1.Risk Factors Analysis and Predictive Model Construction for Acute Kidney Injury Following Amphotericin B Deoxycholate Use in Hospitalized Patients
Hao XIE ; Yixun SHI ; Zhiqing XU ; Minquan LI ; Xiaoli DU ; Gang CHEN ; Bin ZHAO
Medical Journal of Peking Union Medical College Hospital 2026;17(2):429-437
To investigate the risk factors for acute kidney injury (AKI) following the use of amphotericin B deoxycholate and to develop a predictive model to guide clinical monitoring and intervention. A retrospective analysis was conducted on hospitalized patients who received amphotericin B deoxycholate between January 2014 and September 2024. Patients were divided into a training set and a validation set. Demographic data, laboratory findings, and medication orders were collected. Based on the occurrence of AKI during treatment and within 7 days after discontinuation, patients were classified into an AKI group and a non-AKI group. Univariate analysis was used to screen for potential risk factors, multivariate logistic regression was employed to construct a predictive model, and model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow test. The training set included 473 patients, comprising 255 males (53.91%) and 218 females (46.09%), with a median age of 52(35, 62) years. The AKI group consisted of 191 cases (40.38%), and the non-AKI group consisted of 282 cases (59.62%). The validation set included 114 patients, comprising 80 males (70.18%) and 34 females (29.82%), with a median age of 43.5 (31.0, 58.5) years. The AKI group consisted of 42 cases (36.84%), and the non-AKI group consisted of 72 cases (63.16%). Univariate analysis revealed statistically significant differences between the two groups in 23 factors (all Admission to the ICU, elevated serum creatinine at admission, and comorbid cardiac insufficiency as potential risk factors for AKI, while prophylactic use of diphenhydramine/promethazine or sodium bicarbonate showed a protective association. A predictive model with good discrimina-tion and calibration was developed, which may provide a basis for early identification of high-risk patients and timely adjustment of treatment strategies in clinical practice.

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