Swinamer D L, Grace M G, Hamilton S M, Jones R L, Roberts P, King E G
Division of Critical Care Medicine, University of Alberta, Edmonton, Canada.
Crit Care Med. 1990 Jun;18(6):657-61. doi: 10.1097/00003246-199006000-00017.
Traditional formulas, such as the Harris and Benedict equation (HBE), do not accurately predict energy expenditure (EE) in mechanically ventilated, critically ill patients (MVCIP). The purpose of this study was to develop a predictive EE equation to assess EE requirements in MVCIP. A portable metabolic cart was used to measure indirectly EE in 112 MVCIP. Patients were studied at rest and for 30 min on the first or second day of ICU admission. No nutrition was received during the study period. Variables investigated were: age, BSA, Acute Physiology and Chronic Health Evaluation (APACHE II) score, sepsis score, Injury Severity Score (ISS), respiratory rate (f), tidal volume (VT), minute ventilation, mean arterial pressure, heart rate, body temperature (Temp), and outcome. Patient age, APACHE II score, sepsis score, ISS, and BSA were 50 +/- 20 yr, 16 +/- 7, 11 +/- 6, 32 +/- 14, and 1.80 +/- 0.27 m2, respectively. Correlation and multiple regression analyses were used with EE as the dependent variable. A predictive equation (EE [kcal/day] = 945 BSA -6.4 age + 108 Temp + 24.2 f + 817 VT -4349) was determined from variables that contributed greater than 3% to the variance of EE: BSA (52%), age (10%), f (5%), VT (5%), and Temp (3%). The HBE underestimated measured EE by 34 +/- 19% and in 79 patients deviated greater than 15%. Using the new equation, only 15 patients' EE deviated greater than 15% from measured EE. The new predictive EE equation can accurately assess EE in MVCIP.
传统公式,如哈里斯-本尼迪克特方程(HBE),不能准确预测机械通气的危重症患者(MVCIP)的能量消耗(EE)。本研究的目的是开发一种预测EE的方程,以评估MVCIP的EE需求。使用便携式代谢车间接测量112例MVCIP的EE。在ICU入院的第一天或第二天,对患者进行静息状态及30分钟的研究。研究期间未给予营养支持。研究的变量包括:年龄、体表面积(BSA)、急性生理与慢性健康状况评分系统(APACHE II)评分、脓毒症评分、损伤严重度评分(ISS)、呼吸频率(f)、潮气量(VT)、分钟通气量、平均动脉压、心率、体温(Temp)及预后。患者年龄、APACHE II评分、脓毒症评分、ISS及BSA分别为50±20岁、16±7、11±6、32±14及1.80±0.27 m²。以EE作为因变量进行相关分析和多元回归分析。从对EE方差贡献大于3%的变量中确定了一个预测方程(EE[千卡/天]=945×BSA - 6.4×年龄 + 108×Temp + 24.2×f + 817×VT - 4349):BSA(52%)、年龄(10%)、f(5%)、VT(5%)及Temp(3%)。HBE低估实测EE达34±19%,79例患者的偏差大于15%。使用新方程时,只有15例患者的EE与实测EE的偏差大于15%。新的预测EE方程能够准确评估MVCIP的EE。