Dataset Information

Limited-sampling strategies for anidulafungin in critically ill patients.

ABSTRACT: Efficacy of anidulafungin is driven by the area under the concentration-time curve (AUC)/MIC ratio. Determination of the anidulafungin AUC along with MIC values can therefore be useful. Since obtaining a full concentration-time curve to determine an AUC is not always feasible or appropriate, limited-sampling strategies may be useful in adequately estimating exposure. The objective of this study was to develop a model to predict the individual anidulafungin exposure in critically ill patients using limited-sampling strategies. Pharmacokinetic data were derived from 20 critically ill patients with invasive candidiasis treated with anidulafungin. These data were used to develop a two-compartment model in MW\Pharm using an iterative 2-stage Bayesian procedure. Limited-sampling strategies were subsequently investigated using two methods, a Bayesian analysis and a linear regression analysis. The best possible strategies for these two methods were evaluated by a Bland-Altman analysis for correlation of the predicted and observed AUC from 0 to 24 h (AUC0-24) values. Anidulafungin exposure can be adequately estimated with the concentration from a single sample drawn 12 h after the start of the infusion either by linear regression (R2=0.99; bias, 0.05%; root mean square error [RMSE], 3%) or using a population pharmacokinetic model (R2=0.89; bias, -0.1%; RMSE, 9%) in critically ill patients and also in less severely ill patients, as reflected by healthy volunteers. Limited sampling can be advantageous for future studies evaluating the pharmacokinetics and pharmacodynamics of anidulafungin and for therapeutic drug monitoring in selected patients. (This study has been registered at ClinicalTrials.gov under registration no. NCT01047267.).

SUBMITTER: van Wanrooy MJ

PROVIDER: S-EPMC4335880 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Limited-sampling strategies for anidulafungin in critically ill patients.

van Wanrooy Marjolijn J P MJ Proost Johannes H JH Rodgers Michael G G MG Zijlstra Jan G JG Uges Donald R A DR Kosterink Jos G W JG van der Werf Tjip S TS Alffenaar Jan-Willem C JW

Antimicrobial agents and chemotherapy 20141208 2

Efficacy of anidulafungin is driven by the area under the concentration-time curve (AUC)/MIC ratio. Determination of the anidulafungin AUC along with MIC values can therefore be useful. Since obtaining a full concentration-time curve to determine an AUC is not always feasible or appropriate, limited-sampling strategies may be useful in adequately estimating exposure. The objective of this study was to develop a model to predict the individual anidulafungin exposure in critically ill patients usi ...[more]

PMID: 25487797

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Project description:Echinocandins, such as anidulafungin, are the first-line treatment for candidemia or invasive candidiasis in critically ill patients. There are conflicting data on the pharmacokinetic properties of anidulafungin in intensive care unit (ICU) patients. Adult ICU patients (from 3 hospitals) receiving anidulafungin for suspected or proven fungal infections were included in the present study. Patients were considered evaluable if a pharmacokinetic curve for day 3 could be completed. Twenty-three of 36 patients (7 female and 16 male) were evaluable. The median (range) age and body weight were 66 (28 to 88) years and 76 (50 to 115) kg, respectively. Pharmacokinetic sampling on day 3 (n = 23) resulted in a median anidulafungin area under the concentration-time curve from 0 to 24 h (AUC0-24) of 72.1 (interquartile range [IQR], 61.3 to 94.0) mg · h · liter-1, a median daily trough concentration (C24) of 2.2 (IQR, 1.9 to 2.9) mg/liter, a median maximum concentration of drug in serum (Cmax) of 5.3 (IQR, 4.1 to 6.0) mg/liter, a median volume of distribution (V) of 46.0 (IQR, 32.2 to 60.2) liters, and a median clearance (CL) of 1.4 (IQR, 1.1 to 1.6) liters · h-1 Pharmacokinetic sampling on day 7 (n = 13) resulted in a median AUC0-24 of 82.7 (IQR, 73.0 to 129.5) mg · h · liter-1, a median minimum concentration of drug in serum (Cmin) of 2.8 (IQR, 2.2 to 4.2) mg/liter, a median Cmax of 5.9 (IQR, 4.6 to 8.0) mg/liter, a median V of 39.7 (IQR, 32.2 to 54.4) liters, and a median CL of 1.2 (IQR, 0.8 to 1.4) liters · h-1 The geometric mean ratio for the AUCday7/AUCday3 term was 1.13 (90% confidence interval [CI], 1.03 to 1.25). The exposure in the ICU patient population was in accordance with previous reports on anidulafungin pharmacokinetics in ICU patients but was lower than that for healthy volunteers or other patient populations. Larger cohorts of patients or pooled data analyses are necessary to retrieve relevant covariates. (This study has been registered at ClinicalTrials.gov under identifier NCT01438216.).

Project description:Objective: COVID19 is caused by the SARS-CoV-2 virus and has been associated with severe inflammation leading to organ dysfunction and mortality. Our aim was to profile the transcriptome in leukocytes from critically ill ICU patients positive for COVID19 vs. those negative for COVID19 to better understand the COVID19 associated host response. Design: Transcriptome profiling of buffy coat cells via ribonucleic acid sequencing (RNAseq) at the time of admission to the ICU. Setting: Tertiary care ICU and academic laboratory. Subjects: All patients admitted to the ICU suspected of being infected with SARS-CoV-2, using standardized hospital screening methodologies, had blood samples collected at the time of admission to the ICU. Interventions: None. Measurement and Main Results: Age- and sex-matched ICU patients that were either COVID19+ (PCR positive, 2 genes) or COVID19- (PCR negative) were enrolled. Cohorts were well-balanced with the exception that COVID19- patients had significantly higher total white blood cell counts and circulating neutrophils and COVID19+ patients were more likely to suffer bilateral pneumonia compared to COVID19- patients. Further, the mortality rate for this cohort of COVID19+ ICU patients was 29%. Transcriptional analysis revealed that when compared to COVID19- patients, the altered transcriptional responses of leukocytes in critically ill COVID19+ ICU patients appeared to be associated with multiple interrelated outcomes, including but not limited to robust interferon (IFN)-associated transcriptional responses, a marked decrease in the transcriptional activity of genes contributing to protein synthesis and the dysregulated expression of genes that contribute to coagulation, platelet activation, Toll-like receptor activation, neurotrophin signaling, and protein SUMOylation/ubiquitination. Conclusions: COVID19+ patients on day 1 of admission to the ICU display a unique leukocyte transcriptional profile that distinguishes them from COVID19- patients. Identification of this profile provides guidance for future targeted studies exploring novel prognostic/therapeutic aspects of COVID19.

Project description:BackgroundCurrent critical care prognostic models are predominantly developed in high-income countries (HICs) and may not be feasible in intensive care units (ICUs) in lower- and middle-income countries (LMICs). Existing prognostic models cannot be applied without validation in LMICs as the different disease profiles, resource availability, and heterogeneity of the population may limit the transferability of such scores. A major shortcoming in using such models in LMICs is the unavailability of required measurements. This study proposes a simplified critical care prognostic model for use at the time of ICU admission.MethodsThis was a prospective study of 3855 patients admitted to 21 ICUs from Bangladesh, India, Nepal, and Sri Lanka who were aged 16 years and over and followed to ICU discharge. Variables captured included patient age, admission characteristics, clinical assessments, laboratory investigations, and treatment measures. Multivariate logistic regression was used to develop three models for ICU mortality prediction: model 1 with clinical, laboratory, and treatment variables; model 2 with clinical and laboratory variables; and model 3, a purely clinical model. Internal validation based on bootstrapping (1000 samples) was used to calculate discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer-Lemeshow C-Statistic; higher values indicate poorer calibration). Comparison was made with the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II models.ResultsModel 1 recorded the respiratory rate, systolic blood pressure, Glasgow Coma Scale (GCS), blood urea, haemoglobin, mechanical ventilation, and vasopressor use on ICU admission. Model 2, named TropICS (Tropical Intensive Care Score), included emergency surgery, respiratory rate, systolic blood pressure, GCS, blood urea, and haemoglobin. Model 3 included respiratory rate, emergency surgery, and GCS. AUC was 0.818 (95% confidence interval (CI) 0.800-0.835) for model 1, 0.767 (0.741-0.792) for TropICS, and 0.725 (0.688-0.762) for model 3. The Hosmer-Lemeshow C-Statistic p values were less than 0.05 for models 1 and 3 and 0.18 for TropICS. In comparison, when APACHE II and SAPS II were applied to the same dataset, AUC was 0.707 (0.688-0.726) and 0.714 (0.695-0.732) and the C-Statistic was 124.84 (p < 0.001) and 1692.14 (p < 0.001), respectively.ConclusionThis paper proposes TropICS as the first multinational critical care prognostic model developed in a non-HIC setting.

Dataset Information

Limited-sampling strategies for anidulafungin in critically ill patients.

Publications

Limited-sampling strategies for anidulafungin in critically ill patients.

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