The Relationship of Indomethacin Exposure With Efficacy and Renal Toxicity Outcomes for Preterm Infants in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
ABSTRACT Indomethacin is commonly used in the Neonatal Intensive Care Unit (NICU) for intraventricular hemorrhage (IVH) prophylaxis and patent ductus arteriosus (PDA) treatment, yet unpredictable clinical efficacy and toxicity occur with standard weight‐based dosing. Model‐informed precision dosing (MIPD) produces individualized doses to overcome deficiencies of standard dosing. To identify an indomethacin therapeutic index for MIPD to target, exposure–response relationships (ERRs) were determined. Indomethacin pharmacokinetic and demographic data collected from preterm infants treated at two US NICUs were leveraged. The following ERRs were assessed: (1) AUC0–∞ and IVH prevention efficacy (non‐severe vs. severe), (2) AUCcourse (course start to end+12 h) and PDA treatment efficacy (success vs. failure), and (3) Cmax and renal toxicity (urine output nadir within 12 h after dose [UOPnadir]). A previously developed indomethacin population pharmacokinetic model was used to predict exposure estimates. For the ERR analyses, fixed‐effect or mixed‐effect regression models (linear or logistic) were used. Data from 83 neonates were available for analysis. The regression analyses supported a lack of an ERR for IVH prevention and PDA treatment efficacy, with only gestational age as the significant predictor of IVH severity. Cmax was a significant modulator of natural log UOPnadir and was used to simulate UOPnadir < 0.5 and < 1 mL/kg/h for renal toxicity. On average, these levels were reached with Cmax values of 22 and 14 μg/mL, respectively. Although an ERR exists for indomethacin renal toxicity, the lack of ERR for indomethacin efficacy may indicate that current dosing does not give exposures sufficiently high to observe an ERR.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".