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Record W7133095890

Improving Precision of Vancomycin Dosing in Neonatal Sepsis based on Clinical Outcome Evaluation and Population Pharmacokinetics

2023· dissertation· W7133095890 on OpenAlexfundno aff
Erin Chung

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsnot available
FundersHospital for Sick ChildrenUniversity of Toronto
KeywordsVancomycinDosingConfidence intervalOdds ratioPopulationTherapeutic drug monitoringPharmacokineticsLogistic regressionNeonatal intensive care unit
DOInot available

Abstract

fetched live from OpenAlex

Background: Neonatal sepsis is commonly treated with vancomycin in the neonatal intensive care unit (NICU). Vancomycin dosing remains a challenge in neonates due to significant pharmacokinetic variability and unclear vancomycin target range.Objectives: This research aims to determine vancomycin target range associated with clinical outcomes and develop a better dosing strategy using population pharmacokinetics (popPK) to maximize probability to reach study-derived target range in neonates. Methods: A systematic review was conducted to summarize vancomycin popPK models in neonatal and paediatric patients. A retrospective cohort study included NICU patients receiving intravenous vancomycin. The associations between vancomycin trough concentrations and persistent/recurrent infections and mortality or acute kidney injury were assessed using logistic regression and classification and regression tree (CART) analyses. A popPK model was derived and validated using nonlinear mixed effects modelling. The predictive performance of the derived popPK model was compared against published popPK models. Monte Carlo simulations (MCS) were performed to derive optimal dosing regimens. Results: A one-compartment model incorporating weight, postmenstrual age (PMA), and serum creatinine (SCR) best described the observed data from 655 vancomycin courses in 448 neonates with highest accuracy and precision compared to other published models. A strong association between time to reach target range and composite outcomes was demonstrated (p=0.005). A vancomycin trough concentration >10 mg/L was associated with lower odds of persistent/recurrent infections (adjusted odds ratio: 0.3, 95% confidence interval (CI): 0.09-0.86, p=0.023) and >15 mg/L was associated with increased risk of acute kidney injury (adjusted hazard ratio of 2.94, 95% CI: 1.10-7.90, p=0.003). CART-derived area under the concentration-time curve over 24 hours (AUC24h) of 420-650 mg*h/L appeared to be associated with lowest risk of persistent/recurrent infections or mortality (p=0.025). MCS-derived vancomycin doses achieved >90% target attainment for trough target range of 10-15 mg/L in majority of PMA and SCR categories (78%). Conclusion: A vancomycin trough target range of 10-15 mg/L was associated with most optimal outcomes in treating neonatal sepsis, which supports using vancomycin trough concentrations for therapeutic drug monitoring in neonates. A vancomycin dosing guideline using loading dose was derived to increase probability of target attainment and time at target in neonates.

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)0.047
metaresearch head score (Gemma)0.121
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.121
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.076
GPT teacher head0.473
Teacher spread0.396 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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