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Enregistrement W1970420407 · doi:10.1111/j.1365-277x.2008.00881_42.x

Identifying the factors which influence energy deficit in the adult intensive care unit

2008· article· en· W1970420407 sur OpenAlexaboutno aff
Liesl Wandrag, Bashir Ahmad Siddiqui, Fabiana Gordon, John A. O'Flynn, Mary Hickson

Notice bibliographique

RevueJournal of Human Nutrition and Dietetics · 2008
Typearticle
Langueen
DomaineMedicine
ThématiqueIntensive Care Unit Cognitive Disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineIntensive care unitSedationMechanical ventilationAPACHE IIEnergy requirementVentilation (architecture)Parenteral nutritionAnesthesiaEmergency medicinePediatricsIntensive care medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,047
Tête enseignante GPT0,314
Écart entre enseignants0,267 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2008
Routes d'admission1
Résumé présentoui

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