AIMC Topic: Vancomycin
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Optimizing Initial Vancomycin Dosing in Hospitalized Patients Using Machine Learning Approach for Enhanced Therapeutic Outcomes: Algorithm Development and Validation Study.
Journal of medical Internet research
Mar 31, 2025
BACKGROUND: Vancomycin is commonly dosed using standard weight-based methods before dose adjustments are made through therapeutic drug monitoring (TDM). However, variability in initial dosing can lead to suboptimal therapeutic outcomes. A predictive ...
Development and external validation of a machine learning model to predict the initial dose of vancomycin for targeting an area under the concentration-time curve of 400-600 mg∙h/L.
International journal of medical informatics
Jan 29, 2025
PURPOSE: To develop and validate a novel artificial intelligence model for predicting the initial empiric dose of vancomycin, with the aim of achieving an area under the concentration-time curve (AUC) of 400-600 mg∙h/L, using individual clinical data...
Utilizing deep learning-based causal inference to explore vancomycin's impact on continuous kidney replacement therapy necessity in blood culture-positive intensive care unit patients.
Microbiology spectrum
Dec 10, 2024
Patients with positive blood cultures in the intensive care unit (ICU) are at high risk for septic acute kidney injury requiring continuous kidney replacement therapy (CKRT), especially when treated with vancomycin. This study developed a machine lea...
Machine learning-based prediction of vancomycin concentration after abdominal administration in patients with peritoneal dialysis-related peritonitis.
Therapeutic apheresis and dialysis : official peer-reviewed journal of the International Society for Apheresis, the Japanese Society for Apheresis, the Japanese Society for Dialysis Therapy
Jul 21, 2024
INTRODUCTION: Peritonitis is a serious complication of peritoneal dialysis (PD), in which insufficient control of antibacterial drug concentrations poses a significant risk for poor outcomes. Predicting antibacterial drug concentrations is crucial in...
Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age.
International journal of clinical pharmacy
Jun 11, 2024
BACKGROUND: Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiolo...
Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.
British journal of clinical pharmacology
Jun 6, 2024
AIMS: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-bas...
Determining steady-state trough range in vancomycin drug dosing using machine learning.
Journal of critical care
Mar 18, 2024
BACKGROUND: Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate d...
Prediction of vancomycin initial dosage using artificial intelligence models applying ensemble strategy.
BMC bioinformatics
Mar 22, 2023
BACKGROUND: Antibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow. Therefore, clinical dosing decisions must be made with the utmost care; such decisions are said to ...
PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data.
Journal of biomedical informatics
Aug 17, 2022
Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, howev...
A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors.
CPT: pharmacometrics & systems pharmacology
Jul 26, 2021
Model-informed precision dosing (MIPD) approaches typically apply maximum a posteriori (MAP) Bayesian estimation to determine individual pharmacokinetic (PK) parameters with the goal of optimizing future dosing regimens. This process combines knowled...
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