Nutritional status and cannula infections in pediatric patients on ventricular assist device support
Bibliographic record
Abstract
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.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".