Various studies have reported insufficient beta-lactam concentrations in critically ill patients. The extent to which therapeutic drug monitoring (TDM) in clinical practice can reduce insufficient antibiotic concentrations is an ongoing matter of investigation. We retrospectively evaluated routine meropenem and piperacillin measurements in critically ill patients who received antibiotics as short infusions in the first year after initiating a beta-lactam TDM program. Total trough concentrations above 8.0 mg/L for meropenem and above 22.5 mg/L for piperacillin were defined as the breakpoints for target attainment. We included 1832 meropenem samples and 636 piperacillin samples. We found that 39.3% of meropenem and 33.6% of piperacillin samples did not reach the target concentrations. We observed a clear correlation between renal function and antibiotic concentration (meropenem, r = 0.53; piperacillin, r = 0.63). Patients with renal replacement therapy or creatinine clearance (CrCl) of 100 mL/min that achieved the target concentrations with the maximum recommended dosage. Patients with impaired renal function only required TDM if toxic side effects were noted. In contrast, patients with normal renal function required different dosage regimens and TDM-guided therapy to reach the breakpoints of target attainment.

Purpose Inadequate piperacillin (PIP) exposure in intensive care unit (ICU) patients threatens therapeutic success. Model-informed precision dosing (MIPD) might be promising to individualize dosing; however, the transferability of published models to external populations is uncertain. This study aimed to externally evaluate the available PIP population pharmacokinetic (PopPK) models. Methods A multicenter dataset of 561 ICU patients (11 centers/3654 concentrations) was used for the evaluation of 24 identified models. Model performance was investigated for a priori (A) predictions, i.e., considering dosing records and patient characteristics only, and for Bayesian forecasting, i.e., additionally including the first (B1) or first and second (B2) therapeutic drug monitoring (TDM) samples per patient. Median relative prediction error (MPE) [%] and median absolute relative prediction error (MAPE) [%] were calculated to quantify accuracy and precision. Results The evaluation revealed a large inter-model variability (A: MPE − 135.6–78.3% and MAPE 35.7–135.6%). Integration of TDM data improved all model predictions (B1/B2 relative improvement vs. A: |MPE|_{median_all_models} 45.1/67.5%; MAPE_{median_all_models} 29/39%). The model by Kim et al. was identified to be most appropriate for the total dataset (A/B1/B2: MPE − 9.8/− 5.9/− 0.9%; MAPE 37/27.3/23.7%), Udy et al. performed best in patients receiving intermittent infusion, and Klastrup et al. best predicted patients receiving continuous infusion. Additional evaluations stratified by sex and renal replacement therapy revealed further promising models. Conclusion The predictive performance of published PIP models in ICU patients varied considerably, highlighting the relevance of appropriate model selection for MIPD. Our differentiated external evaluation identified specific models suitable for clinical use, especially in combination with TDM.

Hemadsorption of cytokines is used in critically ill patients with sepsis or septic shock. Concerns have been raised that the cytokine adsorber CytoSorb® unintentionally adsorbs vancomycin. This study aimed to quantify vancomycin elimination by CytoSorb®. Critically ill patients with sepsis or septic shock receiving continuous renal replacement therapy and CytoSorb® treatment during a prospective observational study were included in the analysis. Vancomycin pharmacokinetics was characterized using population pharmacokinetic modeling. Adsorption of vancomycin by the CytoSorb® was investigated as linear or saturable process. The final model was used to derive dosing recommendations based on stochastic simulations. 20 CytoSorb® treatments in 7 patients (160 serum samples/24 during CytoSorb®-treatment, all continuous infusion) were included in the study. A classical one-compartment model, including effluent flow rate of the continuous hemodialysis as linear covariate on clearance, best described the measured concentrations (without CytoSorb®). Significant adsorption with a linear decrease during CytoSorb® treatment was identified (p < 0.0001) and revealed a maximum increase in vancomycin clearance of 291% (initially after CytoSorb® installation) and a maximum adsorption capacity of 572 mg. For a representative patient of our cohort a reduction of the area under the curve (AUC) by 93 mg/L*24 h during CytoSorb® treatment was observed. The additional administration of 500 mg vancomycin over 2 h during CytoSorb® attenuated the effect and revealed a negligible reduction of the AUC by 4 mg/L*24 h. We recommend the infusion of 500 mg vancomycin over 2 h during CytoSorb® treatment to avoid subtherapeutic concentrations. Trial registration NCT03985605. Registered 14 June 2019, https://clinicaltrials.gov/ct2/show/NCT03985605

Severe rhabdomyolysis frequently results in acute kidney injury (AKI) due to myoglobin accumulation with the need of kidney replacement therapy (KRT). The present study investigated whether the application of Cytosorb® (CS) led to an increased rate of kidney recovery in patients with KRT due to severe rhabdomyolysis. Adult patients with a myoglobin-concentration >10,000 ng/ml and KRT were included from 2014 to 2021. Exclusion criteria were chronic kidney disease and CS-treatment before study inclusion. Groups 1 and 2 were defined as KRT with and without CS, respectively. The primary outcome parameter was independence from KRT after 30 days. Propensity score (PS) matching was performed (predictors: myoglobin, SAPS-II, and age), and the chi²-test was used. 35 pairings could be matched (mean age: 57 vs. 56 years; mean myoglobin: 27,218 vs. 26,872 ng/ml; mean SAPS-II: 77 vs. 76). The probability of kidney recovery was significantly (p = .04) higher in group 1 (31.4 vs. 11.4%, mean difference: 20.0%, odds ratio (OR): 3.6). Considering patients who survived 30 days, kidney recovery was also significantly (p = .03) higher in patients treated with CS (61.1 vs. 23.5%, mean difference: 37.6%, OR: 5.1). In conclusion, the use of CS might positively affect renal recovery in patients with severe rhabdomyolysis. A prospective randomized controlled trial is needed to confirm this hypothesis.

The altered pharmacokinetics of renally cleared drugs such as meropenem in critically ill patients receiving continuous renal replacement therapy (CRRT) might impact target attainment. Model-informed precision dosing (MIPD) is applied to individualize meropenem dosing.