Population pharmacokinetic analysis of dexmedetomidine in children using real-world data from electronic health records and remnant specimens.

AIMS: Our objectives were to perform a population pharmacokinetic analysis of dexmedetomidine in children using remnant specimens and electronic health records (EHRs) and explore the impact of patient's characteristics and pharmacogenetics on dexmedetomidine clearance. METHODS: Dexmedetomidine dosing and patient data were gathered from EHRs and combined with opportunistically sampled remnant specimens. Population pharmacokinetic models were developed using nonlinear mixed-effects modelling. Stage 1 developed a model without genotype variables; Stage 2 added pharmacogenetic effects. RESULTS: Our final study population included 354 post-cardiac surgery patients aged 0-22 years (median 16 mo). The data were best described with a 2-compartment model with allometric scaling for weight and Hill maturation function for age. Population parameter estimates and 95% confidence intervals were 27.3 L/h (24.0-31.1 L/h) for total clearance, 161 L (139-187 L) for central compartment volume of distribution, 26.0 L/h (22.5-30.0 L/h) for intercompartmental clearance and 7903 L (5617-11 119 L) for peripheral compartment volume of distribution. The estimate for postmenstrual age when 50% of adult clearance is achieved was 42.0 weeks (41.5-42.5 weeks) and the Hill coefficient estimate was 7.04 (6.99-7.08). Genotype was not statistically or clinically significant. CONCLUSION: Our study demonstrates the use of real-world EHR data and remnant specimens to perform a population pharmacokinetic analysis and investigate covariate effects in a large paediatric population. Weight and age were important predictors of clearance. We did not find evidence for pharmacogenetic effects of UGT1A4 or UGT2B10 genotype or CYP2A6 risk score.

ICU sedation with dexmedetomidine after severe traumatic brain injury.

OBJECTIVE: To comprehensively describe the use of dexmedetomidine in a single institutional series of adult ICU patients with severe TBI. This study describes the dexmedetomidine dosage and infusion times, as well as the physiological parameters, neurological status and daily narcotic requirements before, during and after dexmedetomidine infusion. METHODS: This study identified 85 adult patients with severe TBI who received dexmedetomidine infusions in the Trauma ICU at Vanderbilt University Medical Center between 2006-2010. Demographic, haemodynamic, narcotic use and sedative use data were systematically obtained from the medical record and analysed for changes associated with dexmedetomidine infusion. RESULTS: During infusion with dexmedetomidine, narcotic and sedative use decreased significantly (p < 0.001 and p < 0.05). Median MAP, SBP, DBP and HR also decreased significantly during infusion when compared to pre-infusion values (p < 0.001). Despite the use of dexmedetomidine, RASS and GCS scores improved from pre-infusion to infusion time periods. CONCLUSIONS: The findings demonstrate that initiation of dexmedetomidine infusion is not associated with a decline in neurological functioning in adults with severe TBI. Although there was an observed decrease in haemodynamic parameters during infusion with dexmedetomidine, the change was not clinically significant and the requirements for narcotics and additional sedatives were minimized.

Pragmatic pharmacology: population pharmacokinetic analysis of fentanyl using remnant samples from children after cardiac surgery.

AIMS: One barrier contributing to the lack of pharmacokinetic (PK) data in paediatric populations is the need for serial sampling. Analysis of clinically obtained specimens and data may overcome this barrier. To add evidence for the feasibility of this approach, we sought to determine PK parameters for fentanyl in children after cardiac surgery using specimens and data generated in the course of clinical care, without collecting additional blood samples. METHODS: We measured fentanyl concentrations in plasma from leftover clinically-obtained specimens in 130 paediatric cardiac surgery patients and successfully generated a PK dataset using drug dosing data extracted from electronic medical records. Using a population PK approach, we estimated PK parameters for this population, assessed model goodness-of-fit and internal model validation, and performed subset data analyses. Through simulation studies, we compared predicted fentanyl concentrations using model-driven weight-adjusted per kg vs. fixed per kg fentanyl dosing. RESULTS: Fentanyl clearance for a 6.4 kg child, the median weight in our cohort, is 5.7 l h(-1) (2.2-9.2 l h(-1) ), similar to values found in prior formal PK studies. Model assessment and subset analyses indicated the model adequately fit the data. Of the covariates studied, only weight significantly impacted fentanyl kinetics, but substantial inter-individual variability remained. In simulation studies, model-driven weight-adjusted per kg fentanyl dosing led to more consistent therapeutic fentanyl concentrations than fixed per kg dosing. CONCLUSIONS: We show here that population PK modelling using sparse remnant samples and electronic medical records data provides a powerful tool for assessment of drug kinetics and generation of individualized dosing regimens.

Opioid use after cardiac surgery in children with Down syndrome.

OBJECTIVES: To determine the cumulative opioid doses administered to patients with Down syndrome after cardiac surgery and compare them with patients without Down syndrome. DESIGN: Retrospective observational comparative study. SETTING: PICU in a university-affiliated freestanding pediatric teaching hospital. PATIENTS: Infants and children who presented to our institution for heart surgery after July 1, 2008, and met the following criteria: 1) no opioid medications for 48 hours prior to surgery, 2) sternotomy approach with primary closure, and 3) no additional operative procedures in the 5 days after surgery. All patients with Down syndrome were included, and patients without Down syndrome with similar age, type of cardiac lesion, and length of surgical procedure were selected in a ~2:1 ratio, blinded to opioid exposure. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Clinical and demographic data were extracted from electronic medical record data. Univariate analyses and multivariate linear regression modeling were performed to determine the influence of Down syndrome, patient characteristics, and clinical covariates on weight-adjusted opioid dose. The differences in median cumulative opioid doses between those with Down syndrome (n = 44) and those without Down syndrome (n = 77) were not significant in the first 24 hours (+0.39 mg/kg [95% CI, -0.45 to +1.39 mg/kg]) or 96 hours (+0.54 mg/kg [95% CI, -0.59 to +2.07 mg/kg]) after surgery. Age, cardiac bypass time, benzodiazepines, and neuromuscular blocking agents were significantly correlated with opioid dose, but Down syndrome, gender, pain score, creatinine, acetaminophen, nonsteroidal anti-inflammatory drugs, and steroid medications were not. Patients with Down syndrome had longer hospital stays; in multivariate analysis, higher opioid exposures in the first 96 hours after surgery and higher peak serum creatinine values correlated with longer hospitalization. CONCLUSIONS: This cohort did not provide evidence for opioid resistance in patients with Down syndrome. Younger age, longer cardiac bypass time, exposure to benzodiazepines, and neuromuscular blockade did correlate with increased opioid doses after cardiac surgery.