Circulating protein and lipid markers of early sepsis diagnosis and prognosis: a scoping review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Sepsis is the extreme response to infection associated with high mortality, yet reliable biomarkers for its identification and stratification are lacking. RECENT FINDINGS: Our scoping review of studies published from January 2017 to September 2022 that investigated circulating protein and lipid markers to inform non-COVID-19 sepsis diagnosis and prognosis identified interleukin (IL)-6, IL-8, heparin-binding protein (HBP), and angiopoietin-2 as having the most evidence. Biomarkers can be grouped according to sepsis pathobiology to inform biological data interpretation and four such physiologic processes include: immune regulation, endothelial injury and coagulopathy, cellular injury, and organ injury. Relative to proteins, the pleiotropic effects of lipid species' render their categorization more difficult. Circulating lipids are relatively less well studied in sepsis, however, low high-density lipoprotein (HDL) is associated with poor outcome. SUMMARY: There is a lack of robust, large, and multicenter studies to support the routine use of circulating proteins and lipids for sepsis diagnosis or prognosis. Future studies will benefit from standardizing cohort design as well as analytical and reporting strategies. Incorporating biomarker dynamic changes and clinical data in statistical modeling may improve specificity for sepsis diagnosis and prognosis. To guide future clinical decisions at the bedside, point-of-care circulating biomarker quantification is needed.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".