Discrepancies in Electronic Medical Prescriptions Found in a Hospital Emergency Department: A Prospective Observational Study.

The medication in an electronic prescribing system (EPS) does not always match the patient's actual medication. This prospective study analyzes the discrepancies (any inconsistency) between medication prescribed using an EPS and the medication revised by the clinical pharmacist upon admission to the observation area of the emergency department (ED). Adult patients with multimorbidity and/or polypharmacy were included. The pharmacist used multiple sources to obtain the revised medication list, including patient/carer interviews. A total of 1654 discrepancies were identified among 1131 patients. Of these patients, 64.5% had ≥1 discrepancy. The most common types of discrepancy were differences in posology (43.6%), commission (34.7%), and omission (20.9%). Analgesics (11.1%), psycholeptics (10.0%), and diuretics (8.9%) were the most affected. Furthermore, 52.5% of discrepancies affected medication that was high-alert for patients with chronic illnesses and 42.0% of medication involved withdrawal syndromes. Discrepancies increased with the number of drugs (ρ = 0.44, p < 0.01) and there was a difference between non-polypharmacy patients, polypharmacy ones and those with extreme polypharmacy (p < 0.01). Those aged over 75 years had a higher number of prescribed medications and discrepancies occurred more frequently compared with younger patients. The number of discrepancies was larger in women than in men. The EPS medication record requires verification from additional sources, including patient and/or carer interviews.

Population Pharmacokinetics of Valproic Acid in Pediatric and Adult Caucasian Patients.

(1) Background: The aim of this study was to explore the valproic acid (VPA) pharmacokinetic characteristics in a large population of pediatric and adult Caucasian patients and to establish a robust population pharmacokinetic (PopPK) model. (2) Methods: A total of 2527 serum VPA samples collected from 1204 patients included in a therapeutic drug monitoring program were retrospectively analyzed. Patients were randomly assigned to either a model development group or an external evaluation group. PopPK analysis was performed on 1751 samples from 776 patients with NONMEM using a nonlinear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment and comedication variables on the apparent clearance (CL/F) of VPA was studied. The bootstrap method was used to evaluate the final model internally. External evaluation was carried out using 776 VPA serum samples from 368 patients. (3) Results: A one-compartment model with first-order absorption and elimination successfully described the data. The final model included total body weight, age and comedication with phenytoin, phenobarbital and carbamazepine with a significant impact on VPA elimination. Internal and external evaluations demonstrated the good predictability of the model. (4) Conclusions: A PopPK model of VPA in Caucasian patients was successfully established, which will be helpful for model-informed precision dosing approaches in clinical patient care.

Population pharmacokinetics of phenobarbital in Caucasian patients with epilepsy.

The present study aimed to establish a population pharmacokinetic (PopPK) model of Phenobarbital (PB) in Caucasian patients with epilepsy included in a Therapeutic Drug Monitoring program. In total, 855 PB serum concentrations (steady-state trough concentrations) were retrospectively collected during routine clinical monitoring of 395 patients over 15 years of age with epilepsy. The PopPK analysis was performed with NONMEM using a non-linear mixed-effect modelling approach. The influence of demographic, anthropometric, treatment, and comedication variables on the apparent clearance (CL/F) of PB were analysed. Goodness of fit plots and the bootstrap method were performed to evaluate the final model. External validation was carried out using an independent group of patients (107 patients, 178 blood samples). A one-compartment model with first-order absorption and elimination successfully described the data. In the final model, CL/F included body surface area (BSA) and comedication with phenytoin (PHT) and valproic acid (VPA), resulting in the following equation: CL/F[L/h]=(0.236+(0.115×(BSA-1.7)))×(0.822PHT)×(0.711VPA) The model presents acceptable estimation errors in the parameters of fixed (<12%) and random effects (<13%), and of the shrinkage values (<21%). Internal and external validations demonstrated the good predictability of the final model. A PopPK model of PB in Caucasian patients over 15 years of age was successfully established, which can be used to estimate phenobarbital CL/F. BSA and drug-drug interactions with PHT and VPA should be incorporated into dosing decisions. This PopPK, using Bayesian algorithms, could help establish an optimal dosage regimen in routine patient care.