Ex Vivo Endotoxin Stimulation of Blood for Predicting Survival in Patients With Sepsis
Bibliographic record
Abstract
BACKGROUND: Sepsis is a syndrome characterized by host immune dysfunction, with the extent of immunoparalysis differing among patients. Lipopolysaccharide (LPS) is used commonly to assess the immune function of critically ill patients with sepsis. However, the reliability of this ex vivo diagnostic test in predicting clinical outcomes remains uncertain. RESEARCH QUESTION: Does LPS-induced tumor necrosis factor (TNF) production from the blood of patients with sepsis predict mortality? Secondary outcomes included ICU and hospital stay durations, nosocomial infection rate, and organ recovery rate. STUDY DESIGN AND METHODS: Human sepsis studies from various databases through April 2023 were evaluated. Inclusion criteria encompassed LPS-stimulated blood assays, English language, and reported clinical outcomes. Bias risk was evaluated using the Newcastle-Ottawa scale (NOS). Relationships between TNF production and mortality were analyzed at sepsis onset and during established sepsis, alongside secondary outcomes. RESULTS: Of 11,580 studies, 17 studies (14 adult and three pediatric) were selected for analysis. Although 15 studies were evaluated as moderate to high quality using the NOS, it is important to note that some of these studies also had identifiable biases, such as unclear methods of participant recruitment. Nine studies detailed survival outcomes associated with LPS-induced TNF production at sepsis onset, whereas five studies explored TNF production's relationship with mortality during established sepsis. Trends suggested that lower LPS-induced TNF production correlated with higher mortality. However, heterogeneity in methodologies, especially the LPS assay protocol, hindered definitive conclusions. Publication bias was highlighted using funnel plot analysis. Concerning secondary outcomes, diminished TNF production might signify worsening organ dysfunction, although the link between cytokine production and nosocomial infection varied among studies. INTERPRETATION: For functional immune profiling in sepsis, streamlined research methodologies are essential. This entails organizing cohorts based on microbial sources of sepsis, establishing standardized definitions of immunoparalysis, using consistent types and dosages of immune stimulants, adhering to uniform blood incubation conditions, and adopting consistent clinical outcomes.
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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 | 0.049 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".