Table 3 Characteristics of the included studies for target AUC based on nephrotoxicity
Study | Design of study | Country | Duration of study | Age of patients | Patient’s condition | Definition of AUC values | Target AUC breakpoint |
|---|---|---|---|---|---|---|---|
Chavada 2017 [43] | Retrospective | Australia | 2006–2012 | > 18% of patient age ≥ 70: AKI 50.0% Non-AKI 41.1% | MRSA bacteremia | Values estimated by the maximum a posteriori Bayesian estimation, using a priori pharmacokinetic parameters of a previous population pharmacokinetic model | ≥ 563 |
Zasowski 2018 [44] | Retrospective | America | 2014–2015 | > 18 Mean ± SD: 61.7 ± 16.8 | Confirmed or suspected bacteremiaor pneumonia | Values estimated via the maximum a posteriori probability Bayesian function using a previously published 2-compartment population pharmacokinetic model as the Bayesian prior | ≥ 683 |
Meng 2019 [15] | Prospective | America | 2018 | ≥18 Median ± SD (IQR): AKI 51 ± 19 (37–62) Non-AKI 63 ± 17 (50–69) | Pulmonary, skin and soft tissue infection, osteoarticular, febrile neutropenia, abdominal, pelvic, intrathoracic, bacteremia, central nervous system, endocarditis, cardiovascular implantable, electronic device infections, vascular graft | Values obtained by a Stanford hospital–specific spreadsheet calculator with prebuilt pharmacokinetic equations using Microsoft Excel (http://med.stanford.edu/bugsanddrugs.html) | ≥ 600 |
Brunetti 2020 [45] | Retrospective | America | 2011–2018 | ≥18 Mean ± SD: 57 ± 16.4 | N/A | Values estimated by DoseMe software, which uses a Bayesian approach | > 600 |
Lodise 2020 [46] | Prospective | America | 2014–2015 | ≥18 Mean ± SD: 60.7 ± 17.3 | MRSA bloodstream infection | Values estimated post hoc using the maximal a posteriori probability procedure | ≥ 550 |