Abstract 13298: Racial and Ethnic Variation in ECMO Use, Failure to Rescue, & Mortality in Pediatric Cardiac ICU Patients: A Multicenter Cohort Study From the Pediatric Cardiac Critical Care Consortium Registry
Bibliographic record
Abstract
Introduction: Previous studies have reported racial disparities in ECMO use. Using a clinical registry, we sought to determine if there was racial & ethnic variation in ECMO use &, if so, whether outcomes were mediated by differences in ECMO use. Methods: Multicenter, retrospective cohort study of the Pediatric Cardiac Critical Care Consortium (PC 4 ) clinical registry. Racial/ethnic categories included White, Black, Hispanic, Asian, & other race (Native American, Pacific Islander, & mixed race). Analyses were stratified by hospitalization type (medical vs. surgical). Multivariable logistic regression models adjusted for measurable confounders & evaluated the association between race/ethnicity with ECMO use, failure to rescue (FTR), & mortality. Analyses of outcomes were adjusted for ECMO use. Secondary analyses explored interactions between race/ethnicity, insurance, & socioeconomic status with ECMO use & outcomes. Results: From 8/1/14-1/31/19, 50,552 hospitalizations from 34 hospitals were studied. Across all hospitalizations, 2.9% (n=1467) included ECMO. In both surgical & medical hospitalizations, Black race & Hispanic ethnicity were associated with severity of illness proxies. In medical hospitalizations, race & ethnicity were not associated with the likelihood of ECMO use. In medical hospitalizations, other race had higher adjusted odds of FTR (aOR 1.69 95% CI 1.06-2.70 p=0.03) & higher adjusted odds of mortality (aOR 1.61 95% CI 1.22-2.12 p=0.001). For surgical hospitalizations, Black (aOR 1.24 95% CI 1.02-1.50 p=0.03) & other race (aOR 1.50 95% CI 1.17-1.93 p=0.001) were associated with higher adjusted odds of ECMO use compared to Whites. No significant differences were found in FTR for surgical hospitalizations but hospitalizations of Hispanics had higher adjusted odds of mortality (aOR 1.31 95% CI 1.03-1.68 p=0.03). No significant interactions were demonstrated between race/ethnicity & indicators of SES with ECMO use or outcomes. Conclusions: Black & other race were associated with increased likelihood of ECMO during surgical hospitalizations. There were racial/ethnic disparities in outcomes not explained by differences in ECMO use. Efforts to mitigate these important disparities should include other aspects of care.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".