Metabolic Determinants Of Resting Energy Expenditure Among Mechanically Ventilated Critically Ill Patients

• Conditions: Critical Illness
• Interventions: Device: Indirect Calorimetry

• Registration Number: NCT03319329
• Lead Sponsor: University of Malaya

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.

Study Timeline

• Study Start: 2017-02-13
• Study First Submitted: 2017-09-23
• Status Verified: 2017-10
• Study First Submitted QC: 2017-10-22
• Last Update Submitted: 2017-10-22
• Study First Posted: 2017-10-24
• Last Update Posted: 2017-10-24
• Primary Completion: 2020-12-31
• Study Completion: 2022-12-31

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 314

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
critically ill adult patients	Indirect Calorimetry	Part I: A cross-sectional study to compare validity of several predictive equations used to predict REE in critically ill adult patients for staying ≤ 5 days, 6 - 10 days and \> 10 days by using indirect calorimetry (IC) as the reference standard. Part II: To develop predictive equation for the estimation of energy requirement by identifying variables that might influence REE of mechanically ventilated critically ill patients. Part III: To validate the newly developed predictive equation for the estimation of energy requirement by using Ten fold cross-validation approach

Primary Outcome Measures

Name	Time	Method
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 Outcome Measures

Name	Time	Method
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.
Determine metabolic determinants	24 months	metabolic determinants that might influence resting energy expenditure among mechanically ventilated 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 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.
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 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.

Trial Locations

Locations (1)

University of Malaya Medical Centre; 🇲🇾 Kuala Lumpur, Wilayah Persekutuan, Malaysia