Critical Illness
Conditions
Brief summary
Currently there are no study related to Indirect Calorimetry (IC) has been done among hospitalised Malaysian ICU adult patients with its racial mix. The aim of this study is to perform a cross-sectional study in Malaysian critically ill patients to determine metabolic determinants that might influence resting energy expenditure (REE) and to develop predictive equation for the estimation of energy requirement using the regression based approach to increase the accuracy in calorie prescriptions. In addition, expected outcome of this study is to determine which equations have clinical usefulness among Malaysian adult critically ill patients and hope to introduce into routine clinical practice in the future if IC is not available.
Detailed description
Nutrition provision in the clinical setting relies heavily on the accurate estimation of energy and protein requirements. This can be done in a quick and inexpensive manner via the use of predictive equations. Some of the most popularly used predictive equations such as the Harris-Benedict equation and the Mifflin-St. Jeor equation have been widely applied within the clinical setting to estimate energy requirements among mechanically ventilated critically ill patients. However, these existing equations were not specially developed for a population with disease, as the equations were derived from a pool of healthy Caucasian adults. In addition, most of the equations for critically ill patients such as the Penn State equation, Faisy equation and Raurich Equation developed and validated among Caucasian in western country and not among Asian population. Therefore, their accuracy in predicting energy requirement is questionable when applied within Malaysian mechanically ventilated critically ill patients with its racial mix.
Interventions
DEVICEIndirect Calorimetry
REE measurements were using IC (Cosmed, Quark RMR 2.0, Indirect Calorimetry Lab, Italy). A standard protocol for conducting the measurement was followed (Schlein & Coulter, 2014);(P. Singer & Singer, 2016); (Taku Oshima et al., 2016). Before each measurement, the metabolic monitor was allowed to warm up for 30 min, and then gas and flowmeter calibrations were performed by an experienced dietitian or healthcare professional. The REE was recorded after a 30 min non-fasting steady state according to RMR protocol and manufacturer instructions.
Sponsors
University of Malaya
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Adult patients aged over 18 years old 2. Critically ill patients with mechanically ventilated 3. Expected to have an ICU stay of more than 5 days 4. Patients had implemented for continuous enteral or parenteral nutrition support.
Exclusion criteria
1. Requirement for inspired oxygen content (FiO2) greater than 0.6 2. Patients on high frequency ventilation 3. Patients with chest tubes that leak air 4. Patients with incompetent tracheal cuff 5. Patients inhaled nitric oxide therapy 6. Patients receiving intermittent hemodialysis and continuous renal replacement therapy (CRRT) during IC measurement 7. Patients with pregnancy 8. Patients with burn injury 9. Patients infected with human immunodeficiency virus (HIV) 10. Patients with severe liver disease (Child-Pugh score C) 11. Patients with post open heart surgery 12. Patients with paraplegia and quadriplegia
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of participants measured resting energy expenditure for the development of predictive equations | 24 months | predictive equations for the estimation of energy requirement among mechanically ventilated critically ill patients among Malaysian population. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Determine metabolic determinants | 24 months | metabolic determinants that might influence resting energy expenditure among mechanically ventilated critically ill patients. |
| The best regression equation model | 24 months | Regression equation model for predicting energy requirement of mechanically ventilated critically ill patients. |
| Determine and compare REE measured by IC among mechanically ventilated critically ill patients | 24 months | during early phase (staying ≤ 5 days), late phase (staying 6-10 days) and chronic phase (staying \> 10 days) in ICU. |
| The validity of several predictive equations by using Intraclass Correlation Coefficient (ICC) test | 24 months | predictive equations used to predict REE in critically ill adult patients among Malaysian population by using indirect calorimetry (IC) as the reference standard. |
| The association of REE in critically ill patients with quality of life | 24 months | Questionnaire SF-36v2 Health Survey to measure quality of life for critically ill patients. |
| The association of REE in critically ill patients with nutrition risk | 24 months | NUTRIC score to quantify the nutrition risk of critically ill patients developing adverse events |
| The energy and protein adequacy in relation to patient outcome. | 24 months | Energy and protein adequacy in terms of Energy/Nitrogen ratio in relation to patient outcome. |
| The association of REE in critically ill patients with clinical outcome | 24 months | Clinical outcome are hospital mortality and ICU mortality in 28 days and 60 days, length of mechanical ventilation in hours, duration of ICU stay in days and infectious complications such as Hospital acquired infection. |
Countries
Malaysia
Contacts
Primary ContactPei Chien Tah
Backup ContactPei Chien Tah
Outcome results
None listed