A Systematic Review and Meta‐Analysis of the Role of Peripheral Inflammation in Delirium
Bibliographic record
Abstract
INTRODUCTION: The pathophysiology of delirium is poorly understood, but the importance of inflammation is widely accepted. The objective was to investigate whether changes in the peripheral immune system have been associated with the development of delirium in hospitalized adults. METHODS: Embase and MEDLINE databases were searched. Risk of bias was assessed using the Newcastle-Ottawa Scale. Eligible studies were split into three groups: Peripheral immune response was measured (1) preceding delirium, (2) during delirium, and (3) in both incident and prevalent delirium. Quantitative data was extracted and included in the meta-analysis; otherwise, studies were included in the qualitative analysis. (Prospero 102931) RESULTS: 149 records were included in the qualitative synthesis and 92 in the meta-analysis. Measured preceding delirium, there was the strongest evidence for higher neutrophil-to-lymphocyte ratio (NLR) (mean difference (MD) 1.00, 95% Confidence Interval (CI) 0.53, 1.48, p < 0.00001) in those that developed delirium compared to those that did not. During delirium there was strongest evidence for higher interleukin-6 (IL-6) (MD 21.29, 95% CI 11.78, 30.80, p < 0.00001), cortisol (MD 159.6, 95% CI 120.52, 198.68, p < 0.00001), and leukocyte count (MD 0.79, 95% CI 0.51-1.07, p < 0.000001) in delirium compared to no delirium. DISCUSSION: These results support a role for peripheral immune response and inflammation in delirium. However, the heterogeneity of the condition was reflected in the meta-analysis, and we should be cautious extrapolating these results to specific populations. Many studies measured the same soluble markers of inflammation, and a new era of delirium research is needed that transcends this to better understand the condition and develop future treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".