Extracorporeal Membrane Oxygenation Candidacy in Pediatric Patients Treated With Hematopoietic Stem Cell Transplant and Chimeric Antigen Receptor T-Cell Therapy: An International Survey
Bibliographic record
Abstract
INTRODUCTION: Pediatric patients who undergo hematopoietic cell transplant (HCT) or chimeric antigen receptor T-cell (CAR-T) therapy are at high risk for complications leading to organ failure and the need for critical care resources. Extracorporeal membrane oxygenation (ECMO) is a supportive modality that is used for cardiac and respiratory failure refractory to conventional therapies. While the use of ECMO is increasing for patients who receive HCT, candidacy for these patients remains controversial. We therefore surveyed pediatric critical care and HCT providers across North America and Europe to evaluate current provider opinions and decision-making and institutional practices regarding ECMO use for patients treated with HCT or CAR-T. METHODS: database. RESULTS: Two-hundred and ten participants completed the survey. Of these, 159 (76%) identified themselves as pediatric critical care physicians and 47 (22%) as pediatric HCT physicians or oncologists. The majority (99.5%) of survey respondents stated that they would consider patients treated with HCT or CAR-T therapy as candidates for ECMO support. However, pediatric critical care physicians identified more absolute and relative contraindications for ECMO than non-pediatric critical care physicians. While only 0.5% of respondents reported that they consider HCT as an absolute contraindication for ECMO, 6% of respondents stated that ECMO is contraindicated in HCT patients within their institution and only 23% have an institutional protocol or policy to guide the evaluation for ECMO candidacy of these patients. Almost half (49.1%) of respondents would accept a survival to hospital discharge of 20-30% for pediatric HCT patients requiring ECMO as adequate. CONCLUSIONS: ECMO use for pediatric patients treated with HCT and CAR-T therapy is generally acceptable amongst physicians. However, there are differences in the evaluation and decision-making regarding ECMO candidacy amongst providers across medical specialties and institutions. Therefore, multidisciplinary collaboration is an essential component in establishing practice guidelines and advancing ECMO outcomes for these patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".