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 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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".