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

Ignasi Sacanella,Erika Esteve-Pitarch, Jessica Guevara-Chaux, Julen Berrueta, Alejandro Garcia-Martinez,Josep Gomez, Cecilia Casarino, Florencia Ales,Laura Canadell,Ignacio Martin-Loeches,Santiago Grau,Francisco Javier Candel,Maria Bodi,Alejandro Rodriguez

ANTIBIOTICS-BASEL(2024)

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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.
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Key words
liposomal amphotericin B,acute kidney injury,machine learning,critical care,antifungal agents,random forest
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