[EngO5-5] A new predictive equation for resting energy expenditure in mechanically ventilated Thai patients
Background: The gold standard used to determine calorie-need in critically ill patients has been conducted through an indirect calorimeter. However, in Thailand, it is rarely affordable. Therefore current predictive equations have attracted a great deal of attention as the alternatives in routine clinical practice. The precision of previously validated equations for estimating energy requirements in critically ill patients ranged from 37-65% of measured resting energy expenditure (REE). However, up to our knowledge, the validated equation for evaluating REE in mechanically ventilated Thai patients has not been yet clarified.
Purpose: To formulate the specific predictive equations for predicting REE in mechanically ventilated Thai patients and evaluate the accuracy of the 10 current predictive equations for predicting REE in mechanically ventilated Thai patients.
Methods: This was a prospective observational, single-center study conducted in adult medical and surgical ICU. The study population comprised mechanically ventilated Thai patients (N = 63) were measured REE by indirect calorimeter and compared to the REE calculated from the 10 predictive equations. The specific data (10 variables) were prospectively collected to formulate the new equation.
Results: 63 patients (67.03±17.25 Y, SAP II 31.86±10.59) from both medical ICU ( 47.6% ) and surgical ICU ( 52.4% ) were included in this study. We found that only 6 variables from 10 variables that were used in the ten current predictive equations have the significant correlations with the REE that measured from indirect calorimeter (p<0.001). Among the ten current predictive equations - the Penn State 2010 (r = 0.757, p<0.001), Swinamer 1990 (r = 0.753, p < 0.001) and Ireton Jones 2002 (r = 0.696, p < 0.001) respectively have the good correlations with the gold standard REE measured from the indirect colorimeter. We proposed the new predictive equation [ REE = 1528.85+(3.07x heart rate) – (27.88 x Respiratory rate )+ (64.92x Maximum Body Temperature) + (75.94 x minute ventilation ) + 136.52 X Gender ( Male=1 or Female = 0 ) – ( 5.95 x Age ) – (27.92x Height ) – ( 7.94 x Actual body weight ) + (1534.56 x Body surface area ) – ( 290.12 x Type of patient : Medical = 0 , Surgical =1)] that have the best correlation (r=0.845 , p<0.001) with the REE that measured from the indirect calorimeter.
Conclusions: In this study we can formulate a specific equation for estimating resting energy expenditures in mechanically-ventilated Thai patients that has the best accuracy and correlation with the gold standard REE measured from the indirect calorimeter.
Purpose: To formulate the specific predictive equations for predicting REE in mechanically ventilated Thai patients and evaluate the accuracy of the 10 current predictive equations for predicting REE in mechanically ventilated Thai patients.
Methods: This was a prospective observational, single-center study conducted in adult medical and surgical ICU. The study population comprised mechanically ventilated Thai patients (N = 63) were measured REE by indirect calorimeter and compared to the REE calculated from the 10 predictive equations. The specific data (10 variables) were prospectively collected to formulate the new equation.
Results: 63 patients (67.03±17.25 Y, SAP II 31.86±10.59) from both medical ICU ( 47.6% ) and surgical ICU ( 52.4% ) were included in this study. We found that only 6 variables from 10 variables that were used in the ten current predictive equations have the significant correlations with the REE that measured from indirect calorimeter (p<0.001). Among the ten current predictive equations - the Penn State 2010 (r = 0.757, p<0.001), Swinamer 1990 (r = 0.753, p < 0.001) and Ireton Jones 2002 (r = 0.696, p < 0.001) respectively have the good correlations with the gold standard REE measured from the indirect colorimeter. We proposed the new predictive equation [ REE = 1528.85+(3.07x heart rate) – (27.88 x Respiratory rate )+ (64.92x Maximum Body Temperature) + (75.94 x minute ventilation ) + 136.52 X Gender ( Male=1 or Female = 0 ) – ( 5.95 x Age ) – (27.92x Height ) – ( 7.94 x Actual body weight ) + (1534.56 x Body surface area ) – ( 290.12 x Type of patient : Medical = 0 , Surgical =1)] that have the best correlation (r=0.845 , p<0.001) with the REE that measured from the indirect calorimeter.
Conclusions: In this study we can formulate a specific equation for estimating resting energy expenditures in mechanically-ventilated Thai patients that has the best accuracy and correlation with the gold standard REE measured from the indirect calorimeter.