Pooled population pharmacokinetic analysis for exploring ciprofloxacin pharmacokinetic variability in intensive care patients

Background and objective: Previous pharmacokinetic (PK) studies of ciprofloxacin in intensive care (ICU) patients have shown large differences in estimated PK parameters, suggesting that further investigation is needed for this population. Hence, we performed a pooled population PK analysis of ciprofloxacin after intravenous administration using individual patient data from three studies. Additionally, we studied the PK differences between these studies through a post-hoc analysis.Methods: Individual patient data from three studies (study 1, 2, and 3) were pooled. The pooled data set consisted of 1094 ciprofloxacin concentration-time data points from 140 ICU patients. Nonlinear mixed-effects modeling was used to develop a population PK model. Covariates were selected following a stepwise covariate modeling procedure. To analyze PK differences between the three original studies, random samples were drawn from the posterior distribution of individual PK parameters. These samples were used for a simulation study comparing PK exposure and the percentage of target attainment between patients of these studies.Results: A two-compartment model with first-order elimination best described the data. Inter-individual variability was added to the clearance, central volume, and peripheral volume. Inter-occasion variability was added to clearance only. Body weight was added to all parameters allometrically. Estimated glomerular filtration rate on ciprofloxacin clearance was identified as the only covariate relationship resulting in a drop in inter-individual variability of clearance from 58.7 to 47.2%. In the post-hoc analysis, clearance showed the highest deviation between the three studies with a coefficient of variation of 14.3% for posterior mean and 24.1% for posterior inter-individual variability. The simulation study showed that following the same dose regimen of 400 mg three times daily, the area under the concentration-time curve of study 3 was the highest with a mean area under the concentration-time curve at 24 h of 58 mg·h/L compared with that of 47.7 mg·h/L for study 1 and 47.6 mg·h/L for study 2. Similar differences were also observed in the percentage of target attainment, defined as the ratio of area under the concentration-time curve at 24 h and the minimum inhibitory concentration. At the epidemiological cut-off minimum inhibitory concentration of Pseudomonas aeruginosa of 0.5 mg/L, percentage of target attainment was only 21%, 18%, and 38% for study 1, 2, and 3, respectively.Conclusions: We developed a population PK model of ciprofloxacin in ICU patients using pooled data of individual patients from three studies. A simple ciprofloxacin dose recommendation for the entire ICU population remains challenging owing to the PK differences within ICU patients, hence dose individualization may be needed for the optimization of ciprofloxacin treatment.Pharmacolog

Focus on focus: Lack of coherence between systemic and microvascular indices of oedema formation

Background: Fluid therapy remains a cornerstone of therapy in shock states. However, fluid overloading ultimately results in oedema formation which is related to excess morbidity and mortality. Handheld microscopes are now frequently used to study the sublingual microcirculation. As a corollary, these devices measure focal distance, or surface to capillary distance. Physiologically, this could represent a microvascular index of oedema formation and could have the potential to guide fluid therapy. This potential tool should be investigated, especially given the frequently reported lack of coherence between systemic and microvascular parameters in the critically ill. Therefore, we set out to assess the correlation between microvascular focal distance and systemic indices of oedema formation, specifically fluid balance and weight gain. Methods: Following ex vivo testing of focal distance measurement reliability, we conducted a prospective observational cohort study in patients admitted to the intensive care unit of our university teaching hospital. We determined surface to capillary distance using sidestream dark field (SDF) and incident dark field (IDF) imaging by assessing the focal distance point or object distance range at which a sharp recording could be made. Measurements were performed in post-cardiac surgery patients and in patients following emergency admission at two time points separated by at least several hours. Data on fluid balance, weight and weight gain were collected simultaneously. Results: Sixty patients were included. The focal setting, focus point for SDF and the object distance range for IDF did not differ significantly between time points. Focus was not correlated with difference in fluid balance or weight gain. Conclusions: There is a lack of coherence between surface to capillary distance as determined by SDF or IDF imaging and fluid balance or weight gain. Thus, focal distance as a microvascular index of oedema formation cannot currently be used as a proxy for systemic indices of oedema formation. However, given the lack of coherence, further research should determine whether focal distance may provide better guidance for fluid therapy than traditional markers of overzealous fluid administration

Why we should sample sparsely and aim for a higher target: lessons from model-based therapeutic drug monitoring of vancomycin in intensive care patients

Aims To explore the optimal data sampling scheme and the pharmacokinetic (PK) target exposure on which dose computation is based in the model-based therapeutic drug monitoring (TDM) practice of vancomycin in intensive care (ICU) patients. Methods We simulated concentration data for 1 day following four sampling schemes,C-min,C-max+C-min,C-max+Cmid-interval+C-min, and rich sampling where a sample was drawn every hour within a dose interval. The datasets were used for Bayesian estimation to obtain PK parameters, which were used to compute the doses for the next day based on five PK target exposures: AUC(24)= 400, 500, and 600 mg center dot h/L andC(min)= 15 and 20 mg/L. We then simulated data for the next day, adopting the computed doses, and repeated the above procedure for 7 days. Thereafter, we calculated the percentage error and the normalized root mean square error (NRMSE) of estimated against "true" PK parameters, and the percentage of optimal treatment (POT), defined as the percentage of patients who met 400 <= AUC(24)<= 600 mg center dot h/L andC(min)<= 20 mg/L. Results PK parameters were unbiasedly estimated in all investigated scenarios and the 6-day average NRMSE were 32.5%/38.5% (CL/V, whereCLis clearance andVis volume of distribution) in the trough sampling scheme and 27.3%/26.5% (CL/V) in the rich sampling scheme. Regarding POT, the sampling scheme had marginal influence, while target exposure showed clear impacts that the maximum POT of 71.5% was reached when doses were computed based on AUC(24)= 500 mg center dot h/L. Conclusions For model-based TDM of vancomycin in ICU patients, sampling more frequently than taking only trough samples adds no value and dosing based on AUC(24)= 500 mg center dot h/L lead to the best POT.Pharmacolog

Optimizing predictive performance of bayesian forecasting for vancomycin concentration in intensive care patients

This article was updated to correct Figs. 1 and 4 as author corrections were overlooked during the production process.Pharmacolog

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

Background The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. Methods A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. Results Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. Conclusions In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Data on sex differences in one-year outcomes of out-of-hospital cardiac arrest patients without ST-segment elevation

Sex differences in out-of-hospital cardiac arrest (OHCA) patients are increasingly recognized. Although it has been found that post-resuscitated women are less likely to have significant coronary artery disease (CAD) than men, data on follow-up in these patients are limited. Data for this data in brief article was obtained as a part of the randomized controlled Coronary Angiography after Cardiac Arrest without ST-segment elevation (COACT) trial. The data supplements the manuscript “Sex differences in out-of-hospital cardiac arrest patients without ST-segment elevation: A COACT trial substudy” were it was found that women were less likely to have significant CAD including chronic total occlusions, and had worse survival when CAD was present. The dataset presented in this paper describes sex differences on interventions, implantable-cardioverter defibrillator (ICD) shocks and hospitalizations due to heart failure during one-year follow-up in patients successfully resuscitated after OHCA. Data was derived through a telephone interview at one year with the patient or general practitioner. Patients in this randomized dataset reflects a homogenous study population, which can be valuable to further build on research regarding long-term sex differences and to further improve cardiac care

Comparison of outcome and characteristics between 6343 COVID-19 patients and 2256 other community-acquired viral pneumonia patients admitted to Dutch ICUs

Purpose: Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs. Materials and methods: Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared. Results: 6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58. Conclusion: Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/)