A simplified equation for total energy expenditure in mechanically ventilated critically ill patients / Ecuación simplificada para el cálculo del gasto energético total en pacientes críticos con ventilación mecánica

Introduction: 'tight calorie control' concept arose to avoid over- and under-feeding of patients. Objective: to describe and validate a simplified predictive equation of total energy expenditure (TEE) in mechanically ventilated critically ill patients. Methods: this was a secondary analysis of measurements of TEE by indirect calorimetry in critically ill patients. Patients were allocated in a 2:1 form by a computer package to develop the new predictive equation TEE (prediction cohort) and the validation cohort. Indirect calorimetry was performed with three different calorimeters: the Douglas-bag, a metabolic computer and the Calorimet®. We developed a new TEE predictive equation using measured TEE (in kcal/kg/d) as dependent variable and as independent variables different factors known to influence energy expenditure: age, gender, body mass index (BMI) and type of injury. Results: prediction cohort: 179 patients. Validation cohort: 91 patients. The equation was: TEEPE (kcal/Kg/d) = 33 - (3 x A) - (3 x BMI) - (1 x G). Where: A (age in years): ≤ 50 = 0; > 50 = 1. BMI (Kg/m2 ): 18.5 - 24.9 = 0; 25 - 29.9 = 1; 30 - 34.9 = 2;35 - 39.9 = 3. G (gender): male = 0; female = 1. The bias (95% CI) was -0.1 (-1.0 - 0.7) kcal/kg/d and the limits of agreement (± 2SD) were -8.0 to 7.8 kcal/kg/d. Predicted TEE was accurate (within 85% to 115%) in 73.6% of patients. Conclusion: the new predictive equation was acceptable to predict TEE in clinical practice for most mechanically ventilated critically ill patients (AU)

Introducción: el concepto de 'control calórico estricto' surgió para evitar la excesiva y la deficiente nutrición de los pacientes. Objetivo: describir y validar una ecuación simplificada para el cálculo del gasto energético total (GET) en pacientes críticos con ventilación mecánica. Métodos: análisis secundario de las mediciones de GET por calorimetría indirecta en pacientes críticos. Los pacientes fueron asignados de forma 2:1 por un paquete estadístico; el primer grupo se empleó para desarrollar la nueva ecuación predictiva del GET (grupo predictivo) y el segundo para validarla (grupo validación). La calorimetría indirecta se realizó con tres calorímetros diferentes: la bolsa de Douglas, un computador metabólico y el equipo Calorimet®. Hemos desarrollado la nueva ecuación predictiva del GET utilizando el GET medido (en kcal/kg/d), como variable dependiente, y como variables independientes los diferentes factores que influyen en el gasto energético: edad, género, índice de masa corporal (IMC) y tipo de lesión. Resultados: el grupo de predicción incluyó 179 pacientes y el de validación 91 pacientes. La ecuación predictiva fue: GETEP = 33 - (3 x E) - (3 x IMC) - (1 x G). Donde: E (edad en años): ≤ 50 = 0; > 50 = 1. IMC (kg / m2): 18,5- 24,9 = 0; 25-29,9 = 1; 30-34,9 = 2; 35-39,9 = 3. G (género): hombre = 0; mujer = 1. El sesgo (IC del 95%) entre el GET medido y el predicho fue de -0,1 (-1,0 a 0,7) kcal/ kg/día y los límites de acuerdo (± 2SD) fueron -8,0 a 7,8 kcal/kg/d. El GET por la ecuación predictiva fue preciso (entre el 85% y el 115%) en el 73,6% de los pacientes. Conclusiones: La nueva ecuación predictiva fue aceptable para predecir el GET de la mayoría de pacientes críticos con ventilación mecánica en la práctica clínica (AU)

A SIMPLIFIED EQUATION FOR TOTAL ENERGY EXPENDITURE IN MECHANICALLY VENTILATED CRITICALLY ILL PATIENTS.

INTRODUCTION: "tight calorie control" concept arose to avoid over- and under-feeding of patients. OBJECTIVE: to describe and validate a simplified predictive equation of total energy expenditure (TEE) in mechanically ventilated critically ill patients. METHODS: this was a secondary analysis of measurements of TEE by indirect calorimetry in critically ill patients. Patients were allocated in a 2:1 form by a computer package to develop the new predictive equation TEE (prediction cohort) and the validation cohort. Indirect calorimetry was performed with three different calorimeters: the Douglas-bag, a metabolic computer and the CalorimetR. We developed a new TEE predictive equation using measured TEE (in kcal/kg/d) as dependent variable and as independent variables different factors known to influence energy expenditure: age, gender, body mass index (BMI) and type of injury. RESULTS: prediction cohort: 179 patients. Validation cohort: 91 patients. The equation was: TEEPE (kcal/Kg/d) = 33 - (3 x A) - (3 x BMI) - (1 x G). Where: A (age in years): ≤ 50 = 0; > 50 = 1. BMI (Kg/m2): 18.5 - 24.9 = 0; 25 - 29.9 = 1; 30 - 34.9 = 2; 35 - 39.9 = 3. G (gender): male = 0; female = 1. The bias (95% CI) was -0.1 (-1.0 - 0.7) kcal/kg/d and the limits of agreement (} 2SD) were -8.0 to 7.8 kcal/kg/d. Predicted TEE was accurate (within 85% to 115%) in 73.6% of patients. CONCLUSION: the new predictive equation was acceptable to predict TEE in clinical practice for most mechanically ventilated critically ill patients.

Introducción: el concepto de "control calorico estricto" surgio para evitar la excesiva y la deficiente nutricion de los pacientes. Objetivo: describir y validar una ecuacion simplificada para el calculo del gasto energetico total (GET) en pacientes criticos con ventilacion mecanica. Métodos: analisis secundario de las mediciones de GET por calorimetria indirecta en pacientes criticos. Los pacientes fueron asignados de forma 2:1 por un paquete estadistico; el primer grupo se empleo para desarrollar la nueva ecuacion predictiva del GET (grupo predictivo) y el segundo para validarla (grupo validacion). La calorimetria indirecta se realizo con tres calorimetros diferentes: la bolsa de Douglas, un computador metabolico y el equipo CalorimetR. Hemos desarrollado la nueva ecuacion predictiva del GET utilizando el GET medido (en kcal/kg/d), como variable dependiente, y como variables independientes los diferentes factores que influyen en el gasto energetico: edad, genero, indice de masa corporal (IMC) y tipo de lesion. Resultados: el grupo de prediccion incluyo 179 pacientes y el de validacion 91 pacientes. La ecuacion predictiva fue: GETEP = 33 - (3 x E) - (3 x IMC) - (1 x G). Donde: E (edad en anos): ≤ 50 = 0; > 50 = 1. IMC (kg / m2): 18,5- 24,9 = 0; 25-29,9 = 1; 30-34,9 = 2; 35-39,9 = 3. G (genero): hombre = 0; mujer = 1. El sesgo (IC del 95%) entre el GET medido y el predicho fue de -0,1 (-1,0 a 0,7) kcal/ kg/dia y los limites de acuerdo (} 2SD) fueron -8,0 a 7,8 kcal/kg/d. El GET por la ecuacion predictiva fue preciso (entre el 85% y el 115%) en el 73,6% de los pacientes. Conclusiones: La nueva ecuacion predictiva fue aceptable para predecir el GET de la mayoria de pacientes criticos con ventilacion mecanica en la practica clinica.

Resting energy expenditure during mechanical ventilation and its relationship with the type of lesion.

BACKGROUND: Resting energy expenditure (REE) of critically ill patients is usually calculated according to basal energy expenditure obtained from Harris-Benedict equations traditionally corrected by different stress factors, resulting in a variable accuracy for the individual patient. The objective of this study was to investigate whether or not the type of lesion affects the metabolism level of critically ill patients treated with mechanical ventilation. We performed a retrospective study measuring the REE of critically ill patients with 3 different types of lesions (trauma, medical, surgical) who were treated with mechanical ventilation and sedation. Each lesion group of patients was matched with another group, differing in the type of lesion, according to gender, age, and weight. METHODS: Eighty-seven from a database of 175 critically ill patients undergoing indirect calorimetry were necessary for matching. Twenty matched pairs of patients for each of the following different type of lesion were obtained: medical vs surgical, medical vs trauma, and surgical vs trauma. RESULTS: The mean REE difference was 52 kcal/d (95% confidence interval [CI] of -136 -241 kcal/d) for the medical vs surgical group, 5 kcal/d (95% CI -236 -247 kcal/d) for the medical vs trauma group and 43 kcal/d (95% CI of -132-219 kcal/d) for the surgical vs trauma group. No statistically significant differences between groups were found in the measured REE. We did not find statistically significant differences in the measured REE of patients with and without infection. CONCLUSIONS: Critically ill patients with different types of lesion treated with mechanical ventilation have similar measured REE.