Identifying the factors which influence energy deficit in the adult intensive care unit
Bibliographic record
Abstract
Background: Critically ill patients frequently receive inadequate nutrition support due to under‐ or overestimation of nutritional needs (Reid, 2006), delays in initiating nutrition support and frequent interruptions to nutritional support (Heyland et al., 2003). The purpose of this research was to identify the significant factors which influence energy deficit in the adult intensive care unit (ICU). Methods: ICU patients with a length of stay of ≥3 days were studied for 30 days over two consecutive years at a large university teaching hospital. Fifty‐six patients were studied with a total of 530 records of feeding days. The following information was collected: day feeding was initiated, age, length of stay, Acute Physiology & Chronic Health Evaluation score (APACHE II), fed within 24 h (yes/no) gender, speciality, type of ventilation (endotracheal tube (ETT), tracheostomy, non‐invasive (NIV), self), feeding route, outcome (survived/died), diarrhoea, aspirate volume, dietitian observed nutritional status (at risk or not), sedation, estimated energy requirements and energy received. The statistical method used was mixed linear models for longitudinal data with energy deficit (energy received – energy requirements) as the dependent variable. Results: The model showed that factors which significantly affected energy deficit were: day feeding was initiated (P < 0.001), whether fed within 24 h (P < 0.001) and whether sedated (P < 0.001). Energy deficit was greatest during the first week but this reduced and became more stable thereafter. Furthermore, three combined effects were found: Ventilation mode and aspirate volume (P < 0.007); in patients with larger aspirate volumes the type of ventilation really affected energy deficit, particularly with ETT. Fed within 24 h and sedation (P < 0.017); Patients who were fed within 24 h had lower energy deficits and sedation had little effect on this. However, in the group who were not fed within 24 h, sedated patients had much greater deficits than non‐sedated. Fed within 24 h and ventilation mode (P < 0.001); Patients not fed within the first 24 h and ventilated with ETT or NIV had much higher energy deficits compared to other forms of ventilation. Discussion: The time on ICU prior to commencing feeding and whether fed in the first 24 h will obviously affect overall energy deficit. Sedation can affect gastric motility and the decision to feed, which could lead to the observed effect on energy deficit. These findings confirm those of previous studies (Heyland et al., 2003; Reid, 2006). In addition this study also showed that aspirate volume, type of ventilation and sedation were involved in combined effects. A plausible explanation for these findings is that sedated and ventilated patients are the sickest patients on ICU and their nutrition support is frequently interrupted by procedures, surgery and poor feed tolerance. Interestingly baseline APACHE II score, specialty, feeding route, presence of diarrhoea and nutritional status were not included in the final model which best predicted energy deficit, probably due to correlations with the included variables. Conclusions: Day when feeding was initiated, fed within 24 h and sedation have been identified as independent factors which predict energy deficit during ICU stay. More focus can therefore be given to the patients most at risk to try and ensure that adequate energy intakes are achieved. References Heyland, D.K., Schroter‐Neppe, D., Drover, J.W., Jain, M., Keefe, L., Dhaliwal, R. & Day, A. (2003) Nutrition support in the critical care setting: current practice in Canadian ICU's – opportunities for improvement? J. Parenter. Enteral. Nutr. 27, 74–83. Reid, C. (2006) Frequency of under‐ and overfeeding in mechanically ventilated ICU patients: causes and possible consequences. J. Hum. Nutr. Diet.19, 13–22.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".