1. Prediction of teicoplanin plasma concentration in critically ill patients: a combination of machine learning and population pharmacokinetics.
- Author
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Ma P, Shang S, Liu R, Dong Y, Wu J, Gu W, Yu M, Liu J, Li Y, and Chen Y
- Subjects
- Humans, Retrospective Studies, Male, Female, Middle Aged, Aged, Staphylococcal Infections drug therapy, Staphylococcal Infections microbiology, Adult, Algorithms, Aged, 80 and over, Teicoplanin pharmacokinetics, Teicoplanin blood, Teicoplanin administration & dosage, Critical Illness, Anti-Bacterial Agents pharmacokinetics, Anti-Bacterial Agents blood, Anti-Bacterial Agents administration & dosage, Anti-Bacterial Agents therapeutic use, Machine Learning
- Abstract
Background: Teicoplanin has been widely used in patients with infections caused by Staphylococcus aureus, especially for critically ill patients. The pharmacokinetics (PK) of teicoplanin vary between individuals and within the same individual. We aim to establish a prediction model via a combination of machine learning and population PK (PPK) to support personalized medication decisions for critically ill patients., Methods: A retrospective study was performed incorporating 33 variables, including PPK parameters (clearance and volume of distribution). Multiple algorithms and Shapley additive explanations were employed for feature selection of variables to determine the strongest driving factors., Results: The performance of each algorithm with PPK parameters was superior to that without PPK parameters. The composition of support vector regression, categorical boosting and a backpropagation neural network (7:2:1) with the highest R2 (0.809) was determined as the final ensemble model. The model included 15 variables after feature selection, of which the predictive performance was superior to that of models considering all variables or using only PPK. The R2, mean absolute error, mean squared error, absolute accuracy (±5 mg/L) and relative accuracy (±30%) of external validation were 0.649, 3.913, 28.347, 76.12% and 76.12%, respectively., Conclusions: Our study offers a non-invasive, fast and cost-effective prediction model of teicoplanin plasma concentration in critically ill patients. The model serves as a fundamental tool for clinicians to determine the effective plasma concentration range of teicoplanin and formulate individualized dosing regimens accordingly., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
- Published
- 2024
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