Clinical impact of vancomycin‐resistant enterococci colonization in nonliver solid organ transplantation and its implications for infection control strategies: A single‐center, 10‐year retrospective study
Bibliographic record
Abstract
BACKGROUND: Vancomycin-resistant enterococci (VRE) colonization in nonliver solid organ transplantation (SOT) is poorly defined. Infection control management of these patients is influenced by the association of VRE with adverse outcomes in liver transplantation. This study examines the frequency and clinical impact of VRE colonization specifically on nonliver SOT patients and discusses implications for nosocomial VRE control. METHODS: We retrospectively reviewed all nonliver SOT patients at a single transplant center from 2005 to 2015. We determined colonization rates in the peritransplant period and the rate of VRE infections. The association between VRE colonization with 90-day mortality and other clinical outcomes was examined. RESULTS: There were 1786 nonliver SOTs from 2005 to 2015, with 81 (4.6%) colonized with VRE in the peritransplantation period. The colonization prevalence varied by organ type: 45 of 423 lung (10.6%), 12 of 352 heart (3.4%), one of 18 heart-lung (5.6%), 20 of 884 kidney (2.3%), three of 63 kidney-pancreas (4.8%), zero of 11 pancreas, zero of five small bowel, and zero of 11 multivisceral. Peritransplant VRE colonization was not associated with 90-day mortality odds ratio = 2.35 (95% CI = 0.53, 10.29) and adjusted odds ratio = 1.52 (95% CI = 0.34, 6.88). In the multivariable logistic regression, there was no association with mortality at 1 year or 5 years, hospital length of stay, rehospitalization, or days alive out of hospital. There were 14 inpatient VRE infections up to 1 year after transplantation. CONCLUSION: Nonliver SOT patients have lower rates of VRE colonization than liver SOT, and colonization was not associated with increased adverse clinical outcomes. Although infection control strategies for VRE in hospital remain controversial, nonliver SOT should be considered among typical hospitalized patients when designing strategies for prevention.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".