Towards model-informed precision dosing of piperacillin: multicenter systematic external evaluation of pharmacokinetic models in critically ill adults with a focus on Bayesian forecasting

Purpose Inadequate piperacillin (PIP) exposure in intensive care unit (ICU) patients threatens therapeutic success. Model-informed precision dosing (MIPD) might be promising to individualize dosing; however, the transferability of published models to external populations is uncertain. This study aim...

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Bibliographic Details
Published in:Intensive care medicine 2023-08, Vol.49 (8), p.966-976
Main Authors: Greppmair, Sebastian, Brinkmann, Alexander, Roehr, Anka, Frey, Otto, Hagel, Stefan, Dorn, Christoph, Marsot, Amélie, El-Haffaf, Ibrahim, Zoller, Michael, Saller, Thomas, Zander, Johannes, Schatz, Lea Marie, Scharf, Christina, Briegel, Josef, Minichmayr, Iris K., Wicha, Sebastian G., Liebchen, Uwe
Format: Article
Language:English
Subjects:
Adult
Analysis
Anesthesiology
Anti-Bacterial Agents
Bayes Theorem
Bayesian analysis
Critical Care
Critical Care Medicine
Critical Illness - therapy
Datasets
Dosage
Drug Monitoring
Emergency Medicine
Evaluation
Forecasting
Forecasts and trends
Health aspects
Health care industry
Hospital patients
Humans
Intensive
Mathematical models
Medical research
Medicine
Medicine & Public Health
Medicine, Experimental
Original
Pain Medicine
Patients
Pediatrics
Performance prediction
Pharmacokinetics
Piperacillin
Pneumology/Respiratory System
Population studies
Predictions
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Summary:Purpose Inadequate piperacillin (PIP) exposure in intensive care unit (ICU) patients threatens therapeutic success. Model-informed precision dosing (MIPD) might be promising to individualize dosing; however, the transferability of published models to external populations is uncertain. This study aimed to externally evaluate the available PIP population pharmacokinetic (PopPK) models. Methods A multicenter dataset of 561 ICU patients (11 centers/3654 concentrations) was used for the evaluation of 24 identified models. Model performance was investigated for a priori (A) predictions, i.e., considering dosing records and patient characteristics only, and for Bayesian forecasting, i.e., additionally including the first (B1) or first and second (B2) therapeutic drug monitoring (TDM) samples per patient. Median relative prediction error (MPE) [%] and median absolute relative prediction error (MAPE) [%] were calculated to quantify accuracy and precision. Results The evaluation revealed a large inter-model variability (A: MPE − 135.6–78.3% and MAPE 35.7–135.6%). Integration of TDM data improved all model predictions (B1/B2 relative improvement vs. A: |MPE| median_all_models 45.1/67.5%; MAPE median_all_models 29/39%). The model by Kim et al. was identified to be most appropriate for the total dataset (A/B1/B2: MPE − 9.8/− 5.9/− 0.9%; MAPE 37/27.3/23.7%), Udy et al. performed best in patients receiving intermittent infusion, and Klastrup et al. best predicted patients receiving continuous infusion. Additional evaluations stratified by sex and renal replacement therapy revealed further promising models. Conclusion The predictive performance of published PIP models in ICU patients varied considerably, highlighting the relevance of appropriate model selection for MIPD. Our differentiated external evaluation identified specific models suitable for clinical use, especially in combination with TDM.
ISSN:0342-4642
1432-1238
DOI:10.1007/s00134-023-07154-0