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Record W4399740286 · doi:10.1111/aor.14810

Nutritional status and cannula infections in pediatric patients on ventricular assist device support

2024· article· en· W4399740286 on OpenAlexaff
Mostafa Abbasi Dezfouly, Aamir Jeewa, Andrea Maurich, Osami Honjo, Tara Pidborochynski, Holger Buchholz, Jennifer Conway

Bibliographic record

VenueArtificial Organs · 2024
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsStollery Children's HospitalHospital for Sick ChildrenUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsMedicineCannulaVentricular assist deviceParenteral nutritionMalnutritionRetrospective cohort studyHeart diseaseCohortEnteral administrationOdds ratioHeart transplantationIntensive care medicineSurgeryInternal medicineTransplantationHeart failure

Abstract

fetched live from OpenAlex

BACKGROUND: Ventricular assist devices (VADs) are used to bridge pediatric patients to heart transplantation. Paracorporeal VADs require the placement of cannulas, which can create an environment for infections. We examined cannula infections in pediatric VAD patients and the role of nutritional status. METHODS: This retrospective study (2005-2021) included patients <20 years old on VAD support using Berlin Heart EXCOR® cannulas. Cannula infections were defined by a positive culture and need for antibiotic therapy. Malnutrition was defined using the American Society of Parenteral and Enteral Nutrition guidelines as well as the Michigan MTool. RESULTS: There were 76 patients with a median age at implant of 0.9 years (IQR 0.4, 3.6), 50% male, with 73.7% having non-congenital heart disease. More than one-quarter (26.3%) of patients developed a cannula infection. Higher pre-implant weight (OR = 1.93, p = 0.05), creatinine (OR = 1.02, p = 0.044), and pre-albumin (OR = 15.79, p = 0.025), as well as duration of VAD support (OR = 1.01; p = 0.003) were associated with increased odds of developing a cannula infection. There was no difference in the malnutrition parameters between those with and without an infection. CONCLUSIONS: Further exploration in a larger cohort is needed to see whether these associations remain and if the incorporation of objective measures of nutritional status at the time of infection are predictive.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.020
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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