Association between Serum Lactate and Morbidity and Mortality in Neonates: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Lactate is a marker of hypoperfusion in critically ill patients. Whether lactate is useful for identifying and stratifying neonates with a higher risk of adverse outcomes remains unknown. This study aimed to investigate the association between lactate and morbidity and mortality in neonates. METHODS: A meta-analysis was performed to determine the association between blood lactate levels and outcomes in neonates. Ovid MEDLINE, EMBASE, Cochrane Library, and ClinicalTrials.gov were searched from inception to 1 May 2021. A total of 49 observational studies and 14 data accuracy test studies were included. The risk of bias was assessed using the Newcastle-Ottawa Scale for observational studies and the QUADAS-2 tool for data accuracy test studies. The primary outcome was mortality, while the secondary outcomes included acute kidney injury, necessity for renal replacement therapy, neurological outcomes, respiratory morbidities, hemodynamic instability, and retinopathy of prematurity. RESULTS: Of the 3184 articles screened, 63 studies fulfilled all eligibility criteria, comprising 46,069 neonates. Higher lactate levels are associated with mortality (standard mean difference, -1.09 [95% CI, -1.46 to -0.73]). Using the estimated sensitivity (0.769) and specificity (0.791) and assuming a prevalence of 15% for adverse outcomes (median of prevalence among studies) in a hypothetical cohort of 10,000 neonates, assessing the lactate level alone would miss 346 (3.46%) cases (false negative) and wrongly diagnose 1776 (17.76%) cases (false positive). CONCLUSIONS: Higher lactate levels are associated with a greater risk of mortality and morbidities in neonates. However, our results do not support the use of lactate as a screening test to identify adverse outcomes in newborns. Research efforts should focus on analyzing serial lactate measurements, rather than a single measurement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".