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Record W3084494625 · doi:10.1016/j.clnu.2020.09.005

Achieving enteral nutrition during the acute phase in critically ill children: Associations with patient characteristics and clinical outcome

2020· article· en· W3084494625 on OpenAlexaff
R.D. Eveleens, Jessie M. Hulst, Barbara A. E. de Koning, J. van Brakel, Dimitris Rizopoulos, Gonzalo Garcia Guerra, Ilse Vanhorebeek, Greet Van den Berghe, Koen Joosten, Sascha Verbruggen

Bibliographic record

VenueClinical Nutrition · 2020
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsUniversity of AlbertaStollery Children's HospitalHospital for Sick Children
FundersEuropean Research CouncilKU LeuvenFonds NutsOhraFonds Wetenschappelijk OnderzoekNutricia Research FoundationVlaamse regeringEuropean Society for Clinical Nutrition and MetabolismEuropean Commission
KeywordsMedicineCritically illIntensive care medicineParenteral nutritionOutcome (game theory)Critical illnessEnteral administration

Abstract

fetched live from OpenAlex

BACKGROUND & AIMS: In the absence of methodologically sound randomized controlled trials (RCTs), current recommendations for timing and amount of enteral nutrition (EN) in critically ill children are based on observational studies. These studies have associated achievement of a higher EN intake in critically ill children with improved outcome. Inherent to the observational design of these underlying studies, thorough insight in possible confounding factors to correct for is essential. We evaluated the associations between EN intake and 1) patient and daily clinical characteristics and 2) clinical outcomes adjusted for these patient and clinical characteristics during the first week of critical illness with a multivariable mixed model. METHODS: This secondary analysis of the multicentre PEPaNIC RCT investigated a subgroup of critically ill children with daily prospectively recorded gastrointestinal symptoms and EN intake during the first week with multivariable analyses using two-part mixed effect models, including multiple testing corrections using Holm's method. These models combined a mixed-effects logistic regression for the dichotomous outcome EN versus no EN, and a linear mixed-effects model for the patients who received any EN intake. EN intake per patient was expressed as mean daily EN as % of predicted resting energy expenditure (% of EN/REE). Model 1 included 40 fixed effect baseline patient characteristics, and daily parameters of illness severity, feeding, medication and gastrointestinal symptoms. Model 2 included these patient and daily variables as well as clinical outcomes. RESULTS: Complete data were available for 690 children. EN was provided in 503 (73%) patients with a start after a median of 2 (IQR 2-3) days and a median % of EN/REE of 38.8 (IQR 14.1-79.5) over the first week. Multivariable mixed model analyses including all patients showed that admission after gastrointestinal surgery (-49%EN/REE; p = 0.002), gastric feeding (-31% EN/REE; p < 0.001), treatment with inotropic agents (-22%EN/REE; p = 0.026) and large gastric residual volume (-64%EN/REE; p < 0.001) were independently associated with a low mean EN intake. In univariable analysis, low mean EN intake was associated with new acquired infections, hypoglycaemia, duration of PICU and hospital stay and duration of mechanical ventilation. However, after adjustment for confounders, these associations were no longer present, except for low EN and hypoglycaemia (-39%EN/REE; p = 0.018). CONCLUSIONS: Several patient and clinical characteristics during the first week of critical illness were associated with EN intake. No independent associations were found between EN intake and clinical outcomes such as mortality, new acquired infection and duration of stay. These data emphasize the necessity of adequate multivariable adjustment in nutritional support research and the need for future RCTs investigating optimal EN intake.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.374
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations16
Published2020
Admission routes1
Has abstractyes

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