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Comparison of pharmacokinetics software for therapeutic drug monitoring of piperacillin in patients with severe infections
  1. Ana Socorro Rodríguez-Báez1,
  2. María Jiménez-Meseguer2,
  3. Rosa del Carmen Milán-Segovia1,
  4. Silvia Romano-Moreno1,
  5. Emilia Barcia3,
  6. Arturo Ortiz-Álvarez4,
  7. Benito García-Díaz2,
  8. Susanna Edith Medellín-Garibay5
  1. 1 Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, San Luis Potosi, SLP, Mexico
  2. 2 Servicio de Farmacia, Hospital Universitario Severo Ochoa, Leganés, Spain
  3. 3 Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid, Spain
  4. 4 Hospital Central “Dr Ignacio Morones Prieto”, San Luis Potosí, Mexico
  5. 5 Universidad Autónoma de San Luis Potosí, San Luis Potosi, SLP, Mexico
  1. Correspondence to Dr Susanna Edith Medellín-Garibay, Universidad Autónoma de San Luis Potosí, San Luis Potosi, SLP, Mexico; susanna.medellin{at}uaslp.mx

Abstract

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.

  • therapeutic drug monitoring
  • drug monitoring
  • medical informatics
  • critical care
  • clinical medicine

Data availability statement

Data are available upon reasonable request. Derived data supporting the findings of this study are available from the corresponding author Medellin Garibay PhD on request.

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Data availability statement

Data are available upon reasonable request. Derived data supporting the findings of this study are available from the corresponding author Medellin Garibay PhD on request.

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