Machine learning and population pharmacokinetics: a hybrid approach for optimizing vancomycin therapy in sepsis patients
This study evaluates and compares the performance of four models-PPK, Bayesian, ML, and hybrid PPK-ML-in predicting vancomycin exposure (AUC<sub>24</sub>) in sepsis patients using real-world data from the MIMIC-IV database. These results underscore the importance of selecting appropriate models based on the availability of concentration data, providing valuable guidance for precision dosing strategies in sepsis care. This work contributes to advancing personalized vancomycin therapy, optimizing dosing regimens, and improving clinical outcomes in sepsis patients.
Authors
- Chuhui Wang (ORCID: https://orcid.org/0000-0001-7328-9851)
- Keyu Chen
- Yalin Dong (ORCID: https://orcid.org/0000-0001-5619-0788)
- Wei-Jia Huang (ORCID: https://orcid.org/0009-0001-8434-3611)
- Yan Wang (ORCID: https://orcid.org/0000-0002-6511-1495)
- Wei Yu (ORCID: https://orcid.org/0000-0002-0536-5637)
- Sai Ma (ORCID: https://orcid.org/0009-0009-9915-2605)
Institutions
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
Publication Details
- Journal
- Microbiology Spectrum
- Published
- 2025-03-31
- DOI
- https://doi.org/10.1128/spectrum.00499-25
- Citations
- 8
- Primary Topic
- Antibiotics Pharmacokinetics and Efficacy
- Type
- article
- Field-Weighted Citation Impact
- 11.16
Funders
- National Natural Science Foundation of China