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A 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. 1Facultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, San Luis Potosi, SLP, Mexico
  2. 2Servicio de Farmacia, Hospital Universitario Severo Ochoa, Leganés, Spain
  3. 3Facultad de Farmacia, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid, Spain
  4. 4Hospital Central “Dr Ignacio Morones Prieto”, San Luis Potosí, Mexico
  5. 5Universidad 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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