Novel visualization techniques towards identification of atherosclerotic patients at risk

Atherosclerosis is an inflammatory disease of the artery wall. The genesis of atherosclerosis is associated with auto-immune diseases (the immune system turns against constituents of the own body) such as rheumatoid arthritis (RA). Atherosclerosis is a chronic process that leads to the formation of atherosclerotic plaques, making the bloodvessels thinning. In part I is several markers for early atherosclerosis have been examined. Examples of this are the endothelial precursor cells (EPCs), vascular endothelial growth factors (VEGF), soluble vascular cell adhesion molecul-1 (sVCAM-1), trombomoduline (TM or CD141) and von Willebrand Factor (vWF), measured in the blood. The vascular elasticity can also be measured (small arterial elasticity; SAE) and the advanced glycation end products (AGEs; measured by the skin with fluorescence (skin AF)). These are related to disease activity in patients with RA, and therefore can be used as risk estimation of cardiovascular disease. As plaques become vulnerable and rupture, which is directly related to cardiovascular diseases, such as a heart attack or stroke. A character of a vulnerable plaque is inflammation. Possible macrophages (a inflammatory cell) can be used to identify inflammatory responses in atherosclerosis, to predict the risk of cardiovascular disease. In part 2, this is done using folate receptor-beta, which is on the surface of activated macrophages, by means of an optical fluorescent contrast agent (FITC) and technetium. Also, matrix metalloproteinases (MMPs; proteins that break down the matrix) have been made visible

Patient-reported physical functioning is limited in almost half of critical illness survivors 1-year after ICU-admission:A retrospective single-centre study

Post-intensive care unit (ICU) sequelae, including physical and mental health problems, are relatively unexplored. Characteristics commonly used to predict outcome lack prognostic value when it comes to long-term physical recovery. Therefore, the objective of this study was to assess the incidence of non-recovery in long-stay ICU-patients. In this single-centre study, retrospective data of adults with an ICU stay >48 hours who visited the specialized post-ICU clinic, and completed the Dutch RAND 36-item Short Form questionnaire at 3 and 12 months post-ICU, were retrieved from electronic patient records. In cases where physical functioning scores at 12 months were below reference values, patients were allocated to the physical non-recovery (NR) group. Significantly different baseline and (post-)ICU-characteristics were assessed for correlations with physical recovery at 12 months post-ICU. Of 250 patients, 110 (44%) fulfilled the criteria for the NR-group. Neither the severity of illness, type of admission, nor presence of sepsis did not differ between groups. However, NR-patients had a higher age, were more often female, and had a higher incidence of co-morbidities. Shorter LOS ICU, lower incidence of medical comorbidities, and better physical performance at 3 months were significantly correlated with 1-year physical recovery. Comorbidities and reduced physical functioning at 3 months were identified as independent risk-factors for long-term physical non-recovery. In conclusion, a substantial proportion of long-stay ICU-patients who visited the standard care post-ICU clinic did not fulfil the criteria for full physical recovery at 12 months post-ICU. Commonly used ICU-characteristics, such as severity of illness, do not have sufficient prognostic value when it comes to long-term recovery of health-related quality of life

Today’s children tomorrow’s changemakers: educational resources to develop entrepreneurial skills

[EN] Entrepreneurship is one of the main objectives of the European Union to ensure that people have the right skills for jobs. It is also part of the Key Competences for Lifelong Learning. Different stakeholders should be involved in fostering entrepreneurial skills. These skills need to be developed from an early stage. For this reason, teachers and parents have an essential responsibility in helping students gain entrepreneurial skills. This work describes the project Today’s Children Tomorrow’s Changemakers; a European funded project focused on developing entrepreneurial skills at primary school through a set of educational resources for teachers and children

Optimization of flucloxacillin dosing regimens in critically ill patients using population pharmacokinetic modelling of total and unbound concentrations

Background: Initial appropriate anti-infective therapy is associated with improved outcomes in patients with severe infections. In critically ill patients, altered pharmacokinetic (PK) behaviour is common and known to influence the achievement of PK/pharmacodynamic targets. Objectives: To describe population PK and optimized dosing regimens for flucloxacillin in critically ill patients. Methods: First, we developed a population PK model, estimated between-patient variability (BPV) and identified covariates that could explain BPV through non-linear mixed-effects analysis, using total and unbound concentrations obtained from 35 adult critically ill patients treated with intermittent flucloxacillin. Second, we validated the model using external datasets from two different countries. Finally, frequently prescribed dosing regimens were evaluated using Monte Carlo simulations. Results: A two-compartment model with non-linear protein binding was developed and validated. BPV of the maximum binding capacity decreased from 42.2% to 30.4% and BPV of unbound clearance decreased from 88.1% to 71.6% upon inclusion of serumalbumin concentrations and estimated glomerular filtration rate (eGFR; by CKD-EPI equation), respectively. PTA (target of 100%fT(>MIC)) was 91% for patients with eGFR of 33mL/min and 1 g q6h, 87% for patients with eGFR of 96 mL/min and 2 g q4h and 71% for patients with eGFR of 153 mL/min and 2 g q4h. Conclusions: For patients with high creatinine clearance who are infected with moderately susceptible pathogens, therapeutic drug monitoring is advised since there is a risk of underexposure to flucloxacillin. Due to the non-linear protein binding of flucloxacillin and the high prevalence of hypoalbuminaemia in critically ill patients, dose adjustments should be based on unbound concentrations

A meta-analysis of protein binding of flucloxacillin in healthy volunteers and hospitalized patients

Objectives: The aim of this study was to develop a mechanistic protein-binding model to predict the unbound flucloxacillin concentrations in different patient populations. Methods: A mechanistic protein-binding model was fitted to the data using non-linear mixed-effects modelling. Data were obtained from four datasets, containing 710 paired total and unbound flucloxacillin concentrations from healthy volunteers, non-critically ill and critically ill patients. A fifth dataset with data from hospitalized patients was used for evaluation of our model. The predictive performance of the mechanistic model was evaluated and compared with the calculation of the unbound concentration with a fixed unbound fraction of 5%. Finally, we performed a fit-for-use evaluation, verifying whether the model-predicted unbound flucloxacillin concentrations would lead to clinically incorrect dose adjustments. Results: The mechanistic protein-binding model predicted the unbound flucloxacillin concentrations more accurately than assuming an unbound fraction of 5%. The mean prediction error varied between -26.2% to 27.8% for the mechanistic model and between -30.8% to 83% for calculation with a fixed factor of 5%. The normalized root mean squared error varied between 36.8% and 69% respectively between 57.1% and 134%. Predicting the unbound concentration with the use of the mechanistic model resulted in 6.1% incorrect dose adjustments versus 19.4% if calculated with a fixed unbound fraction of 5%. Conclusions: Estimating the unbound concentration with a mechanistic protein-binding model outperforms the calculation with the use of a fixed protein binding factor of 5%, but neither demonstrates acceptable performance. When performing dose individualization of flucloxacillin, this should be done based on measured unbound concentrations rather than on estimated unbound concentrations from the measured total concentrations. In the absence of an assay for unbound concentrations, the mechanistic binding model should be preferred over assuming a fixed unbound fraction of 5%