A Systematic Review and Meta-analysis Examining the Impact of Age on Perioperative Inflammatory Biomarkers
Bibliographic record
Abstract
BACKGROUND: Dysregulation of immune responses to surgical stress in older patients and those with frailty may manifest as differences in inflammatory biomarkers. We conducted a systematic review and meta-analysis to examine differences in perioperative inflammatory biomarkers between older and younger patients, and between patients with and without frailty. METHODS: MEDLINE, Embase, Cochrane, and CINAHL databases were searched (Inception to June 23, 2020). Observational or experimental studies reporting the perioperative level or activity of biomarkers in surgical patients stratified by age or frailty status were included. The primary outcome was inflammatory biomarkers (grouped by window of ascertainment: pre-op; post-op: <12 hours, 12-24 hours, 1-3 days, 3 days to 1 week, and >1 week). Quality assessment was conducted using the Newcastle-Ottawa Scale. Inverse-variance, random-effects meta-analysis was conducted. RESULTS: Forty-five studies (4263 patients) were included in the review, of which 36 were pooled for meta-analysis (28 noncardiac and 8 cardiac studies). Two studies investigated frailty as the exposure, while the remaining investigated age. In noncardiac studies, older patients had higher preoperative levels of interleukin (IL)-6 and C-reactive protein (CRP), lower preoperative levels of lymphocytes, and higher postoperative levels of IL-6 (<12 hours) and CRP (12-24 hours) than younger patients. In cardiac studies, older patients had higher preoperative levels of IL-6 and CRP and higher postoperative levels of IL-6 (<12 hours and >1 week). CONCLUSIONS: Our findings demonstrate a paucity of frailty-specific studies; however, the presence of age-associated differences in the perioperative inflammatory response is consistent with age-associated states of chronic systemic inflammation and immunosenescence. Additional studies assessing frailty-specific changes in the systemic biologic response to surgery may inform the development of targeted interventions.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.042 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".