Analysis of a machine learning-based risk stratification scheme for acute kidney injury in vancomycin
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 ana...
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Published in | Frontiers in pharmacology Vol. 13; p. 1027230 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
24.11.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Kunming Pan, Fudan University, China Joseph Carreno, Albany College of Pharmacy and Health Sciences, United States These authors have contributed equally to this work and share first authorship This article was submitted to Renal Pharmacology, a section of the journal Frontiers in Pharmacology Edited by: Antonio Javier Carcas Sansuán, Hospital Universitario La Paz, Universidad Autónoma de Madrid, Spain |
ISSN: | 1663-9812 1663-9812 |
DOI: | 10.3389/fphar.2022.1027230 |