Metabolic syndrome and surgical complications: a systematic review and meta-analysis of 13 million individuals
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome (MetS) is characterised by the presence of at least three of the five following components: insulin resistance, obesity, chronic hypertension, elevated serum triglycerides, and decreased high-density lipoprotein cholesterol concentrations. It is estimated to affect 1 in 3 people around the globe and is reported to affect 46% of surgical patients. For people with MetS who undergo surgery, an emerging body of literature points to significantly poorer postoperative outcomes compared with nonaffected populations. The aim of this study is to review the current evidence on the risks of surgical complications in patients with MetS compared to those without MetS. METHODS: Systematic review and meta-analysis using PRISMA and AMSTAR reporting guidelines. RESULTS: The meta-analysis included 63 studies involving 1 919 347 patients with MetS and 11 248 114 patients without MetS. Compared to individuals without the condition, individuals with MetS were at an increased risk of mortality (OR 1.75 95% CI: 1.36-2.24; P <0.01); all surgical site infection types as well as dehiscence (OR 1.64 95% CI: 1.52-1.77; P <0.01); cardiovascular complications (OR 1.56 95% CI: 1.41-1.73; P <0.01) including myocardial infarction, stroke, cardiac arrest, cardiac arrythmias and deep vein thrombosis; increased length of hospital stay (MD 0.65 95% CI: 0.39-0.9; P <0.01); and hospital readmission (OR 1.55 95% CI: 1.41-1.71; P <0.01). CONCLUSION: MetS is associated with a significantly increased risk of surgical complications including mortality, surgical site infection, cardiovascular complications, increased length of stay, and hospital readmission. Despite these risks and the high prevalence of MetS in surgical populations there is a lack of evidence on interventions for reducing surgical complications in patients with MetS. The authors suggest prioritising interventions across the surgical continuum that include (1) preoperative screening for MetS; (2) surgical prehabilitation; (3) intraoperative monitoring and management; and (4) postoperative rehabilitation and follow-up.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".