Pharmacokinetic model‐guided enoxaparin dosing in the Neonatal ICU: Retrospective cohort study to plan for prospective feasibility trial
Bibliographic record
Abstract
Traditional milligram per kilogram (mg/kg) dosing of enoxaparin in neonates frequently fails to achieve target anti-Xa levels promptly, necessitating repeated laboratory monitoring and dose adjustments. This study investigated whether a personalized dosing strategy based on predicted individual clearance and volume of distribution could improve outcomes, comparing standard-of-care (SOC) mg/kg dosing to pharmacokinetic (PK) model-informed precision dosing (MIPD). A retrospective analysis was conducted on hospitalized neonates treated with enoxaparin at less than 44 weeks postmenstrual age from 2019 to 2022. Data on demographics, drug dosing, PK model covariates, and clinical outcomes were extracted from electronic health records and analyzed using the Pumas-AI Lyv dosing tool. The primary focus was on comparing the initial SOC dose to the MIPD-recommended dose. The secondary outcome measured was the time required to achieve therapeutic anti-Xa levels. The study included 168 neonates with a median postnatal age of 15 days (range 1-149) and a median dosing weight of 3.1 kg (range: 0.82-5.2). MIPD-recommended initial doses were 20%-60% higher than SOC doses in 32% of the cases and over 60% higher in 11% of cases. Neonates who received SOC doses that were much lower than the MIPD recommendation showed the longest delays in reaching therapeutic anti-Xa levels. The results indicate that PK model-informed of enoxaparin dosing leads to higher initial dosages than SOC in neonates, potentially reducing the time to therapeutic anti-Xa levels. These findings are being utilized to define dosing limits for a prospective trial of MIPD in neonatal intensive care settings.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".