Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 2:09 AM
Ignite Modification Date:
2025-12-25 @ 2:09 AM
NCT ID:
NCT05124860
Brief Summary:
The metabolic alterations associated with critical illness have significant implications for the nutritional management of ICU patients. Despite this, little is known about these changes in patients requiring prolonged organ support and nutritional therapy.
The overall aim of this study is to describe changes in metabolism over time in a large prospective cohort of patients requiring \>10 days of ICU care. Our hypothesis is that there is a significant change in mean energy expenditure and respiratory quotient (RQ) between the early (day 1-3), intermediate (day 4-10) and late (\>10 days) phase in ICU.
Detailed Description:
Background
Critical illness has profound effects on human metabolism. The most prominent feature in the early phase is an upregulation of catabolic pathways, which promotes the production of endogenous energy substrates and net protein breakdown \[1\].
There is very little published data describing trends of energy expenditure and substrate utilization in patients with a prolonged ICU stay. While this group only constitutes a small fraction of ICU patients, it accounts for a large part of ICU resource allocation, morbidity and mortality \[2\]. Several studies have been conducted in recent years to better characterize patients with persistent critical illness, focusing on markers of catabolism and inflammation \[3, 4\]. It is not known if these changes are associated with alterations in energy metabolism and substrate utilization.
Bridging these knowledge gaps will improve our understanding of the nutritional needs and metabolism of patients beyond the early phase in ICU. We therefore plan to conduct a prospective observational multi-center study to address these questions.
Aim and hypothesis
The overall aim of this project is to describe longitudinal changes in energy expenditure and associated clinical characteristics in a large cohort of patients with a prolonged ICU stay. Our hypothesis is that there is a significant change in mean energy expenditure and respiratory quotient (RQ) between the early (day 1-3), intermediate (day 4-10) and late (\>10 days) phase in ICU. Correlations between metabolic rate and other clinical characteristics will also be analysed for hypothesis-generating purposes.
Population
All adult ICU patients with at least one measurement of energy expenditure by indirect calorimetry at participating study sites will be included in the study. Study sites are encouraged to routinely perform indirect calorimetry every 3-4 days. Study subjects will be followed until ICU discharge or death, whichever comes first.
Data collection and reporting
Patient data will be reported pseudonymized through a secure online form.
On admission
* Admission date
* Admission diagnosis (ICD-10)
* Surgery prior to admission (YES/NO), elective or emergent
* Outcome prediction score (SAPS 3, APACHE III/IV, MPM, etc.) and risk of death on admission (%)
* ICU source admission (ER/ward/OT/other ICU)
* Days in hospital before ICU admission
Demographic and anthropometric data:
* Sex (male/female)
* Age (years)
* Weight (kg)
* Height (cm)
Chronic comorbidities registered in electronic health records (YES/NO):
* Hypertension
* Ischemic heart disease
* Heart failure
* Diabetes mellitus
* COPD
* Chronic kidney disease
* End-stage renal disease
* Liver cirrhosis
* Active cancer (not in complete remission)
* Haematological malignancy
* Solid organ transplant
On the day of each indirect calorimetry
* REE (kcal/24 h), RQ, VO2 (ml/min), VCO2 (ml/min) and date of investigation
* Invasive mechanical ventilation (YES/NO) or renal replacement therapy (YES/NO)
If YES to invasive mechanical ventilation:
* Fraction of inspired oxygen
* Positive end-expiratory pressure (cmH2O)
Factors that may influence REE:
* Sequential organ failure assessment (SOFA) score
* Fever (≥38.5 ℃) within 2h of measurement (YES/NO/MISSING)
* Richmond Agitation-Sedation Scale score
Results of daily blood tests if available from routine testing:
* P-CRP (mg/L)
* P-albumin (g/L)
* P-urea (mmol/L)
* P-creatinine (μmol/L)
* Haemoglobin (g/L)
Medications, nutrition and other therapies:
* Infusions of vasoactive medications (YES/NO, if YES → name of medication(s))
* Infusions of sedatives or analgesics (YES/NO, if YES → name of medication(s), if propofol → infusion rate at time of measurement)
* Infusions of parenteral and/or enteral nutrition (YES/NO, if YES → brand name, formulation and rate at time of measurement)
On discharge
* Discharge date
* Survival status (ALIVE/DEAD)
* Sepsis during ICU stay (NO/SEPSIS/SEPTIC SHOCK)
Sample size considerations
The goal of this study is to include ≥200 patients with an ICU length of stay of \>10 days. Based on data from the Swedish Intensive Care Registry between 2015-2019, these patients accounted for 5% of all ICU admissions \[5\]. This proportion is comparable to results from a registry study conducted in Australia and New Zealand of over one million ICU admissions \[2\]. Based on these figures we intend to screen 6000 unique patients for study participation, accounting for the possibility that multiple measurements of indirect calorimetry are not consistently performed. In total we expect to include around 1250 unique subjects with at least one measurement with indirect calorimetry.
Statistics
Descriptive data will be presented as mean +/- standard deviation or median (interquartile range) as appropriate. The primary and secondary outcome measures will be analysed using a generalized linear mixed-effects model. Exploratory outcomes and their association to other clinical variables will be analysed using generalized linear regression models. If values are found to be not missing at random, conditional logistic regression censoring will be used to calculate inverse probability weights for accounting for difference in drop-out probabilities.
Study:
NCT05124860
Study Brief:
Protocol Section:
NCT05124860