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Introduction: Vancomycin is used in intensive care unit (ICU) patients for the treatment of infections caused by gram-positive bacteria. The vancomycin pharmacokinetic/pharmacodynamic index is a ratio of the area under the concentration to the minimum inhibitory concentration ≥400-600 h∗mg/L. This target can generally be achieved by a plasma concentration of 20-25 mg/L. Together with the pathophysiological alterations and pharmacokinetic variability associated with critical illness, the use of continuous renal replacement therapy (CRRT) may complicate the attainment of adequate vancomycin concentrations. The primary objective was the prevalence of attainment of vancomycin concentrations 20-25 mg/L after 24 h in adult ICU patients receiving CRRT. Secondary outcomes were to evaluate target attainment at days 2 and 3 and to calculate vancomycin clearance (CL) by CRRT and residual diuresis. Methods: We performed a prospective observational study in adult ICU patients on CRRT, which received at least 24 h continuous infusion of vancomycin. Between May 2020 and February 2021, daily vancomycin residual blood gas and dialysate samples were collected from 20 patients, every 6 h and if possible vancomycin urine samples. Vancomycin was analysed with an immunoassay method. The CL by CRRT was calculated by a different approach correcting for the downtime and providing insight into the degree of filter patency. Results: The proportion of patients with vancomycin concentrations <20 mg/L was 50% 24 h after starting vancomycin (n = 10). No differences were observed in patient characteristics. The target vancomycin concentration 20-25 mg/L was only achieved in 30% of the patients. On days 2 and 3, despite the use of TDM and albeit in lower percentages, sub- and supratherapeutic levels were still observed. Taking downtime and filter patency into account resulted in lower vancomycin CL. Conclusions: 50% of the studied ICU patients on CRRT showed subtherapeutic vancomycin concentr

The number of pharmacokinetic (PK) models of meropenem is increasing. However, the daily role of these PK models in the clinic remains unclear, especially for critically ill patients. Therefore, we evaluated the published meropenem models on real-world ICU data to assess their suitability for use in clinical practice. All models were built in NONMEM and evaluated using prediction and simulation-based diagnostics for the ability to predict the subsequent meropenem concentrations without plasma concentrations (a priori), and with plasma concentrations (a posteriori), for use in therapeutic drug monitoring (TDM). Eighteen PopPK models were included for evaluation. The a priori fit of the models, without the use of plasma concentrations, was poor, with a prediction error (PE)% of the interquartile range (IQR) exceeding the ±30% threshold. The fit improved when one to three concentrations were used to improve model predictions for TDM purposes. Two models were in the acceptable range with an IQR PE% within ±30%, when two or three concentrations were used. The role of PK models to determine the starting dose of meropenem in this population seems limited. However, certain models might be suitable for TDM-based dose adjustment using two to three plasma concentrations.

Aims: Nadroparin is administered to COVID-19 intensive care unit (ICU) patients as thromboprophylaxis. Despite existing population pharmacokinetic (PK) models for nadroparin in literature, the population PK of nadroparin in COVID-19 ICU patients is unknown. Moreover, optimal dosing regimens achieving anti-Xa target levels (0.3–0.7 IU/mL) are unknown. Therefore, a population PK analysis was conducted to investigate different dosing regimens of nadroparin in COVID-19 ICU patients. Methods: Anti-Xa levels (n = 280) from COVID-19 ICU patients (n = 65) receiving twice daily (BID) 5700 IU of subcutaneous nadroparin were collected to perform a population PK analysis with NONMEM v7.4.1. Using Monte Carlo simulations (n = 1000), predefined dosing regimens were evaluated. Results: A 1-compartment model with an absorption compartment adequately described the measured anti-Xa levels with interindividual variability estimated for clearance (CL). Inflammation parameters C-reactive protein, D-dimer and estimated glomerular filtration rate based on the Chronic Kidney Disease Epidemiology Collaboration equation allowed to explain the interindividual variability of CL. Moreover, CL was decreased in patients receiving corticosteroids (22.5%) and vasopressors (25.1%). Monte Carlo simulations demonstrated that 5700 IU BID was the most optimal dosing regimen of the simulated regimens for achieving prespecified steady-state t = 4 h anti-Xa levels with 56.7% on target (0.3–0.7 IU/mL). Conclusion: In our study, clearance of nadroparin is associated with an increase in inflammation parameters, use of corticosteroids, vasopression and renal clearance in critically ill patients. Furthermore, of the simulated regimens, targeted anti-Xa levels were most adequately achieved with a dosing regimen of 5700 IU BID. Future studies are needed to elucidate the underlying mechanisms of found covariate relationships.

Objectives Intravenous admixture preparation errors (IAPEs) may lead to patient harm. The primary aim of this study was to assess the effect of a pharmacy-based centralized intravenous admixture service (CIVAS) on IAPEs. Methods We conducted a before-and-after study in 3 clinical wards before CIVAS implementation and in the CIVAS unit 18 months after implementation. Intravenous admixture preparation error data were collected by disguised observation. The primary outcome was the proportion of admixtures with 1 or more IAPEs. Secondary outcomes were the type and potential severity of IAPEs, noncompliance to hygiene procedures, and nursing staff satisfaction with the CIVAS. The primary outcome was analyzed using a multivariable mixed-effects logistic regression model. Results One or more IAPEs were identified in 14 of 543 admixtures (2.6%) in the CIVAS unit and in 148 of 282 admixtures (52.5%) in the clinical wards (odds ratio, 0.02; 95% confidence interval, 0.004-0.05). The most common IAPE types were wrong solvent or diluent (n=95) and wrong volume of infusion fluid (n=45). No potentially harmful IAPEs occurred in the CIVAS unit as opposed to 22 (7.8%) in the clinical wards. Disinfection procedures were better adhered to in the CIVAS unit. Overall nurse satisfaction with the CIVAS increased from a median of 70 (n=166) 5 months after intervention to 77 (n=115) 18 months after intervention (P < 0.001) on a 100-point scale. Conclusions Centralized intravenous admixture service performed notably better than the clinical wards with regard to IAPEs and noncompliance to hygiene procedures. Nurses were satisfied with the CIVAS. Hence, the implementation of CIVAS is an important strategy to improve medication safety in hospitals.

Background Intravenous admixture preparation errors (IAPEs) may lead to patient harm. Insight into the prevalence as well as the determinants associated with these IAPEs is needed to elicit preventive measures. Aim The primary aim of this study was to assess the prevalence of IAPEs. Secondary aims were to identify the type, severity, and determinants of IAPEs. Method A prospective observational study was performed in a Dutch university hospital. IAPE data were collected by disguised observation. The primary outcome was the proportion of admixtures with one or more IAPEs. Descriptive statistics were used for the prevalence, type, and severity of IAPEs. Mixed-effects logistic regression analyses were used to estimate the determinants of IAPEs. Results A total of 533 IAPEs occurred in 367 of 614 admixtures (59.8%) prepared by nursing staff. The most prevalent errors were wrong preparation technique (n = 257) and wrong volume of infusion fluid (n = 107). Fifty-nine IAPEs (11.1%) were potentially harmful. The following variables were associated with IAPEs: multistep versus single-step preparations (adjusted odds ratio [ORadj] 4.08, 95% confidence interval [CI] 2.27–7.35); interruption versus no interruption (ORadj 2.32, CI 1.13–4.74); weekend versus weekdays (ORadj 2.12, CI 1.14–3.95); time window 2 p.m.-6 p.m. versus 7 a.m.-10 a.m. (ORadj 3.38, CI 1.60–7.15); and paediatric versus adult wards (ORadj 0.14, CI 0.06–0.37). Conclusion IAPEs, including harmful IAPEs, occurred frequently. The determinants associated with IAPEs point to factors associated with preparation complexity and working conditions. Strategies to reduce the occurrence of IAPEs and therefore patient harm should target the identified determinants.

Background and Objective: Many patients treated for COVID-19 related acute respiratory distress syndrome in the intensive care unit are sedated with the benzodiazepine midazolam. Midazolam undergoes extensive metabolism by CYP3A enzymes, which may be inhibited by hyperinflammation. Therefore, an exaggerated proinflammatory response, as often observed in COVID-19, may decrease midazolam clearance. To develop a population pharmacokinetic model for midazolam in adult intensive care unit patients infected with COVID-19 and to assess the effect of inflammation, reflected by IL-6, on the pharmacokinetics of midazolam. Methods: Midazolam blood samples were collected once a week between March 31 and April 30 2020. Patients were excluded if they concomitantly received CYP3A4 inhibitors, CYP3A4 inducers and/or continuous renal replacement therapy. Midazolam and metabolites were analyzed with an ultra-performance liquid chromatography–tandem mass spectrometry method. A population pharmacokinetic model was developed, using nonlinear mixed effects modelling. IL-6 and CRP, markers of inflammation, were analyzed as covariates. Results: The data were described by a one-compartment model for midazolam and the metabolites 1-OH-midazolam and 1-OH-midazolam-glucuronide. The population mean estimate for midazolam clearance was 6.7 L/h (4.8–8.5 L/h). Midazolam clearance was reduced by increased IL-6 and IL-6 explained more of the variability within our patients than CRP. The midazolam clearance was reduced by 24% (6.7–5.1 L/h) when IL-6 increases from population median 116 to 300 pg/mL. Conclusions: Inflammation, reflected by high IL-6, reduces midazolam clearance in critically ill patients with COVID-19. This knowledge may help avoid oversedation, but further research is warranted.

PURPOSE: Early initiation of antibiotics is essential for ameliorating infections in critically ill patients. The correct dosage of antibiotics is imperative to ensure their adequate exposure. Critically ill patients have altered pharmacokinetic parameters and are often infected by less susceptible microorganisms. Differences in drug disposition are not considered with standard doses of antibiotics. This can lead to suboptimal antibiotic exposure in critically ill patients. To overcome this problem of suboptimal dosing, therapeutic drug monitoring (TDM) is a strategy commonly used to support individualized dosing of antibiotics. It is routinely used for vancomycin and aminoglycosides in clinical practice. In recent years, it has become apparent that TDM may also be used in other antibiotics. METHODS: This review summarizes the evidence for TDM of antibiotics in critically ill patients, focuses on clinical outcomes, and summarizes possibilities for optimized TDM in the future. RESULTS AND CONCLUSION: After reviewing the literature, we can conclude that general TDM implementation is advised for glycopeptides and aminoglycosides, as evidence of the relationship between TDM and clinical outcome is present. For antibiotics, such as beta-lactams, fluoroquinolones, and linezolid, it seems rational to perform TDM in specific patient cases. TDM involving other antibiotics is supported by individual cases, specifically to decrease toxicity. When focusing on future possibilities to improve TDM of antibiotics in critically ill patients, implementation of model-informed precision dosing should be investigated because it can potentially streamline the TDM process. The logistics of TDM, such as turnaround time and available equipment, are challenging but may be overcome by rapid bioanalytical techniques or real-time monitoring of drug concentrations through biosensors in the future. Education, clinical information on targets, and clinical outcome studies are other important fac

PURPOSE: Innovative and sustainable sampling strategies for bioanalytical quantification of drugs and metabolites have gained considerable interest. Scavenging can be stratified as a sustainable sampling strategy using residual material because it aligns with the green principles of waste reduction and sampling optimization. Scavenged sampling includes all biological fluids' (eg, blood, liquor, and urine) leftover from standard clinical care. This review elaborates on the past and current landscape of sustainable sampling within therapeutic drug monitoring, with a focus on scavenged sampling. METHODS: In February 2021, 4 databases were searched to assess the literature on the clinical use of innovative and sustainable sampling techniques without applying publication date restrictions. Studies reporting the clinical use of scavenged blood sampling and bridging studies of scavenged sampling and normal blood sampling were eligible for inclusion. RESULTS: Overall, 19 eligible studies concerning scavenged sampling were identified from 1441 records. Scavenged sampling is mainly applied in the pediatric population, although other patient groups may benefit from this strategy. The infrastructure required for scavenged sampling encounters several challenges, including logistic hurdles, storage and handling conditions, and documentation errors. A workflow is proposed with identified opportunities that guide the implementation of scavenged sampling. CONCLUSIONS: This review presents current evidence on the clinical use of scavenged sampling strategies. Scavenged sampling can be a suitable approach for drug quantification to improve dosage regimens, perform pharmacokinetic studies, and explore the value of therapeutic drug monitoring without additional sample collection.

BACKGROUND: Optimizing beta-lactam antibiotic treatment is a promising method to reduce the length of intensive care unit (ICU) stay and therefore reduce ICU costs. We used data from the EXPAT trial to determine whether beta-lactam antibiotic target attainment is a cost determinant in the ICU. METHODS: Patients included in the EXPAT trial were divided into target attainment and target nonattainment based on serum antibiotic levels. All hospital costs were extracted from the hospital administration system and categorized. RESULTS: In total, 79 patients were included in the analysis. Target attainment showed a trend toward higher total ICU costs (€44,600 versus €28,200, P = 0.103). This trend disappeared when correcting for ICU length of stay (€2680 versus €2700). Renal replacement therapy was the most important cost driver. CONCLUSIONS: Target attainment for beta-lactam antibiotics shows a trend toward higher total costs in ICU patients, which can be attributed to the high costs of a long stay in the ICU and renal replacement therapy.

Background: Voriconazole is one of the first‐line therapies for invasive pulmonary aspergillosis. Drug concentrations might be significantly influenced by the use of extracorporeal membrane oxygenation (ECMO). We aimed to assess the effect of ECMO on voriconazole exposure in a large patient population. Methods: Critically ill patients from eight centers in four countries treated with voriconazole during ECMO support were included in this retrospective study. Voriconazole concentrations were collected in a period on ECMO and before/after ECMO treatment. Multivariate analyses were performed to evaluate the effect of ECMO on voriconazole exposure and to assess the impact of possible saturation of the circuit’s binding sites over time. Results: Sixty‐nine patients and 337 samples (190 during and 147 before/after ECMO) were analyzed. Subtherapeutic concentrations (<2 mg/L) were observed in 56% of the samples during ECMO and 39% without ECMO (p = 0.80). The median trough concentration, for a similar daily dose, was 2.4 (1.2–4.7) mg/L under ECMO and 2.5 (1.4–3.9) mg/L without ECMO (p = 0.58). Extensive inter‐and intrasubject variability were observed. Neither ECMO nor squared day of ECMO (saturation) were retained as significant covariates on voriconazole exposure. Conclusions: No significant ECMO‐effect was observed on voriconazole exposure. A large proportion of patients had voriconazole subtherapeutic concentrations.

Background: After liver transplantation (LTx), adherence to immunosuppressive medication and avoidance of contra-indicated drugs is essential for long-term survival. This study aimed to investigate the prevalence, types and severity of medication-related problems (MRPs) and interventions initiated by a clinical pharmacist (CP) in a cohort of LTx recipients in the outpatient setting. Method: This study was a retrospective, observational study in LTx recipients that visited the outpatient clinic for an annual check-up. A 20-minutes consultation with a CP consisted of medication reconciliation and consultation about medication, adherence, and adverse drug reactions (ADRs). Discrepancies between actual and intended drug use, and MRPs were identified and the severity of MRPs was assessed. Potential interventions were discussed with the patient and the treating physician and evaluated after one year. Results: The CP counseled 64 LTx recipients and found 96 discrepancies in 37 patients. Most discrepancies (60.4%, n = 58) concerned missing medications. In total, 98 MRPs were identified in 53 patients (median 2; range 1-5 per patient), with a total of 113 interventions. Most frequent MRPs were: ADRs (22.4%, n = 22), nonadherence (19.3%, n = 19), unnecessary drugs (16.3%, n = 16) and undertreatment (12.2%, n = 12). Interventions most frequently proposed included optimization of dosage regimen (21.2%, n = 24), individualized recommendation regarding compliance (16.8%, n = 19) and drug discontinuation (12.4%, n = 14). After one year, 15 of the 19 patients (79%) experienced no longer compliance issues and 27 of the 29 patients (93%) used no drugs with indication issues anymore. Conclusion: The CP in an outpatient monitoring program for LTx recipients can signal relevant discrepancies and MRPs. This leads to interventions that are accepted by both the patients and the physicians, with a positive effect after one year.

QTc interval prolongation is an adverse effect associated with the use of fluoroquinolones and macrolides. Ciprofloxacin and erythromycin are both frequently prescribed QTc-prolonging drugs in critically ill patients. Critically ill patients may be more vulnerable to developing QTc prolongation, as several risk factors can be present at the same time. Therefore, it is important to know the QTc-prolonging potential of these drugs in the intensive care unit (ICU) population. The aim of this study was to assess the dynamics of the QTc interval over a 24-hour dose interval during intravenous ciprofloxacin and low-dose erythromycin treatment. Therefore, an observational study was performed in ICU patients (≥18 years) receiving ciprofloxacin 400 mg t.i.d. or erythromycin 100 mg b.i.d. intravenously. Continuous ECG data were collected from 2 h before to 24 h after the first administration. QT-analyses were performed using high-end holter software. The effect was determined with a two-sample t-test for clustered data on all QTc values. A linear mixed model by maximum likelihood was applied, for which QTc values were assessed for the available time intervals and therapy. No evident effect over time on therapy with ciprofloxacin and erythromycin was observed on QTc time. There was no significant difference (p = 0.22) in QTc values between the ciprofloxacin group (mean 393 ms) and ciprofloxacin control group (mean 386 ms). The erythromycin group (mean 405 ms) and erythromycin control group (mean 404 ms) neither showed a significant difference (p = 0.80). In 0.6% of the registrations (1.138 out of 198.270 samples) the duration of the QTc interval was longer than 500 ms. The index groups showed slightly more recorded QTc intervals over 500 ms. To conclude, this study could not identify differences in the QTc interval between the treatments analyzed.