Fluid Balance Management Informs Renal Replacement Therapy Use During Pediatric Extracorporeal Membrane Oxygenation: A Survey Report From the Kidney Intervention During Extracorporeal Membrane Oxygenation Group
Bibliographic record
Abstract
Fluid overload (FO) and acute kidney injury (AKI) occur commonly in children supported with extracorporeal membrane oxygenation (ECMO). Continuous renal replacement therapy (CRRT) may be used to manage AKI and FO in children on ECMO. In 2012, our group surveyed ECMO centers to begin to understand the practice patterns around CRRT and ECMO. Since then, more centers are initiating ECMO for increasingly diverse indications and an increased volume of research quantifies the detrimental impacts of AKI and FO. We, therefore, investigated practice patterns of CRRT utilization during ECMO in children. A multi-point survey instrument was distributed to 116 international neonatal and pediatric ECMO centers. Sixty of 116 (51.7%) international neonatal and pediatric ECMO centers responded. All reports using CRRT on ECMO, compared with 75% from the 2012 survey. Eighty-five percent use CRRT to treat or prevent FO, an increased from 59%. The modality of CRRT therapy differed between in-line (slow continuous ultrafiltration, 84.4%) and machine-based (continuous venovenous hemodiafiltration, 87.3%) methods. Most (65%) do not have protocols for fluid management, AKI, or CRRT on ECMO. Trialing off CRRT is dictated by physician preference in 90% (54/60), with varying definitions of success. In this survey study, we found that CRRT use during pediatric ECMO has increased since 2012 with fluid management representing the predominant indication for initiation. Despite the expanded utilization of CRRT with ECMO, there remains significant practice variation in terms of method, modality, indication, the timing of initiation, fluid management, and discontinuation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".