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Record W2087758209 · doi:10.1097/ccm.0b013e31824e18a8

Nutritional practices and their relationship to clinical outcomes in critically ill children—An international multicenter cohort study*

2012· article· en· W2087758209 on OpenAlexaff
Nilesh M. Mehta, Lori J. Bechard, Naomi E. Cahill, Miao Wang, Andrew G. Day, Christopher Duggan, Daren K. Heyland

Bibliographic record

VenueCritical Care Medicine · 2012
Typearticle
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsKingston General Hospital
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsMedicineIntensive care unitParenteral nutritionIntensive carePediatric intensive care unitMechanical ventilationMalnutritionEnteral administrationProspective cohort studyCohort studyBody mass indexCohortConfoundingPediatricsIntensive care medicineEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine factors influencing the adequacy of energy and protein intake in the pediatric intensive care unit and to describe their relationship to clinical outcomes in mechanically ventilated children. DESIGN, SETTING, PATIENTS: We conducted an international prospective cohort study of consecutive children (ages 1 month to 18 yrs) requiring mechanical ventilation longer than 48 hrs in the pediatric intensive care unit. Nutritional practices were recorded during the pediatric intensive care unit stay for a maximum of 10 days, and patients were followed up for 60 days or until hospital discharge. Multivariate analysis, accounting for pediatric intensive care unit clustering and important confounding variables, was used to examine the impact of nutritional variables and pediatric intensive care unit characteristics on 60-day mortality and the prevalence of acquired infections. MAIN RESULTS: 31 pediatric intensive care units in academic hospitals in eight countries participated in this study. Five hundred patients with mean (SD) age 4.5 (5.1) yrs were enrolled and included in the analysis. Mortality at 60 days was 8.4%, and 107 of 500 (22%) patients acquired at least one infection during their pediatric intensive care unit stay. Over 30% of patients had severe malnutrition on admission, with body mass index z-score >2 (13.2%) or <-2 (17.1%) on admission. Mean prescribed goals for daily energy and protein intake were 64 kcals/kg and 1.7 g/kg respectively. Enteral nutrition was used in 67% of the patients and was initiated within 48 hrs of admission in the majority of patients. Enteral nutrition was subsequently interrupted on average for at least 2 days in 357 of 500 (71%) patients. Mean (SD) percentage daily nutritional intake (enteral nutrition) compared to prescribed goals was 38% for energy and 43% (44) for protein. A higher percentage of goal energy intake via enteral nutrition route was significantly associated with lower 60-day mortality (Odds ratio for increasing energy intake from 33.3% to 66.6% is 0.27 [0.11, 0.67], p = .002). Mortality was higher in patients who received parenteral nutrition (odds ratio 2.61 [1.3, 5.3], p = .008). Patients admitted to units that utilized a feeding protocol had a lower prevalence of acquired infections (odds ratio 0.18 [0.05, 0.64], p = .008), and this association was independent of the amount of energy or protein intake. CONCLUSIONS: Nutrition delivery is generally inadequate in mechanically ventilated children across the world. Intake of a higher percentage of prescribed dietary energy goal via enteral route was associated with improved 60-day survival; conversely, parenteral nutrition use was associated with higher mortality. Pediatric intensive care units that utilized protocols for the initiation and advancement of enteral nutrient intake had a lower prevalence of acquired infections. Optimizing nutrition therapy is a potential avenue for improving clinical outcomes in critically ill children.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.464
Teacher spread0.378 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations468
Published2012
Admission routes1
Has abstractyes

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