Surgical Site Infections After Liver Transplantation: Prospective Surveillance and Evaluation of 250 Transplant Recipients in Canada
Bibliographic record
Abstract
OBJECTIVE To evaluate the incidence of surgical-site infections (SSIs) in a cohort of liver transplant recipients and to assess risk factors predisposing patients to these infections. DESIGN Prospective observational cohort study. SETTING Single transplant center in Canada. PATIENTS Patients who underwent liver transplantation between February 2011 and August 2014. METHODS Multivariate logistic regression was used to identify independent risk factors for SSIs in liver transplant patients. RESULTS We enrolled 250 liver transplant recipients. The recipients' median age at the time of transplantation was 56 years (range, 19-70 years), and 166 patients (66.4%) were male. Moreover, 47 SSIs were documented in 43 patients (17.2%). Organ-space, superficial, and deep SSIs were noted in 29, 7, and 3 patients, respectively. In addition, 2 patients developed superficial and organ-space SSIs, and another 2 patients were found to have deep as well as organ-space infections. In total, we identified 33 organ-space SSIs (70.2%), 9 superficial SSIs (19.1%), and 5 deep SSIs (10.6%). Factors predictive of SSIs by multivariate analysis were duct-to-duct anastomosis (odds ratio [OR], 3.88; 95% CI, 1.85-8.13; P<.001) and dialysis (OR, 3.57; 95% CI, 1.02-12.50; P=.046). Of the 66 organisms isolated in both deep and organ-space SSIs, 55 (83%) were resistant to cefazolin. CONCLUSIONS Organ-space SSIs are a common complication after liver transplantation. Duct-to-duct anastomosis and dialysis were independent risk factors associated with SSIs. Appropriate perioperative prophylaxis targeting patients with duct-to-duct anastomosis and dialysis while simultaneously providing optimum coverage for the potential pathogens causing SSIs is warranted. Infect Control Hosp Epidemiol 2017;38:1084-1090.
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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) | 0.001 | 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".