Unraveling Ceftriaxone Dosing: Free Drug Prediction, Threshold Optimization, and Model Validation
Résumé
Ceftriaxone is pivotal in treating severe infections; however, predicting unbound plasma ceftriaxone (CEFu) from total ceftriaxone (CEFtot) remains challenging. This study aimed to (1) predict CEFu from CEFtot, (2) determine optimal target for CEFtot trough concentration in plasma, (3) perform an external validation of published models, and (4) to ascertain whether the CEF dosing regimen was sufficient to achieve the therapeutic objectives. CEFu predictions based on CEFtot were evaluated using previously published models. Optimal CEFtot targets for an MIC of 1mg/L were calculated to achieve CEFu concentrations above MIC and 4xMIC 100% of the time. External validation was conducted assessing serum albumin, CEFtot and CEFu and comparing predicted CEFu across models. Retrospective data, comprising 408 CEFtot from 222 patients, were analyzed to assess the probability of target attainment (PTA) based on model predicted CEFu. CEFu predictions based on CEFtot were evaluated using previously published models. Optimal CEFtot targets for an MIC of 1mg/L were calculated to achieve CEFu concentrations above MIC and 4xMIC 100% of the time. External validation was conducted assessing serum albumin, CEFtot and CEFu and comparing predicted CEFu across models. Retrospective data, comprising 408 CEFtot from 222 patients, were analyzed to assess the probability of target attainment (PTA) based on model predicted CEFu. Optimal CEFtot trough concentration targets ranged from 2.0 mg/L to 16.9 mg/L (1xMIC) and from 7.9 mg/L to 56.2 mg/L (4xMIC) across models. Some models accurately predicted CEFu obtained from prospective external validation. In the retrospective cohort, PTA ranged from 94.4% to 98.7% for 1xMIC and from 66.9% to 97.3% for 4xMIC. Modeling or direct quantification of CEFu may improve patient outcomes, but achieving this requires standardized analytical approaches and further research to assess the ability of published models to accurately predict CEFu.