Association of inflammatory biomarkers with overall survival in burn patients: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: The inflammatory response to burn injuries can lead to organ dysfunction that ultimately results in increased mortality and morbidity. This meta-analysis was conducted to determine the efficacy of inflammatory biomarkers, including the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), procalcitonin (PCT), and C-reactive protein (CRP) as predictive tools of mortality among burn patients. MATERIAL AND METHODS: The biomarker levels of survivors and non-survivors were consolidated according to guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Three main databases were searched electronically: PubMed, Web of Science, and Scopus, on December 8, 2022. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to evaluate and score the methodological quality of the included studies. The standard mean difference (SMD) with a 95% confidence interval (CI) was utilized. RESULTS: Twenty-four studies were included in our systematic review and meta-analysis, (3636 total burn patients), of whom 2878 survived. We found that deceased burn patients had elevated levels of NLR (SMD = 0.60, 95% CI; 0.19-1.00, P < 0.001), CRP (SMD = 0.80, 95% CI; 0.02-1.58, P = 0.04), and PCT (SMD = 0.85, 95% CI; 0.45-1.24, P < 0.001), compared to survivors. However, we found no association between PLR and mortality among burn patients (SMD = 0.00, 95% CI; -0.14-0.15, P < 0.001). In addition, CRP was significantly higher in non-survivors (SMD = 0.80, 95% CI; 0.02-1.58, P =0.04). Similar results were also found about PCT (SMD = 0.85, 95% CI; 0.45-1.24, P < 0.001). When we analyzed the PCT data, collected in the first 24-48 hours, we found similar results; the PCT level was significantly higher in non-survivors in the immediate postinjury-period (SMD = 0.67, 95% CI; 0.31-1.02, P < 0.001). There was no publication bias among studies on the role of NLR in burn (Egger's test P = 0.91). The based cut-off values for NLR (13), CRP (71), and PCT (1.77) yielded sensitivities of 69.2%, 100%, and 93.33%, and specificities of 76%, 72.22%, and 72.22% respectively. DISCUSSION/CONCLUSIONS: PCT is a marker of sepsis, therefore its elevated level is presumably associated with a higher incidence and severity of sepsis among non-survivors. In addition, NLR and CRP are promising biomarkers for predicting and guiding prevention against burn deaths in clinical settings.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".