Risk Factors and Complications Following Percutaneous Endoscopic Gastrostomy: A Case Series of 1041 Patients
Bibliographic record
Abstract
BACKGROUND: Most studies exclude patients with severe coagulation disorders or those taking anticoagulants when evaluating the outcomes of percutaneous endoscopic gastrostomy (PEG). OBJECTIVE: To investigate complications and risk factors of PEG in a large clinical series including patients undergoing antiplatelet and anticoagulant therapy. METHODS: During a six-year period, 1057 patients referred for PEG placement were prospectively audited for clinical outcome. Exclusion criteria and follow-up care were defined. Complications were defined as minor or severe. Uni- and multivariate analyses were used to evaluate 14 risk factors. No standardized antibiotic prophylaxis was given. RESULTS: A total of 1041 patients (66% male, 34% female) with the following conditions underwent PEG: neurogenic dysphagia (n=450), cancer (n=385) and others (n=206). No anticoagulants were administered to 351 patients, thrombosis prophylaxis was given to 348 while full therapeutic anticoagulation was received by 313. No increased bleeding risk was associated with patients who had above-normal international normalized ratio values (OR 0.79 [95% CI 0.08 to 7.64]; P=1.00). The total infection rate was 20.5% in patients with malignant disease, and 5.5% in those with nonmalignant disease. Severe complications occurred in 19 patients (bleeding 0.5%, peritonitis 1.3%). Cirrhosis (OR 2.91 [95% CI 1.31 to 6.54]; P=0.008), cancer (OR 2.34 [95% CI 1.33 to 4.12]; P=0.003) and radiation therapy (OR 2.34 [95% CI 1.35 to 4.05]; P=0.002) were significant predictors of post-PEG infection. The 30-day mortality rate was 5.8%. There were no procedure-related deaths. CONCLUSIONS: Cancer, cirrhosis and radiation therapy were predictors of infection. Post-PEG bleeding and other complications were rare events. Collectively, the data suggested that patients taking concurrent anticoagulants had no elevated risk of post-PEG bleeding.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".