UTILITY OF PRE-HOSPITAL LACTATE MEASUREMENT FOR TRAUMA PATIENTS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background Serum lactate serves as a surrogate marker for tissue hypoxia following traumatic injury, and may be used to guide resuscitation. The portable, lightweight nature of handheld point-of-care monitoring devices enables lactate values to be readily available in the pre-hospital environment, whether in land or air based evacuation modalities. The current review evaluates the utility of pre-hospital lactate measurement in the management of trauma patients. Methods The published literature up to 06 August 2015 was searched using Medline and EMBASE using pre-defined criteria (English language, pre-hospital lactate measurement, trauma patients; excluding non-trauma/mixed patient groups). The Newcastle-Ottawa Scale was used to assess risk of bias of individual studies. Results Of 857 non-duplicate articles of interest, 5 articles met the inclusion criteria, and included four cohort studies and one cross-sectional study. There were 2049 patients included in all studies. Key findings ▸ Pre-hospital lactate may be an independent prognostic marker of in-hospital mortality, multiple organ dysfunction, and requirement for transfusion or surgical intervention in trauma patients, particularly in blunt trauma. ▸ Pre-hospital lactate measurement may be more sensitive than systolic blood pressure in determining need for resuscitative care. ▸ Early lactate measurement may be particularly useful in the detection of occult hypotension, with elevated levels detectable within 30 minutes of injury. ▸ All current studies that investigate pre-hospital lactate were assessed as being at risk of bias. Conclusions There is a paucity of evidence relating to pre-hospital lactate-guided management in trauma. From the limited literature, it seems that pre-hospital lactate may be a useful early tool in guiding the management of trauma patient resuscitation. Further prospective studies are required to elucidate the sensitivity and specificity of abnormal pre-hospital lactate values.
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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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".