mean (SD) REE (MJ/24 h): Patients: 5.48 (1.1); controls: 5.28(0.8)). In patients with deformities the Schofield equation gave values which were closest to measured REE (mean difference and limits of agreement IC vs S: 0.098 MJ/24 h; -0.822 and 1.018). The Harris-Benedict equation using height (Ht) and armspan (AS) in lieu of height also gave acceptable results (IC vs HB (Ht): 0.34; -0.638 and 1.318; IC vs HB (AS): 0.255; -0.683 and 1.253). Equations based on MAC and MAMC compared poorly (IC vs MAC equation: 0.398; -1.530 and 2.326; IC vs MAMC equation 0.687; -0.911 and 2.285). On regression analysis the equation REE = 0.295 (MAMC) + 0.0483 (AS) -0.0324 (age) -6.25 predicted REE best in the patient population (r(2) = 0.861).