恶性肿瘤患儿静息能量代谢及影响因素

目的了解恶性肿瘤患儿能量代谢的特点,探索可能影响恶性肿瘤患儿能量代谢的因素。方法芬兰产间接能量测定仪即代谢车全封闭面罩法测定27例恶性肿瘤患儿、15例良性肿瘤患儿及268例正常对照组术前能量消耗值。并进行24小时膳食调查,测量身高、体重,评估营养状况,分析能量消耗值与饮食摄入、营养状况的关系。结果恶性肿瘤组与良性肿瘤对照组、正常对照组预计静息能量消耗值的百分比(PEE)分别为(87.5±22.6)%、(90.2±9.0)%、(89.2±10.6)%,3组比较差异无显著性;良、恶性组间营养状况差异无显著性;恶性肿瘤患儿饮食摄入较良性肿瘤患儿明显不足,分别按有否饮食摄入不足与营养不良将恶性肿瘤患儿分为两组后,两样本t检验比较组间PEE值差异无显著性。结论现有的能量预计公式可能并不适用于中国儿童;恶性肿瘤患儿并非均显示高代谢;饮食摄入减少、营养不良等均无法解释静息能量消耗值的变化;肿瘤本身代谢特点可能是导致恶性肿瘤患儿静息能量消耗值变化的主要原因。     

新生儿静息能量消耗影响因素研究进展

新生儿静息能量消耗受多种因素影响，而且新生儿的能量代谢变化率很大。个体能量消耗的确定能指导合理营养支持，预防过低或过度喂养。预计公式仅考虑到单一因素对能量消耗的影响，所以不能正确估计能量消耗，要正确的估计能量需要应对新生儿进行间接能量测定。     

心理护理对风心病瓣膜置换手术患者术前手术应激的影响

目的 探讨心理护理对风心病瓣膜置换手术患者术前手术应激的影响.方法 将我院2010年1月至2013年1月间60例风心病患者随机分为研究组和对照组各30例,在常规护理的基础上,研究组采用心理护理,对照组不应用心理护理,比较两组患者术前心理和生理应激变化情况.结果 两组护理前心理和生理应激差异无统计学意义(P＞0.05),护理后均有明显改善,研究组焦虑改善情况明显优于对照组(P＜0.01);研究组患者血糖和血浆皮质醇含量明显低于对照组(P＜0.05).结论 心理护理可有效预防和减轻风心病瓣膜置换手术患者术前的心理和生理应激反应,降低手术危险性.     

健康老年人静息能量消耗

目的 :　探讨老年人 REE与性别、年龄 ,人体测量学指标的相关性。方法 :　用间接能量测定仪测试 82名 (男 3 0、女 5 2 )平均年龄 80岁的中国健康汉族老年人的静息能量消耗 (rest-ing energy expenditure,REE)的水平 ,并将 REE测试值与根据 Harris- Benedict公式算出的基础能量消耗值 (basal energy expenditure,BEE)进行比较。同时应用生物电阻抗分析法 (bioelectricalimpedance analysis,BIA)测定去脂体重 (fatfree mass,FFM)和体脂重量 (fat mass,FM)等人体测量学数据。结果 :　 82名健康老人的 REE平均值为 (4.44± 0 .5 2 ) MJ/2 4 h,与公式计算的 BEE比无统计学差异 ,但比 FAO/WHO/UNU(1 985 )公式值低 9% ,比 Owen公式值低 1 9%。本研究观察到我国健康老年人的 REE与去脂体重、体重、体表面积 (body surface area,BSA)、年龄、身高、性别和体重指数 (body mass index,BMI)之间有相关性。老年男女的每公斤体重、每公斤去脂体重和单位体表面积所产生的 REE间无统计学差异。结论 :　 Harris- Benedict公式、FAO/WHO/UNU(1 985 )公式与 Owen公式都过高估计了我国健康老年人的基础能量消耗。由于老年人的REE存在较大的个体差异 ,其 REE值宜实测而不宜用公式预测。我国健康老年人的 REE与去脂体重、体?     

不同能量代谢水平对西安市中老年女性体质健康的影响

目的探讨西安市中老年女性不同剩余能量水平与体质健康的关系。方法2011年采用多阶段分层随机抽样的方法,抽取西安市40~60岁650名长住居民进行形态、机能、血生化、心理等指标进行测量;通过问卷调查其营养及体力活动状况,采用四分位数法对剩余能量(SE)进行水平分级,将受试对象分为以下4组:当SE相似文献   

人工心脏瓣膜再次置换手术的体外循环管理

[目的]回顾总结30例再次心脏瓣膜置换手术的体外循环(CPB)管理和方法。[方法]对2005年1月～2010年1月的30例再次心脏瓣膜置换手术患者,术前进行全面评估,充分准备,术中采用适宜的插管部位,自体血液回收,中一高流量,维持平均动脉压(MAP)50～80mmHg。[结果]二尖瓣或主动脉瓣单瓣置换16例;二尖瓣+主动脉瓣置换7例;二尖瓣+主动脉瓣+三尖瓣置换4例;三尖瓣置换3例,无手术死亡,术后死亡2例。[结论]再次心脏瓣膜置换手术中,术前充分准备,选择适当插管部位,术中加强血液保护、心肌保护,采取综合措施是保证CPB成功的关键。     

初诊食管癌患者能量代谢的影响因素和营养风险筛查

目的 研究影响初诊食管癌患者能量代谢的相关因素。方法对2008年11月至2009年6月新华医院40例初诊食管癌患者，采用营养风险筛查2002（NRS2002）进行营养风险筛查，分别应用间接测热法和生物电阻抗法测定其静息能量消耗（REE）和人体组成成分。结果67．5％的初诊食管癌患者存在营养风险，且营养风险评分与其前白蛋白及白蛋白水平分别呈负相关关系（r=-0．444，P=0．004；r=-0．386，P=0．014）。REE实测值和Harris-Benedict公式REE预测值分别为（6770±1360）和（6021±841）kJ／d，两者具有相关性（r=0．503，P=0．001），且前者显著高于后者（P〈0．001）。40例患者中，57．5％处于高代谢状态，30．0％处于正常代谢状态，12．5％处于低代谢状态。多元线性逐步回归分析显示，在众多营养指标中仅去脂组织对REE值的影响具有统计学意义（P〈0．001）。结论去脂组织是初诊食管癌患者能量代谢的影响因素之一。     

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。     

神经疾病机械通气病人的间接能量测定与经验能量估算比较

目的:通过间接能量测定值与经验能量估算值比较,为重症神经疾病病人提供合理能量供给依据.方法:纳入40例机械通气治疗的神经疾病危重症病人,采用间接能量测定法获得能量值,根据间接能量测定值偏离经验能量估算值程度分为能量接近组(偏离经验能量估算值≤15％)和能量偏离组(偏离经验能量估算值＞15％),分析两组病人能量值偏离的影响因素. 结果:能量接近组19例,能量偏离组21例.两组单因素比较,年龄、BMI、脑卒中、脑炎、应用镇静肌松药等5个因素差异均有显著性统计学意义(P＜0.05).经多因素Logistic回归分析后,仅病种分类中的脑炎为能量值偏离的独立影响因素(P=0.024). 结论:脑炎病人间接能量测定值明显偏离(高于)经验能量估算值,不适合采用经验能量估算法,其他病人间接能量测定值与经验能量估算值接近.     

机械通气患者静息能量消耗测定

目的探讨机械通气治疗患者能量消耗与疾病严重程度的相关性,比较间接能量消耗测定仪测定的能量消耗与Harris—Benedict公式推导的能量消耗差异。方法以24例采用机械通气治疗的普外科重症监护病房患者为研究对象,收集采用机械通气72h时的相关数据计算急性生理与既往健康状况评分Ⅱ（APACHEⅡ）和MarshaⅡ评分。机械通气72h时,采用MedGraphicsCCM／DSystem能量测定系统测定静息能耗值（MREE）;采用Harris—Benedict公式计算基础能耗值,再乘以相应应激系数得出预测静息能耗值（PREE）。结果机械通气72h时,所有患者的平均APACHEⅡ评分和MarshaⅡ评分分别为（14±5）和（6±3）分,MREE和PREE分别为（6793．64±1197．15）和（8041．02±1971．54）kJ／d。MREE与PREE间无相关性（r^2=0．28,P=0．07）,差异有统计学意义（t=7．62,P=0．04）。MREE与APACHEⅡ评分（r^2=0．14,P=0．08）和MarshaⅡ评分（r^2=0．08,P=0．63）间、PREE与APACHEⅡ评分（r^2=0．05,P=0．65）和MarshaⅡ评分（r^2=0．03,P=0．87）间均无显著相关性。结论机械通气患者能量消耗与疾病严重程度无相关性。采用校正Harris—Benedict公式推导的PREE过高估计了机械通气患者的能耗水平。     

Comparison of Resting Energy Expenditure Prediction Methods With Measured Resting Energy Expenditure in Obese,Hospitalized Adults

Background: Several methods are available to estimate caloric needs in hospitalized, obese patients who require specialized nutrition support; however, it is unclear which of these strategies most accurately approximates the caloric needs of this patient population. The purpose of this study was to determine which strategy most accurately predicts resting energy expenditure in this subset of patients. Methods: Patients assessed at high nutrition risk who required specialized nutrition support and met inclusion and exclusion criteria were enrolled in this observational study. Adult patients were included if they were admitted to a medical or surgical service with a body mass index ≥ 30 kg/m². Criteria excluding patient enrollment were pregnancy and intolerance or contraindication to indirect calorimetry procedures. Investigators calculated estimations of resting energy expenditure for each patient using variations on the following equations: Harris‐Benedict, Mifflin–St. Jeor, Ireton‐Jones, 21 kcal/kg body weight, and 25 kcal/kg body weight. For nonventilated patients, the MedGem handheld indirect calorimeter was used. For ventilated patients, the metabolic cart was used. The primary endpoint was to identify which estimation strategy calculated energy expenditures to within 10% of measured energy expenditures. Results: The Harris‐Benedict equation, using adjusted body weight with a stress factor, most frequently estimated resting energy expenditure to within 10% measured resting energy expenditure at 50% of patients. Conclusion: Measured energy expenditure with indirect calorimetry should be employed when developing nutrition support regimens in obese, hospitalized patients, as estimation strategies are inconsistent and lead to inaccurate predictions of energy expenditure in this patient population.     

Resting Energy Expenditure in Older Inpatients: A Comparison of Prediction Equations and Measurements

Determining energy requirements are an important component of nutritional support for patients with malnutrition; however, the validity of prediction equations for resting energy expenditure (REE) is disputed in older hospitalized patients. We aimed to assess the validity of these equations in older hospitalized patients in Japan. This was a single-center, cross-sectional study of 100 patients aged ≥70 years, hospitalized between January 2020 and December 2021. REE was measured using an indirect calorimeter and was compared to the predicted values calculated from five REE prediction equations. The mean (95% confidence interval) measured REE was 968.1 (931.0, 1005.3) kcal/day, and the mean predicted REE was higher for the FAO/WHO/UNU (1014.3 [987.1, 1041.6] kcal/day, p = 0.164) and Schofield (1066.0 [1045.8, 1086.2] kcal/day, p < 0.001) equations and lower for the Harris-Benedict (898.6 [873.1, 924.1] kcal/day, p = 0.011), Ganpule (830.1 [790.3, 869.9] kcal/day, p < 0.001), and body weight (kg) × 20 (857.7 [821.9, 893.5] kcal/day, p < 0.001) equations. In the age group analysis, none of the predicted values were within a 10% error for more than 80% of patients aged 70–89 years and ≥90 years. The five REE prediction equations did not provide accurate estimates. Validated REE prediction equations need to be developed for older hospitalized patients.     

Changes in Resting Energy Expenditure Following Orthotopic Liver Transplantation

Background:There is no consensus whether resting energy expenditure (REE) following orthotopic liver transplantation (OLT) is altered. Methods: The objectives of this investigation were to describe changes in measured REE (mREE) using indirect calorimetry in 25 OLT patients on days 5, 10, and 15 after baseline (within 72 hours following OLT) and compare mREE changes with those calculated with 2 predicted equations for energy expenditure (pREE): the Harris‐Benedict and Schofield equations. Results: Patients were 57 ± 5.4 years of age, 44% were male, 36% were black, and 72% had liver disease of viral etiology. Measured REE (at baseline and days 5, 10, and 15, per kcal/d: 1832 ± 952, 1565 ± 383, 1538 ± 345, 1578 ± 418) and kcal per kilogram of body weight (22.7 ± 12.8, 18.4 ± 4, 18.7 ± 3.8, 21 ± 6.5) did not change over time. In contrast, changes in pREE based on either the Harris‐Benedict (P < .001) or Schofield (P = .006) equation using measured weights at each corresponding time point and lowest body weight during the study to estimate dry weight were significant. Conclusions: Wide ranges in both mREE and mREE expressed per kilogram of body weight at each study time point were observed in contrast to pREE, which declined by day 15. The observed differences in mREE over time suggest indirect calorimetry is indicated if available following OLT. Additional research is warranted to determine the most appropriate predictive equation with suitable stress factors to use when indirect calorimetry is not available.     

Accuracy of Predictive Equations for Energy Expenditure in Mexicans Living With HIV/AIDS With and Without Antiretroviral Therapy

Introduction: Determination of the resting energy expenditure (REE) is essential for planning nutrition therapy in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) to help to improve their nutrition status. We aim to evaluate the agreement and accuracy of prediction equations that estimate the REE in a Mexican population with a diagnosis of HIV/AIDS with and without antiretroviral therapy (ART). Methods: A cross‐sectional study in Mexican patients with HIV/AIDS with and without ART. Weight, height, and body composition measured with dual‐energy x‐ray absorptiometry were evaluated. The REE was determined with indirect calorimetry and estimated using the Mifflin–St Jeor (MSJ), Harris‐Benedict (HB), Schofield 1 and 2, Cunningham, Melchior 91, Melchior 93, and Batterham equations. The Bland‐Altman method assessed agreement between the real and estimated values, and the percent difference between these values was used to assess the prediction accuracy. Results: Sixty‐five adults without ART and 102 adults with ART were included. The mean REE (kcal/kg) was 24.8 ± 2.4 and 23.8 ± 3.6 in patients without and with ART, respectively. Good agreement and reliability were observed in the HB (intraclass correlation coefficient [ICC], 0.75; P < .05), Batterham (ICC, 0.79; P < .05), Schofield 1 (ICC, 0.74; P < .05), and Schofield 2 (ICC, 0.78; P < .05) results in individuals without ART. In individuals with ART, good agreement and reliability were observed with the HB equation (ICC, 0.76; P < .05). The MSJ equation showed good agreement with poor reliability (ICC, 0.05; P < .05). Conclusion: The equations with the best agreement and accuracy were Schofield 2, Batterham, and HB in individuals without ART and HB and MSJ in the population with ART.     

Resting Energy Expenditure and Related Factors in 6- to 9-Year-Old Southern African Children of Diverse Population Groups

Lower resting energy expenditure (REE) may partially explain the disproportionate prevalence of overweight/obesity among black African women. As no previous studies have investigated the REE of Southern African (South. Afr.) children, we aimed to determine, by sex and population group, the REE of 6- to 9-year-old urban school children. In a cross-sectional study with quota sampling, REE was measured with indirect calorimetry (IC). Confounders considered were: body composition (BC) (fat-free mass (FFM), FFM index, fat mass (FM), FM index), assessed using multifrequency bioelectrical impedance analysis, and physical activity (PA) measured with a pedometer. Multivariate regression was used to calculate REE adjusted for phenotypes (BC, z-scores of weight-for-age, height-for-age, body mass index-for-age) and PA. Sex and population differences in REE were determined with two-way ANOVA. Ninety-four healthy children (59.6% girls; 52.1% black) with similar socioeconomic status and PA opportunities participated. Despite BC variations, sex differences in REE were not significant (41 kcal/day; P = 0.375). The REE of black participants was lower than of white (146 kcal/day; P = 0.002). When adjusted for FFM and HFA z-score, the differences in REE declined but remained clinically meaningful at 91 kcal/day (P = 0.039) and 82 kcal/day (P = 0.108), respectively. We recommend the development of population-specific REE prediction equations for South. Afr. children.     

Predictive Equations Are Inaccurate in the Estimation of the Resting Energy Expenditure of Children With End‐Stage Liver Disease

Background and Objectives: Malnutrition is common in children with end‐stage liver disease (ESLD) and is associated with increased morbidity and mortality. The inability to accurately estimate energy needs of these patients may contribute to their poor nutrition status. In clinical practice, predictive equations are used to calculate resting energy expenditure (cREE). The objective of this study is to assess the accuracy of commonly used equations in pediatric patients with ESLD. Methods: Retrospective study performed at the Hospital for Sick Children. Clinical, laboratory, and indirect calorimetry data from children listed for liver transplant between February 2013 and December 2014 were reviewed. Calorimetry results were compared with cREE estimated using the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), Schofield [weight], and Schofield [weight and height] equations. Results: Forty‐five patients were included in this study. The median age was 9 months, and the most common indication for transplantation was biliary atresia (64%). The Schofield [weight and height], FAO/WHO/UNU, and Schofield [weight] equations were compared with indirect calorimetry and found to have a mean (SD) difference of 48.8 (344.0), 59.3 (229.8), and 206.5 (502.6) kcal/d, respectively. The FAO/WHO/UNU, Schofield [weight], and Schofield [weight and height] equations introduced a mean error of 21%, 38%, and 76%, respectively. The FAO/WHO/UNU equation tended to underestimate, whereas the Schofield equations overestimated the REE. Conclusions: Commonly used predictive equations perform poorly in infants and young children with ESLD. Indirect calorimetry should be used when available to guide energy provision, particularly in children who are already malnourished.