Schatz, Lea Marie and Greppmair, Sebastian and Kunzelmann, Alexandra K. and Starp, Johannes and Brinkmann, Alexander and Roehr, Anka and Frey, Otto and Hagel, Stefan and Dorn, Christoph and Zoller, Michael and Scharf, Christina and Wicha, Sebastian G. and Liebchen, Uwe (2024) Predictive performance of multi-model approaches for model-informed precision dosing of piperacillin in critically ill patients. INTERNATIONAL JOURNAL OF ANTIMICROBIAL AGENTS, 64 (4): 107305. ISSN 0924-8579, 1872-7913
Full text not available from this repository. (Request a copy)Abstract
Objectives: Piperacillin (PIP)/tazobactam is a frequently prescribed antibiotic; however, over- or underdosing may contribute to toxicity, therapeutic failure, and development of antimicrobial resistance. An external evaluation of 24 published PIP-models demonstrated that model-informed precision dosing (MIPD) can enhance target attainment. Employing various candidate models, this study aimed to assess the predictive performance of different MIPD-approaches comparing (i) a single-model approach, (ii) a model selection algorithm (MSA) and (iii) a model averaging algorithm (MAA). Methods: Precision, accuracy and expected target attainment, considering either initial (B1) or initial and secondary (B2) therapeutic drug monitoring (TDM)-samples per patient, were assessed in a multicentre dataset (561 patients, 11 German centres, 3654 TDM-samples). Results: The results demonstrated a slight superiority in predictive performance using MAA in B1, regardless of the candidate models, compared to MSA and the best single models (MAA, MSA, best single models: inaccuracy +/- 3%, +/- 10%, +/- 8%; imprecision: <25%, <31%, <28%; expected target attainment >77%, >71%, >73%). The inclusion of a second TDM-sample notably improved precision and target attainment for all MIPD-approaches, particularly within the context of MSA and most of the single models. The expected target attainment is maximized (up to >90%) when the TDM-sample is integrated within 24 h. Conclusions: In conclusion, MAA streamlines MIPD by reducing the risk of selecting an inappropriate model for specific patients. Therefore, MIPD of PIP using MAA implicates further optimisation of antibiotic exposure in critically ill patients, by improving predictive performance with only one sample available for Bayesian forecasting, safety, and usability in clinical practice. (c) 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | POPULATION PHARMACOKINETICS; PHARMACODYNAMICS; SUPPORT; Therapeutic drug monitoring; Model-informed precision dosing; Population pharmacokinetics; Intensive care unit; Sepsis; Critically ill |
| Subjects: | 600 Technology > 615 Pharmacy |
| Divisions: | Chemistry and Pharmacy > Institute of Pharmacy > Group Clinical Pharmacy (Dr. Dorn) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 04 Dec 2025 05:42 |
| Last Modified: | 04 Dec 2025 05:42 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64923 |
Actions (login required)
 |
View Item |
