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Background Early and appropriate antibiotic dosing is associated with improved clinical outcomes in critically ill patients, yet target attainment remains a challenge. Traditional antibiotic dosing is not suitable in critically ill patients, since these patients undergo physiological alterations that strongly affect antibiotic exposure. For beta-lactam antibiotics, the unbound plasma concentrations above at least one to four times the minimal inhibitory concentration (MIC) for 100% of the dosing interval (100%ƒT > 1–4×MIC) have been proposed as pharmacodynamic targets (PDTs) to maximize bacteriological and clinical responses. The objectives of this study are to describe the PDT attainment in critically ill patients and to identify risk factors for target non-attainment. Methods This prospective observational study was performed in two ICUs in the Netherlands. We enrolled adult patients treated with the following beta-lactam antibiotics: amoxicillin (with or without clavulanic acid), cefotaxime, ceftazidime, ceftriaxone, cefuroxime, and meropenem. Based on five samples within a dosing interval at day 2 of therapy, the time unbound concentrations above the epidemiological cut-off (ƒT > MIC ECOFF and ƒT > 4×MIC ECOFF ) were determined. Secondary endpoints were estimated multivariate binomial and binary logistic regression models, for examining the association of PDT attainment with patient characteristics and clinical outcomes. Results A total of 147 patients were included, of whom 63.3% achieved PDT of 100%ƒT > MIC ECOFF and 36.7% achieved 100%ƒT > 4×MIC ECOFF . Regression analysis identified male gender, estimated glomerular filtration rate (eGFR) ≥ 90 mL/min/1.73 m 2 , and high body mass index (BMI) as risk factors for target non-attainment. Use of continuous renal replacement therapy (CRRT) and high serum urea significantly increased the probability of target attainment. In addition, we found a significant association between the 100%ƒT > MIC ECOFF target attainment and ICU length of stay (LOS), but no significant correlation was found for the 30-day survival. Conclusions Traditional beta-lactam dosing results in low target attainment in the majority of critically ill patients. Male gender, high BMI, and high eGFR were significant risk factors for target non-attainment. These predictors, together with therapeutic drug monitoring, may help ICU clinicians in optimizing beta-lactam dosing in critically ill patients. Trial registration Netherlands Trial Registry (EXPAT trial), NTR 5632 . Registered on 7 December 2015.

Discharge from the intensive care unit (ICU) is a high-risk process, leading to numerous potentially harmful medication transfer errors (PH-MTE). PH-MTE could be prevented by medication reconciliation by ICU pharmacists, but resources are scarce, which renders the need for predicting which patients are at risk for PH-MTE. The aim of this study was to develop a prognostic multivariable model in patients discharged from the ICU to predict who is at increased risk for PH-MTE after ICU discharge, using predictors of PH-MTE that are readily available at the time of ICU discharge. Data for this study were derived from the Transfer ICU Medication reconciliation study, which included ICU patients and scored MTE at discharge of the ICU. The potential harm of every MTE was estimated with a validated score, where after MTE with potential for harm were indicated as PH-MTE. Predictors for PH-MTE at ICU discharge were identified using LASSO regression. The c statisticprovided a measure of the overall discriminative ability of the prediction model and the prediction model was internally validated by bootstrap resampling. Based on sensitivity and specificity, the cut-off point of the prediction model was determined. The cohort contained 258 patients and six variables were identified as predictors for PH-MTE: length of ICU admission, number of home medications and patient taking one of the following medication groups at home: vitamin/mineral supplements, cardiovascular medication, psycholeptic/analeptic medication and medication for obstructive airway disease. The c of the final prediction model was 0.73 (95%CI 0.67-0.79) and decreased to 0.62 according to bootstrap resampling. At a cut-off score of two the prediction model yielded a sensitivity of 70% and a specificity of 61%. A multivariable prediction model was developed to identify patients at risk for PH-MTE after ICU discharge. The model contains predictors that are available on the day of ICU discharge. Once external validation and evaluation of this model in daily practice has been performed, its incorporation into clinical practice could potentially allow institutions to identify patients at risk for PH-MTE after ICU discharge, on the day of ICU discharge, thus allowing for efficient, patient-specific allocation of clinical pharmacy services. Dutch trial register: NTR4159, 5 September 2013, retrospectively registered.

PurposeIndividualising drug dosing using model-informed precision dosing (MIPD) of beta-lactam antibiotics and ciprofloxacin has been proposed as an alternative to standard dosing to optimise antibiotic efficacy in critically ill patients. However, randomised clinical trials (RCT) on clinical outcomes have been lacking.MethodsThis multicentre RCT, including patients admitted to the intensive care unit (ICU) who were treated with antibiotics, was conducted in eight hospitals in the Netherlands. Patients were randomised to MIPD with dose and interval adjustments based on monitoring serum drug levels (therapeutic drug monitoring) combined with pharmacometric modelling of beta-lactam antibiotics and ciprofloxacin. The primary outcome was ICU length of stay (LOS). Secondary outcomes were ICU mortality, hospital mortality, 28-day mortality, 6-month mortality, delta sequential organ failure assessment (SOFA) score, adverse events and target attainment.ResultsIn total, 388 (MIPD n = 189; standard dosing n = 199) patients were analysed (median age 64 [IQR 55–71]). We found no significant differences in ICU LOS between MIPD compared to standard dosing (10 MIPD vs 8 standard dosing; IRR = 1.16; 95% CI 0.96–1.41; p = 0.13). There was no significant difference in target attainment before intervention at day 1 (T1) (55.6% MIPD vs 60.9% standard dosing; p = 0.24) or at day 3 (T3) (59.5% vs 60.4%; p = 0.84). There were no significant differences in other secondary outcomes.ConclusionsWe could not show a beneficial effect of MIPD of beta-lactam antibiotics and ciprofloxacin on ICU LOS in critically ill patients. Our data highlight the need to identify other approaches to dose optimisation.

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.

Background The role of haloperidol as treatment for ICU delirium and related symptoms remains controversial despite two recent large controlled trials evaluating its efficacy and safety. We sought to determine whether haloperidol when compared to placebo in critically ill adults with delirium reduces days with delirium and coma and improves delirium-related sequelae. Methods This multi-center double-blind, placebo-controlled randomized trial at eight mixed medical-surgical Dutch ICUs included critically ill adults with delirium (Intensive Care Delirium Screening Checklist ≥ 4 or a positive Confusion Assessment Method for the ICU) admitted between February 2018 and January 2020. Patients were randomized to intravenous haloperidol 2.5 mg or placebo every 8 h, titrated up to 5 mg every 8 h if delirium persisted until ICU discharge or up to 14 days. The primary outcome was ICU delirium- and coma-free days (DCFDs) within 14 days after randomization. Predefined secondary outcomes included the protocolized use of sedatives for agitation and related behaviors, patient-initiated extubation and invasive device removal, adverse drug associated events, mechanical ventilation, ICU length of stay, 28-day mortality, and long-term outcomes up to 1-year after randomization. Results The trial was terminated prematurely for primary endpoint futility on DSMB advice after enrolment of 132 (65 haloperidol; 67 placebo) patients [mean age 64 (15) years, APACHE IV score 73.1 (33.9), male 68%]. Haloperidol did not increase DCFDs (adjusted RR 0.98 [95% CI 0.73–1.31], p  = 0.87). Patients treated with haloperidol (vs. placebo) were less likely to receive benzodiazepines (adjusted OR 0.41 [95% CI 0.18–0.89], p  = 0.02). Effect measures of other secondary outcomes related to agitation (use of open label haloperidol [OR 0.43 (95% CI 0.12–1.56)] and other antipsychotics [OR 0.63 (95% CI 0.29–1.32)], self-extubation or invasive device removal [OR 0.70 (95% CI 0.22–2.18)]) appeared consistently more favorable with haloperidol, but the confidence interval also included harm. Adverse drug events were not different. Long-term secondary outcomes (e.g., ICU recall and quality of life) warrant further study. Conclusions Haloperidol does not reduce delirium in critically ill delirious adults. However, it may reduce rescue medication requirements and agitation-related events in delirious ICU patients warranting further evaluation. Trial registration : ClinicalTrials.gov (#NCT03628391), October 9, 2017.

Background For specific medical specialties it has been shown that clinical pharmacists can have a beneficial effect on the reduction of drug-related problems by performing medication reviews. However, little is known on the cost–benefit ratio of hospital-wide implementation of medication reviews. Aim To investigate the effect of conducting hospital-wide medication reviews on the detection and resolution of drug-related problems, and to calculate the cost–benefit ratio of the intervention. Method In this observational prospective period prevalence study, medication reviews were conducted during five consecutive working days in a Dutch university hospital. Patients admitted for more than 24 h were included. The cost–benefit ratio of conducting the medication reviews was calculated by dividing the total costs by the total savings. Results In 622 medication reviews, 709 potential drug-related problems (1.1 per patient) were detected. The most common advice was to stop medication (38.6%). Patients with a potentially drug-related problem were significantly older, had a higher median number of prescriptions, and the median number of days from admission to the time of medication reviews was longer. Conducting medication reviews showed a positive cost–benefit ratio of 9.7. Conclusions Hospital-wide medication reviews by clinical pharmacists have a positive cost–benefit ratio and contribute to the detection and the resolution of drug related problems (DRPs), mainly by reducing overtreatment.

PurposeTo develop and validate a population pharmacokinetic model of ciprofloxacin intravenously in critically ill patients, and determine target attainment to provide guidance for more effective regimens.MethodsNon-linear mixed-effects modelling was used for the model development and covariate analysis. Target attainment of an ƒAUC0–24/MIC ≥ 100 for different MICs was calculated for standard dosing regimens. Monte Carlo simulations were performed to define the probability of target attainment (PTA) of several dosing regimens.ResultsA total of 204 blood samples were collected from 42 ICU patients treated with ciprofloxacin 400–1200 mg/day, with median values for age of 66 years, APACHE II score of 22, BMI of 26 kg/m2, and eGFR of 58.5 mL/min/1.73 m2. The median ƒAUC0–24 and ƒCmax were 29.9 mg•h/L and 3.1 mg/L, respectively. Ciprofloxacin pharmacokinetics were best described by a two-compartment model. We did not find any significant covariate to add to the structural model. The proportion of patients achieving the target ƒAUC0–24/MIC ≥ 100 were 61.9% and 16.7% with MICs of 0.25 and 0.5 mg/L, respectively. Results of the PTA simulations suggest that a dose of ≥ 1200 mg/day is needed to achieve sufficient ƒAUC0–24/MIC ratios.ConclusionsThe model described the pharmacokinetics of ciprofloxacin in ICU patients adequately. No significant covariates were found and high inter-individual variability of ciprofloxacin pharmacokinetics in ICU patients was observed. The poor target attainment supports the use of higher doses such as 1200 mg/day in critically ill patients, while the variability of inter-individual pharmacokinetics parameters emphasizes the need for therapeutic drug monitoring to ensure optimal exposure.

[...]ARC may be under-recognized during daily ICU rounds. [...]to make a bedside assessment whether ARC plays a role in your patient and how to act next, the following questions must be answered: why should I think of ARC, how can I identify ARC, when should I think of ARC, and what should I do with ARC (Fig. 1)? 2 Why should I think of ARC? [...]in order to optimize patient treatment and outcome, being aware of ARC should lead to assessment of patients at risk and subsequently individualization of drug dosing. 3 How can I identify ARC? [...]a young age of <50 years is clearly associated with a high risk of ARC, which is also consistent with the renal functional reserve being higher in younger patients. [...]there is also a significant proportion of patients who do not meet the ARC definition, but show a relevant increase in CrCl during their hospital stay resulting in possible subtherapeutic drug concentrations [26]. [...]we advocate a definition that takes into account an increase towards baseline CrCl as retrieved from hospital database or from the moment of ICU admission. [...]ARC is not a static phenomenon and can change over time [6].