The utility of brain biomarkers in predicting survival and neurological outcomes in pediatric patients after cardiac arrest: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cardiac arrest in children is associated with high morbidity and mortality, primarily due to neurological injury. Biomarkers linked to brain injury, released into circulation from compromised elements of the neurovascular unit, act as significant prognostic indicators in patients suffering from hypoxic-ischemic brain injury (HIBI) subsequent to the restoration of spontaneous circulation (ROSC) after pediatric cardiac arrest. The aim of this systematic review and meta-analysis is to evaluate the prognostic utility of brain injury biomarkers in predicting neurological outcomes and survival in patients following cardiac arrest in the pediatric population. METHODS: Bibliographic databases (PubMed, the Cochrane Library, and Embase) were searched from their inception to November 2024. A random-effect model was used for all analyses. RESULTS: Our meta-analysis demonstrates significant associations between various biomarkers and survival or neurological outcomes after cardiac arrest. Neuron-specific enolase (NSE) levels were consistently elevated in non-survivors and patients with unfavorable neurological outcomes, with pronounced differences observed on Days 2 and 3 (e.g., Day 3 mean difference: -88.48, 95%CI: -146.77 to -30.19, P = 0.003). Emerging biomarkers, including UCH-L1 and GFAP, showed striking differences, such as elevated UCH-L1 levels on Day 1 (mean difference: -415.41, 95%CI: -474.41 to -356.61, P < 0.001) and GFAP levels exceeding 4000 ng/mL in non-survivors on Day 2 (P < 0.001). CONCLUSIONS: Our findings underscore the significant prognostic value of biomarkers in predicting survival and neurological outcomes following cardiac arrest. Neuron-specific enolase (NSE) consistently demonstrated its reliability across multiple time points, while emerging biomarkers like UCH-L1 and GFAP showed promising potential for early outcome stratification.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".