Association between fluid overload and mortality in children with sepsis: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Sepsis is one of the main causes of morbidity and mortality worldwide. Fluid resuscitation is among the most common interventions and is associated with fluid overload (FO) in some patients. The objective of this systematic review and meta-analysis was to summarise the available evidence on the association between FO and morbimortality in children with sepsis. Methods A systematic search was carried out in PubMed/Medline, Embase, Cochrane and Google Scholar up to December 2022 (PROSPERO 408148), including studies in children with sepsis which reported more than 10% FO 24 hours after admission to intensive care. The risk of bias was assessed using the Newcastle-Ottawa scale. Heterogeneity was assessed using I2, considering it absent if <25% and high if >75%. A sensitivity analysis was run to explore the impact of the methodological quality on the size of the effect. Mantel-Haenszel’s model of random effects was used for the analysis. The primary outcome was to determine the risk of mortality associated with FO and the secondary outcomes were the need for mechanical ventilation (MV), multiple organ dysfunction syndrome (MODS) and length of hospital stay associated with FO. Results A total of 9 studies (2312 patients) were included, all of which were observational. Children with FO had a higher mortality than patients without overload (46% vs 26%; OR 5.06; 95% CI 1.77 to 14.48; p<0.01). We found no association between %FO and the risk of MODS (OR: 0.97; 95% CI 0.13 to 7.12; p=0.98). Children with FO required MV more often (83% vs 47%; OR: 4.78; 95% CI 2.51 to 9.11; p<0.01) and had a longer hospital stay (8 days (RIQ 6.5–13.2) vs 7 days (RIQ 6.1–11.5); p<0.01). Conclusion In children with sepsis, more than 10% FO 24 hours after intensive care admission is associated with higher mortality, the need for MV and length of hospital stay.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".