Risk factors for acute kidney injury in patients with severe acute pancreatitis: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: This systematic review and meta-analysis aimed to identify the risk factors for acute kidney injury (AKI) in patients with severe acute pancreatitis (SAP). Methods: A comprehensive literature search was conducted using the PubMed, Embase and Cochrane Library databases for case-control studies comparing the clinical characteristics of patients with SAP with and without AKI. The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS). Pooled odds ratios (ORs) with 95 % confidence intervals (CIs) were calculated using fixed- or random-effects models, based on heterogeneity. Results: Five studies involving 795 patients with SAP were included, of whom 173 (21.8 %) developed AKI. All studies were of high quality according to the NOS. Among the 17 potential risk factors that were analysed, a history of alcohol consumption (OR = 2.36, 95% CI = 0.54–10.43, p < 0.001), elevated serum amylase (OR = 4.50, 95% CI = 1.77–11.43, p = 0.002) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (OR = 1.57, 95% CI = 0.49–2.64, p = 0.004) were significantly associated with an increased risk of AKI. However, hypertension (OR = 1.14, 95% CI = 0.60–2.16, p = 0.69) and diabetes (OR = 1.88, 95% CI = 0.51–6.95, p = 0.34) were not significantly associated with AKI risk. Based on funnel plots, no obvious publication bias was detected. Conclusions: A history of alcohol consumption, elevated serum amylase and APACHE II score are significant risk factors for AKI in patients with SAP. For early intervention, clinical physicians should be vigilant about the risk of AKI in patients with SAP with these factors. More high-quality studies are needed to validate these findings and explore other potential risk factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".