Incidence, Risk Factors and Outcome of Pre-engraftment Gram-Negative Bacteremia After Allogeneic and Autologous Hematopoietic Stem Cell Transplantation: An Italian Prospective Multicenter Survey
Bibliographic record
Abstract
BACKGROUND: Gram-negative bacteremia (GNB) is a major cause of illness and death after hematopoietic stem cell transplantation (HSCT), and updated epidemiological investigation is advisable. METHODS: We prospectively evaluated the epidemiology of pre-engraftment GNB in 1118 allogeneic HSCTs (allo-HSCTs) and 1625 autologous HSCTs (auto-HSCTs) among 54 transplant centers during 2014 (SIGNB-GITMO-AMCLI study). Using logistic regression methods. we identified risk factors for GNB and evaluated the impact of GNB on the 4-month overall-survival after transplant. RESULTS: The cumulative incidence of pre-engraftment GNB was 17.3% in allo-HSCT and 9% in auto-HSCT. Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa were the most common isolates. By multivariate analysis, variables associated with GNB were a diagnosis of acute leukemia, a transplant from a HLA-mismatched donor and from cord blood, older age, and duration of severe neutropenia in allo-HSCT, and a diagnosis of lymphoma, older age, and no antibacterial prophylaxis in auto-HSCT. A pretransplant infection by a resistant pathogen was significantly associated with an increased risk of posttransplant infection by the same microorganism in allo-HSCT. Colonization by resistant gram-negative bacteria was significantly associated with an increased rate of infection by the same pathogen in both transplant procedures. GNB was independently associated with increased mortality at 4 months both in allo-HSCT (hazard ratio, 2.13; 95% confidence interval, 1.45-3.13; P <.001) and auto-HSCT (2.43; 1.22-4.84; P = .01). CONCLUSIONS: Pre-engraftment GNB is an independent factor associated with increased mortality rate at 4 months after auto-HSCT and allo-HSCT. Previous infectious history and colonization monitoring represent major indicators of GNB. CLINICAL TRIALS REGISTRATION: NCT02088840.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".