The Effect of Sarcopenia on Postoperative Outcomes Following Emergency Laparotomy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Emergency laparotomy procedures have high rates of postoperative mortality and morbidity in older patient. Sarcopenia is associated with poor postoperative outcomes in elective surgeries and there is growing evidence for its use as a risk predictor in the emergency setting. The study aimed to evaluate the effect of sarcopenia on postoperative mortality and morbidity following emergency laparotomy. Five electronic databases were systematically searched (MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, and Web of Science) from conception until the 14th February 2022. All prospective cohort studies were included. Risk of bias was assessed with the Newcastle-Ottawa score. Pooled meta-analyses were estimated using the Mantel-Haenszel and inverse-generic variance method for mortality and morbidity outcomes. Eleven retrospective cohort studies were included, of which ten were included in the meta-analysis comprising of 3492 patients (1027 sarcopenic, 2465 non-sarcopenic). The study level incidence of sarcopenia ranged from 24.6 to 50.3% with a median rate of 25.1%. Sarcopenia was associated with increased 30-day mortality (OR 2.36, 95% CI, 1.66, 3.37, I2 = 43%), 90-day mortality (OR 2.51, 95% CI, 1.79, 3.52, I2 = 0%), and length of hospital stay (in days) (MD 1.18, 95% CI, 0.42, 1.94, I2 = 0%, P=0.002), but not incidence of postoperative major complications (OR 1.49, 95% CI, 0.86, 2.56, I2 = 70%, P = 0.15). Sarcopenia predicts poor outcomes following emergency laparotomy. We suggest assessment of sarcopenia should be incorporated into acute surgical assessment to identify high risk patients and inform clinical decision-making prior to an emergency laparotomy.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".