Comparison of pharmacokinetics software for therapeutic drug monitoring of piperacillin in patients with severe infections.

Autor:	Rodríguez-Báez AS; Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, San Luis Potosi, SLP, Mexico., Jiménez-Meseguer M; Servicio de Farmacia, Hospital Universitario Severo Ochoa, Leganés, Spain., Milán-Segovia RDC; Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, San Luis Potosi, SLP, Mexico., Romano-Moreno S; Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, San Luis Potosi, SLP, Mexico., Barcia E; Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid, Spain., Ortiz-Álvarez A; Hospital Central 'Dr Ignacio Morones Prieto', San Luis Potosí, Mexico., García-Díaz B; Servicio de Farmacia, Hospital Universitario Severo Ochoa, Leganés, Spain., Medellín-Garibay SE; Universidad Autónoma de San Luis Potosí, San Luis Potosi, SLP, Mexico susanna.medellin@uaslp.mx.
Jazyk:	angličtina
Zdroj:	European journal of hospital pharmacy : science and practice [Eur J Hosp Pharm] 2024 Apr 23; Vol. 31 (3), pp. 201-206. Date of Electronic Publication: 2024 Apr 23.
DOI:	10.1136/ejhpharm-2022-003367
Abstrakt:	Objective: To evaluate the predictive performance of population pharmacokinetic models for piperacillin (PIP) available in the software MwPharm, TDMx and ID-ODs for initial dosing selection and therapeutic drug monitoring (TDM) purposes. Methods: This is a prospective observational study in adult patients with severe infections receiving PIP treatment. Plasma concentrations were quantified by ultra-high performance liquid chromatography coupled to tandem mass spectrometry. The differences between predicted and observed PIP concentrations were evaluated with Bland-Altman plots; additionally, the relative and absolute bias and precision of the models were determined. Results: A total of 145 PIP plasma concentrations from 42 patients were analysed. For population prediction, MwPharm showed the best predictive performance with a mean relative difference of 34.68% (95% CI -197% to 266%) and a root mean square error (RMSE) of 60.42 µg/mL; meanwhile TDMx and ID-ODs under-predicted PIP concentrations. For individual prediction, the TDMx model was found to be the most precise with a mean relative difference of 7.61% (95% CI -57.63 to 72.86%), and RMSE of 17.86 µg/mL. Conclusion: Current software for TDM is a valuable tool, but it may also include different population pharmacokinetic models in patients with severe infections, and should be evaluated before performing a model-based TDM in clinical practice. Considering the heterogeneous characteristics of patients with severe infections, this study demonstrates the need for therapy personalisation for PIP to improve pharmacokinetic/pharmacodynamic target attainment. Competing Interests: Competing interests: None declared. (© European Association of Hospital Pharmacists 2024. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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