Biomarkers for the Early Diagnosis of Sepsis in Burns: Systematic Review and Meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the diagnostic performance of all biomarkers studied to date for the early diagnosis of sepsis in hospitalized patients with burns. BACKGROUND: Early clinical diagnosis of sepsis in burns patients is notoriously difficult due to the hypermetabolic nature of thermal injury. A considerable variety of biomarkers have been proposed as potentially useful adjuncts to assist with making a timely and accurate diagnosis. METHODS: We searched Medline, Embase, Cochrane CENTRAL, Biosis Previews, Web of Science, and Medline In-Process to February 2020. We included diagnostic studies involving burns patients that assessed biomarkers against a reference sepsis definition of positive blood cultures or a combination of microbiologically proven infection with systemic inflammation and/or organ dysfunction. Pooled measures of diagnostic accuracy were derived for each biomarker using bivariate random-effects meta-analysis. RESULTS: We included 28 studies evaluating 57 different biomarkers and incorporating 1517 participants. Procalcitonin was moderately sensitive (73%) and specific (75%) for sepsis in patients with burns. C-reactive protein was highly sensitive (86%) but poorly specific (54%). White blood cell count had poor sensitivity (47%) and moderate specificity (65%). All other biomarkers had insufficient studies to include in a meta-analysis, however brain natriuretic peptide, stroke volume index, tumor necrosis factor (TNF)-alpha, and cell-free DNA (on day 14 post-injury) showed the most promise in single studies. There was moderate to significant heterogeneity reflecting different study populations, sepsis definitions and test thresholds. CONCLUSIONS: The most widely studied biomarkers are poorly predictive for sepsis in burns patients. Brain natriuretic peptide, stroke volume index, TNF-alpha, and cell-free DNA showed promise in single studies and should be further evaluated. A standardized approach to the evaluation of diagnostic markers (including time of sampling, cut-offs, and outcomes) would be useful.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".