Infectious Complications and Antibiotic Prophylaxis during Induction Therapy with Venetoclax Plus Azacitidine for Previously Untreated AML: A Real-World Experience
Bibliographic record
Abstract
Venetoclax + azacitidine (ven-aza) induction therapy has become the standard of care at many centers for AML patients who are unfit for intensive induction therapy. Although widely utilized, the efficacy of antimicrobial prophylaxis during induction with this regimen has not been clearly determined. We retrospectively evaluated infectious complications during ven-aza induction therapy in all previously untreated AML patients receiving this regimen at the University of Alberta Hospital, Alberta, Canada from August 2020 - June 2024. There were 93 patients; the median age was 76 (range 58-90) years. ELN 2022 risk scores were: favorable 15, Intermediate 19, adverse 50, indeterminate 9; 30 had a prior myeloid malignancy or therapy-related AML. All patients received azacitidine 75 mg/m2 subcutaneously daily x7 plus venetoclax po daily x21-28 days, dose adjusted according to the azole antifungal used. Of these, 75 were managed on an outpatient basis after the venetoclax ramp-up phase; the remainder were hospitalized due to co-morbid medical issues. Use of antimicrobial prophylaxis was at the discretion of the treating physician. Outcomes were assessed only during the initial induction cycle, for a minimum 30 days, or up to day 60 if delayed neutrophil recovery occurred. Antibacterial prophylaxis was used in 81 patients (76 levofloxacin, 5 amoxicillin-clavulanate); 2 were already on IV antibiotics for a pre-existing infection, and 10 received no prophylaxis. Of the patients receiving prophylaxis, 3 (4%) developed bacteremia during induction (Streptococcus mitis, Enterococcus faecium and Pseudomonas aeruginosa). Of the patients not receiving prophylaxis, 4 (40%) developed bacteremia (Escherichia coli x2, Pseudomonas, Stenotrophomonas, Clostridium sporogenes; 1 patient had 2 organisms). Of 70 prophylaxed patients followed on an outpatient basis, 20 (29%) were readmitted: 8 with unexplained culture-negative febrile neutropenia (FN), 9 with culture-negative pneumonia, and 3 with COVID; there were also 4 urinary tract infections (E. faecium x2, E. faecalis and E. coli). Of the 10 patients receiving no prophylaxis, there were 3 additional infections (COVID, pneumocystis jiroveci and culture-negative pneumonia). There were no toxicities attributable to the antibacterial prophylaxis. Antifungal prophylaxis was used in 89 patients (70 posaconazole, 18 fluconazole, 1 voriconazole). There were no documented or probable invasive fungal infections seen in any of the 93 patients. The best complete response rate with induction was 66% (44% CR, 22% CRi), and 6 MLFS for an ORR of 72%. The median time to ANC >0.5x109/L was 33 days (range 6-68 days) for those achieving CR/CRi; 26 either did not reach ANC 0.5 or died early. There were 11 induction deaths from a variety of causes: 3 pneumonia, 2 culture negative shock, 1 congestive heart failure, 1 hypokalemic cardiac arrest, 1 GI hemorrhage, 2 switched to comfort care and 1 of unknown cause; 9 deaths were in patients receiving antibacterial prophylaxis. On univariate analysis, the use of antibacterial prophylaxis was significantly associated with a lower risk of bacteremia (p<0.0001 by Chi square), but there was no difference in FN, pneumonia, readmission or induction death rates compared with those not receiving antibacterial prophylaxis. Age, secondary AML or ELN risk group also did not predict for bacteremia, FN, early death or readmission. Conclusion: The use of antibacterial prophylaxis during ven-aza induction is associated with very low rates of bacteremia. However, readmissions for other suspected or proven infections, including culture-negative febrile neutropenia and pneumonia, are still relatively frequent.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".