Associations of inflammatory biomarkers with morbidity and mortality after noncardiac surgery: A systematic review and meta-analysis
Bibliographic record
Abstract
Noncardiac surgery is associated with an inflammatory response. Whether increased inflammation in the perioperative period is associated with subsequent morbidity and mortality is unknown. MEDLINE, EMBASE, and CENTRAL were systematically searched from date of inception until May 2023. Longitudinal studies were included if they reported multivariable adjusted associations of biomarkers measured preoperatively and/or within 10 days after surgery with at least one prespecified adverse outcome in noncardiac surgery patients. Data were extracted independently and in duplicate. Risk estimates were pooled using DerSimonian-Laird random-effects models and reported as summary odds ratios (ORs) with 95% CIs. The outcomes were all-cause mortality and major adverse cardiovascular events. Fifty-two studies with a total of 121,849 patients were included. The median follow-up was 56 [IQR, 28–63] months and the average age was 57 (±3) years. Elevated preoperative C-reactive protein (CRP) levels were associated with a higher risk of mortality (OR 1.57, 95% CI 1.29–1.90, I2 = 93%, 28 studies). This association was stronger in non-cancer surgery populations (OR 2.10, 95% CI 1.92–2.31, I2 = 0%, 4 studies) when compared to cancer surgery populations (OR 1.51, 95% CI 1.26–1.81, I2 = 83%, 24 studies) (p for subgroup difference = 0.001). Similarly, higher postoperative CRP levels were associated with all-cause mortality (OR 1.61, 95% CI 1.17–2.20, I2 = 90%, 7 studies). Higher preoperative CRP levels were associated with major cardiovascular events (OR 2.11, 95% CI 1.51–2.94, I2 = 0%, 2 studies). Other preoperatively measured biomarkers associated with all-cause mortality were fibrinogen (OR 1.48, 95% CI 1.05–2.09, I2 = 52%, 5 studies), interleukin-6 (OR 1.17, 95% CI 1.07–1.28, I2 = 27%, 3 studies), and tumour necrosis factor-alpha (OR 1.37, 95% CI 1.16–1.61, I2 = 0%, 2 studies). Inflammatory biomarker levels in the perioperative period were associated with all-cause mortality and adverse cardiovascular events in patients undergoing noncardiac surgery.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".