Post‐operative fluid overload as a predictor of hospital and long‐term outcomes in a pediatric heart transplant population
Bibliographic record
Abstract
BACKGROUND: Pediatric patients undergoing heart transplant have a number of factors predisposing them to become fluid-overloaded, including capillary leak syndrome. Capillary leak and FO are associated with organ injury and may influence both short- and long-term outcomes. This study aimed to 1) determine the extent, timing, and predictors of post-operative FO and 2) investigate the association of FO with clinically important outcomes. METHODS: Between 2000 and 2012, 70 children less than 6 years old had a heart transplant at our institution. This was a secondary analysis of data from an ongoing prospective cohort study. RESULTS: FO, defined as cumulative fluid balance greater than 10% of body weight in the first 5 post-operative days, occurred in 16/70 patients (23%); 7 of these had more than 20% FO. Shorter donor ischemic time and longer cardiopulmonary bypass time were independently associated with increased risk of FO. FO >20% was a statistically significant independent predictor of mortality (P = .005), ventilation time, and PICU length of stay. There was no statistically significant association between identified neurodevelopment domains and FO. CONCLUSIONS: Our single-center experience demonstrates that FO was common after pediatric heart transplant and was associated with worse clinical outcomes. FO is a potentially modifiable factor, and research is needed to better determine risk factors and whether intervention to reduce FO can improve outcomes in pediatric heart transplant 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 | 0.001 | 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".