Pharmacokinetic Research in Pediatric Extracorporeal Therapies: Current State and Future Directions
Bibliographic record
Abstract
Extracorporeal life support (ECLS), including extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT), are life-saving therapies for critically ill children. Despite this, these modalities carry frustratingly high mortality rates. One driver of mortality may be altered drug disposition due to a combination of underlying illness, patient-circuit interactions, and drug-circuit interactions. Children receiving ECMO and/or CRRT routinely receive 20 or more drugs, and data supporting optimal dosing is lacking for most of these medications. The Pediatric Paracorporeal and Extracorporeal Therapies Summit (PPETS) gathered an international group of experts in the fields of ECMO, CRRT, and other ECLS modalities to discuss the current state of these therapies, disseminate innovative support strategies, share clinical experiences, and foster future collaborations. Here, we summarize the conclusions of PPETS and put forward a pathway to optimize pharmacokinetic (PK) research in this population. We must prioritize specific medications for in-depth study to improve drug use in ECLS and patient outcomes. Based on frequency of use, potential for adverse outcomes if dosed inappropriately, and lack of existing PK data, a list of high priority drugs was compiled for future research. Researchers must additionally reconsider study designs, emphasizing pooling of resources through multi-center studies and the use of innovative PK modeling techniques. Finally, the integration of validated PK models into clinical practice must be streamlined to deliver optimal medication use at the bedside. Focusing on the proposed list of highlighted medications and key methodological considerations will maximize the impact of future research.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".