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Analysis of a machine learning–based risk stratification scheme for acute kidney injury in vancomycin

Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Mu, Fei;
Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Cui, Chen;
Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Tang, Meng;
Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Guo, Guiping;
Affiliation
Department of Health Statistics ,School of Preventive Medicine ,Fourth Military Medical University ,Xi’an ,China
Zhang, Haiyue;
Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Ge, Jie;
Affiliation
Department of Urology ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Bai, Yujia;
Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Zhao, Jinyi;
Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Cao, Shanshan;
Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Wang, Jingwen;
Affiliation
Department of Pharmacy ,Xijing Hospital ,Fourth Military Medical University ,Xi’an ,China
Guan, Yue

Vancomycin-associated acute kidney injury (AKI) continues to pose a major challenge to both patients and healthcare providers. The purpose of this study is to construct a machine learning framework for stratified predicting and interpreting vancomycin-associated AKI. Our study is a retrospective analysis of medical records of 724 patients who have received vancomycin therapy from 1 January 2015 through 30 September 2020. The basic clinical information, vancomycin dosage and days, comorbidities and medication, laboratory indicators of the patients were recorded. Machine learning algorithm of XGBoost was used to construct a series risk prediction model for vancomycin-associated AKI in different underlying diseases. The vast majority of sub-model performed best on the corresponding sub-dataset. Additionally, the aim of this study was to explain each model and to explore the influence of clinical variables on prediction. As the results of the analysis showed that in addition to the common indicators (serum creatinine and creatinine clearance rate), some other underappreciated indicators such as serum cystatin and cumulative days of vancomycin administration, weight and age, neutrophils and hemoglobin were the risk factors for cancer, diabetes mellitus, heptic insufficiency respectively. Stratified analysis of the comorbidities in patients with vancomycin-associated AKI further confirmed the necessity for different patient populations to be studied.

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License Holder: Copyright © 2022 Mu, Cui, Tang, Guo, Zhang, Ge, Bai, Zhao, Cao, Wang and Guan.

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