Outcomes Associated with De-escalation of Antibiotics to Target Positive Cultures when Treating Febrile Neutropenia.
Rebecca Ann RainessPeter CampbellJennifer SantamalaChristine J KubinMonica MehtaPublished in: Journal of pharmacy practice (2022)
Background: Patients with hematologic malignancies frequently develop febrile neutropenia (FN) and subsequently receive long courses of broad-spectrum antibiotics. Limited data is available on de-escalation strategies. Methods : This was a retrospective observational cohort study of adult patients with a hematologic malignancy, FN, and positive culture results from June 2017 to June 2020. A conventional group (patients who remained on empiric, broad-spectrum agents) was compared to a de-escalation group (patients whose antibiotic therapy was de-escalated based on culture results). The primary outcome was the incidence of recurrent fever or antibiotic escalation due to infection while neutropenic. Results: Of the 123 patients included, the composite primary outcome occurred in 35.3% in the de-escalation group and 39.3% in the conventional group ( P = .83). For secondary outcomes, median time to recurrent fever was 7 days in the de-escalation group and 7 days in the conventional group ( P = .73). Incidence of Clostridioides difficile was 5.9% in the de-escalation group and 6.7% in the conventional group ( P = 1.00). Development of multidrug resistant pathogens during hospital admission was 20.6% in the de-escalation group and 14.6% in the conventional group ( P = .59). Median length of broad-spectrum antibiotics was 3 days in the de-escalation group and 8 days in the conventional group ( P < .001). All-cause mortality within 30 days was 0 in the de-escalation group and 5.6% in the conventional group ( P = .32). Conclusion: In a small sample of patients with a hematologic malignancy and FN, de-escalating antibiotics based on positive cultures decreased the duration of antibiotic therapy without increasing the rate of antibiotic failure.
Keyphrases
- open label
- end stage renal disease
- multidrug resistant
- chronic kidney disease
- type diabetes
- emergency department
- ejection fraction
- clinical trial
- escherichia coli
- adipose tissue
- risk factors
- newly diagnosed
- mesenchymal stem cells
- machine learning
- skeletal muscle
- urinary tract infection
- deep learning
- acinetobacter baumannii
- clostridium difficile