Search

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. [ABSTRACT FROM AUTHOR]

Introduction: Introduction: optimal nutrition in the critically ill patient is a key aspect for recovery. Objectives: to promote training in and knowledge of mixed nutrition support (MNS) by means of a clinical algorithm among intensivists for improving the nutritional status of critically ill patients. Methods: a before-and-after study with the participation of 19 polyvalent intensive care units (ICUs) in 10 autonomous communities. Five members of the scientific committee trained the trainers by means of oral presentations and a clinical algorithm on MNS. Then, trainers were responsible for explaining the algorithm to local intensivists in their ICUs. The 30-item study questionnaire was completed before and after the intervention by 179 and 105 intensivists, respectively. Results: a clear improvement of knowledge was found in six (20 %) specific MNS-related questions. In 11 items (36.6 %), adequate knowledge on different aspects of nutritional support that were already present before the intervention were maintained, and in five items (16.7 %) an improvement in the rate of correct responses was recorded. There were no improvements in correct responses for four items (13.3 %), and for four (13.3 %) additional items the percentage of correct responses decreased. Conclusions: the use of the MNS algorithm has achieved a solid consolidation of the main concepts of MNS. Some aspects regarding how to manage the malnourished patient, how to identify them and what type of nutrition to guide from the beginning of admission to the ICU, nutritional contributions in special situations, and the monitoring of possible complications such as refeeding are areas for which further training strategies are needed. [ABSTRACT FROM AUTHOR]