Preoperative Risk Factors for Short-Term Postoperative Mortality of Acute Mesenteric Ischemia after Laparotomy: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Our objective was to comprehensively present the evidence of preoperative risk factors for short-term postoperative mortality of acute mesenteric ischemia after laparotomy. METHODS: PubMed, Embase, and Google Scholar were searched from January 2000 to January 2020. Studies evaluating the postoperative risk factors for short-term postoperative mortality of acute mesenteric ischemia after laparotomy were included. The outcome extracted were patients' demographics, medical history, and preoperative laboratory tests. RESULTS: Twenty studies (5011 patients) met the inclusion criteria. Studies were of high quality, with a median Newcastle-Ottawa Scale Score of 7. Summary short-term postoperative mortality was 44.38% (range, 18.80%-67.80%). Across included studies, 49 potential risk factors were examined, at least two studies. Meta-analysis of predictors based on more than three studies identified the following preoperative risk factors for higher short-term postoperative mortality risk: old age (odds ratio [OR], 1.90, 95% confidence interval [CI], 1.57-2.30), arterial occlusive mesenteric ischemia versus mesenteric venous thrombosis (OR, 2.45, 95% CI 1.12-5.33), heart failure (OR 1.33, 95% CI 1.03-1.72), renal disorders (OR 1.61, 95% CI 1.24-2.07), and peripheral vascular disease (OR 1.38, 95% CI 1.00-1.91). Nonsurvivors were older (standardized mean difference [SMD], 0.32, 95% CI 0.24-0.40), had higher creatinine levels (SMD 0.50, 95% CI 0.25-0.75), and had lower platelet counts (SMD -0.32, 95% CI -0.50 to -0.14). CONCLUSION: The short-term postoperative mortality of acute mesenteric ischemia who underwent laparotomy is still high. A better understanding of these risk factors may help in the early identification of high-risk patients, optimization of surgical procedure, and improvement of perioperative management.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".