A Real-World Data Observational Analysis of the Impact of Liposomal Amphotericin B on Renal Function Using Machine Learning in Critically Ill Patients

Antibiotics (Basel). 2024 Aug 12;13(8):760. doi: 10.3390/antibiotics13080760.

Abstract

Background: Liposomal amphotericin B (L-AmB) has become the mainstay of treatment for severe invasive fungal infections. However, the potential for renal toxicity must be considered.

Aims: To evaluate the incidence of acute kidney injury (AKI) in critically ill patients receiving L-AmB for more than 48 h.

Methods: Retrospective, observational, single-center study. Clinical, demographic and laboratory variables were obtained automatically from the electronic medical record. AKI incidence was analyzed in the entire population and in patients with a "low" or "high" risk of AKI based on their creatinine levels at the outset of the study. Factors associated with the development of AKI were studied using random forest models.

Results: Finally, 67 patients with a median age of 61 (53-71) years, 67% male, a median SOFA of 4 (3-6.5) and a crude mortality of 34.3% were included. No variations in serum creatinine were observed during the observation period, except for a decrease in the high-risk subgroup. A total of 26.8% (total population), 25% (low risk) and 13% (high risk) of patients developed AKI. Norepinephrine, the SOFA score, furosemide (general model), potassium, C-reactive protein and procalcitonin (low-risk subgroup) were the variables identified by the random forest models as important contributing factors to the development of AKI other than L-AmB administration.

Conclusions: The development of AKI is multifactorial and the administration of L-AmB appears to be safe in this group of patients.

Keywords: acute kidney injury; antifungal agents; critical care; liposomal amphotericin B; machine learning; random forest.

Grants and funding

This research received no external funding.