Pharmacokinetics of Sirolimus-Eluting Stents Implanted in the Neonatal Arterial Duct
Bibliographic record
Abstract
BACKGROUND: Sirolimus-eluting stents may have clinical advantages over bare-metal stents in the extremely proliferative environment of the neonatal arterial duct. However, sirolimus has immunosuppressive actions and little is known regarding sirolimus pharmacokinetics in the newborn. METHODS AND RESULTS: This is a retrospective review of sirolimus pharmacokinetics in neonates who underwent sirolimus-eluting stent implantation in the arterial duct for pulmonary blood flow augmentation. Pharmacokinetic parameters were obtained by noncompartmental analysis and by a Bayesian one-compartment nonlinear mixed model. Nine neonates received a single sirolimus-eluting stent with a total sirolimus dose of 245 μg (n = 1), 194 μg (n = 5), or 143 μg (n = 3). Peak sirolimus concentrations were 13.6 ± 4.5 μg/L (24.8 μg/L highest) and clearance was 0.042 ± 0.03 L/hour (noncompartmental analysis) and 0.051 L/hour (95% credible intervals 0.037-0.069, nonlinear mixed model). Sirolimus remained > 5 μg/L, the trough level used in oral immunosuppressive therapy, for (95% credible interval) 15.9 (11.4, 22.8), 12.9 (7.6, 19.0), and 8.4 (2.3, 14.5) days for the 245, 194, and 143 μg sirolimus dose stents, respectively. Estimates of the duration of systemic immunosuppression are provided for combinations of 2 stents. CONCLUSIONS: In neonates after sirolimus-eluting stent implantation, peak sirolimus levels were 20 × higher and clearance 30 × lower than previously reported in older children and adults. Sirolimus levels were within the immunosuppressive range for a prolonged period, but with no observable clinically significant adverse outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".