Infection risk and role of antibiotic prophylaxis in acute myeloid leukemia patients receiving lower intensity chemotherapy: A literature review
Bibliographic record
Abstract
Patients with acute myeloid leukemia (AML) are at high risk for infections due to chemotherapy-induced neutropenia. AML patients who receive lower dose chemotherapy are typically elderly patients who have comorbid conditions or have failed initial therapy. Antibiotic prophylaxis use is not clearly understood in this patient population, but early studies were conducted to study the incidence of infections and the use of antibiotic prophylaxis to reduce the risk of infections. The purpose of this literature review was to determine the incidence of infections in patients with AML, if antibiotic use leads to reduced incidence of infections and the role of antibiotic prophylaxis in patients with AML who received lower intensity chemotherapy. This review searched the PubMed database for relevant papers that addressed the research question using the following search terms: leukemia, infections, febrile neutropenia and chemotherapy. Four retrospective studies were identified using this approach and are included in this review. Comparing the four retrospective studies, there was a significant number of patients infected with Gram negative bacteria and the most common pathogens were Enterobacteriaceae and Pseudomonas aeruginosa. Increased incidence of infections were observed during cycles 1 and 2 of chemotherapy. In one of the studies, despite use of antibiotic prophylaxis, infections occurred and induced the growth of antibiotic resistant bacteria. In another study, the use of antibiotic prophylaxis significantly decreased the incidence of infections in patients with severe neutropenia. Due to the increased incidence of infections in the early chemotherapy cycles, antibiotic prophylaxis has a potential role during this time period especially for severely neutropenic patients. Febrile neutropenia could be the only sign of infection for AML patients so close monitoring during this time period would allow for early detection of infections and prompt treatment with antibiotics. Further studies are required in order to clarify the role of antibiotic prophylaxis, its indication and duration of use in this patient population.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".