Drug-drug interactions in neonatal intensive care: A prospective cohort study

Population pharmacokinetics of fluconazole for prevention or treatment of invasive candidiasis in Chinese young infants

The dosing of fluconazole for young infants remains empirical because of the limited pharmacokinetic (PK) data. We aimed to establish a population PK model and assess the systematic exposure-response of commonly used regimens of fluconazole in Chinese infants. We included infants with a postnatal age of less than 120 days and received intravenous fluconazole. Both scheduled and scavenged plasma samples were collected, and fluconzaole concentration was determined by a validated ultra-performance liquid chromatography-tandem mass spectrometry assay. Population PK analysis was conducted using Phoenix NLME, and then Monte Carlo simulation was conducted to predict the probability of target attainment (PTA) of empirically used regimens of both prophylactic and therapeutic purposes. Based on 304 plasma samples from 183 young infants, fluconazole concentration data was best described by a one-compartment model with first-order elimination. Gestational Age (GA), postnatal age (PNA), and body weight (BW) were included in the final model as CL = 0.02*(GA/214)2.77*(PNA/13)0.24*exp(nCL); V = 1.56*(BW/1435)0.90*exp(nV). Model validation revealed the final model had qualified stability and acceptable predictive properties. Monte Carlo simulation indicated that under the same minimum inhibitory concentration (MIC) value and administration regimen, PTA decreased with GA and PNA. The commonly used prophylactic regimens can meet the clinical need, while higher doses might be needed for treatment of invasive candidiasis. This population PK model of fluconazole discriminated the impact of GA and PNA on CL and BW on V. Dosing adjustment was needed according to the GA and PNA of infants to achieve targeted exposures.

An Assessment of Medication Errors Among Pediatric Patients in Three Hospitals in Freetown Sierra Leone: Findings and Implications for a Low-Income Country

Background

Pediatric patients are prone to medicine-related problems like medication errors (MEs), which can potentially cause harm. Yet, this has not been studied in this population in Sierra Leone. Therefore, this study investigated the prevalence and nature of MEs, including potential drug-drug interactions (pDDIs), in pediatric patients.

Methods

The study was conducted in three hospitals among pediatric patients in Freetown and consisted of two phases. Phase one was a cross-sectional retrospective review of prescriptions for completeness and accuracy based on the global accuracy score against standard prescription writing guidelines. Phase two was a point prevalence inpatient chart review of MEs categorized into prescription, administration, and dispensing errors and pDDIs. Data was analyzed using frequency, percentages, median, and interquartile range. Kruskal-Wallis H and Mann-Whitney U-tests were used to compare the prescription accuracy between the hospitals, with p<0.05 considered statistically significant.

Results

Three hundred and sixty-six (366) pediatric prescriptions and 132 inpatient charts were reviewed in phases one and two of the study, respectively. In phase one, while no prescription attained the global accuracy score (GAS) gold standard of 100%, 106 (29.0%) achieved the 80-100% mark. The patient 63 (17.2%), treatment 228 (62.3%), and prescriber 33 (9.0%) identifiers achieved an overall GAS range of 80-100%. Although the total GAS was not statistically significant (p=0.065), the date (p=0.041), patient (p=<0.001), treatment (p=0.022), and prescriber (p=<0.001) identifiers were statistically significant across the different hospitals. For phase two, the prevalence of MEs was 74 (56.1%), while that of pDDIs was 54 (40.9%). There was a statistically positive correlation between the occurrence of pDDI and number of medicines prescribed (r=0.211, P=0.015).

Conclusion

A Low GAS indicates poor compliance with prescription writing guidelines and high prescription errors. Medication errors were observed at each phase of the medication use cycle, while clinically significant pDDIs were also reported. Thus, there is a need for training on prescription writing guidelines and medication errors.

Impact of clinical pharmacist-led intervention for drug-related problems in neonatal intensive care unit a randomized controlled trial

Introduction: Drug-related problems (DRPs) incidence is higher in neonatal intensive care units (NICUs), compared to other pediatric wards due to aspects like off-label medications, pharmacokinetic/dynamic variability, or organ dysfunction/immaturity. This study aimed to determine whether and to what extent a clinical pharmacist intervention improves medication safety and prevents DRPs [medication errors (MEs), adverse drug reactions (ADRs), drug-drug interactions (DDIs)]. Methods: A prospective, randomized, double blind, controlled study in NICU-admitted neonates was conducted. NICU patients were randomly assigned to the intervention (clinical pharmacist-led) (IG) or control group (standard care such as clinical diagnosis, pharmacotherapy) (CG). The clinical pharmacist was involved in the IG to identify-prevent-intervene MEs, or identify and monitor ADRs and DDIs. The primary outcome was the number of neonates who developed at least one DRP compared with those seen across IG and CG. Secondary outcomes included length of hospital stay, total number of drugs or DRP type. Results: Neonates were randomly assigned to CG (n = 52) or IG (n = 48). In total, 45%, 42%, and 16% of patients had at least 1 MEs, ADRs, and clinically significant DDIs, respectively. The number of patients with at least 1 ME was 28 (53%) and 17 (35%) in the CG and IG (p>0.05). The median (range) number of ME was higher in CG [1 (0-7)] than in IG [0 (0-4)] (p = 0.003). Applying regression analysis, the CG had 2.849 times more MEs than the IG (p<0.001). Furthermore, the number of patients (CG to IG) with at least one detected ADR or clinical DDI was 19 (36%) to 23 (47%) (p>0.05) and 4 (7%) to 12 (25%), respectively (p = 0.028). Conclusion: Clinical pharmacist availability to systematically and standardized identify, prevent and resolve DRPs among NICU patients is effective. Daily detailed clinical pharmacist observations and interventions enables prevention and monitoring of DRPs. Clinical Trial Registration ClinicalTrials.gov, identifier NCT04899960.

Novel Method for Early Prediction of Clinically Significant Drug-Drug Interactions with a Machine Learning Algorithm Based on Risk Matrix Analysis in the NICU

Aims: Evidence for drug–drug interactions (DDIs) that may cause age-dependent differences in the incidence and severity of adverse drug reactions (ADRs) in newborns is sparse. We aimed to develop machine learning (ML) algorithms that predict DDI presence by integrating each DDI, which is objectively evaluated with the scales in a risk matrix (probability + severity). Methods: This double-center, prospective randomized cohort study included neonates admitted to the neonatal intensive care unit in a tertiary referral hospital during the 17-month study period. Drugs were classified by the Anatomical Therapeutic Chemical (ATC) classification and assessed for potential and clinically relevant DDIs to risk analyses with the Drug Interaction Probability Scale (DIPS, causal probability) and the Lexicomp^® DDI (severity) database. Results: A total of 412 neonates (median (interquartile range) gestational age of 37 (4) weeks) were included with 32,925 patient days, 131 different medications, and 11,908 medication orders. Overall, at least one potential DDI was observed in 125 (30.4%) of the patients (2.6 potential DDI/patient). A total of 38 of these 125 patients had clinically relevant DDIs causing adverse drug reactions (2.0 clinical DDI/patient). The vast majority of these DDIs (90.66%) were assessed to be at moderate risk. The performance of the ML algorithms that predicts of the presence of relevant DDI was as follows: accuracy 0.944 (95% CI 0.888–0.972), sensitivity 0.892 (95% CI 0.769–0.962), F1 score 0.904, and AUC 0.929 (95% CI 0.874–0.983). Conclusions: In clinical practice, it is expected that optimization in treatment can be achieved with the implementation of this high-performance web tool, created to predict DDIs before they occur with a newborn-centered approach.

Medication Use in the Neonatal Intensive Care Unit and Changes from 2010 to 2018

OBJECTIVE

To provide up-to-date medication prescribing patterns in US neonatal intensive care units (NICUs) and to examine trends in prescribing patterns over time.

STUDY DESIGN

We performed a cohort study of 799 016 infants treated in NICUs managed by the Pediatrix Medical Group from 2010 to 2018. We used 3 different methods to report counts of medication: exposure, courses, and days of use. We defined the change in frequency of medication administration by absolute change and relative change. We examined the Food and Drug Administration (FDA) package insert for each medication to determine whether a medication was labeled for use in infants and used PubMed to search for pharmacokinetics (PK) studies.

RESULTS

The most frequently prescribed medications included ampicillin, gentamicin, caffeine citrate, poractant alfa, morphine, vancomycin, furosemide, fentanyl, midazolam, and acetaminophen. Of the top 50 medications used in infants with extremely low birth weight, only 20 (40%) are FDA-labeled for use in infants; of the 30 that are not labeled for use in infants, 13 (43%) had at least 2 published PK studies. The medications with the greatest relative increase in use from 2010 to 2018 included dexmedetomidine, clonidine, rocuronium, levetiracetam, atropine, and diazoxide. The medications with the greatest relative decrease in use included tromethamine acetate, pancuronium, chloral hydrate, imipenem + cilastatin, and amikacin.

CONCLUSION

Trends of medication use in the NICU change substantially over time. It is imperative to identify changes in medication use in the NICU to better inform further prospective studies.

Dried matrix spots in forensic toxicology

Dried matrix spots (DMS) has gained the attention of different professionals in different fields, including toxicology. Investigations have been carried out in order to assess the potential of using DMS for the analysis of illicit substances, the main interest of forensic toxicologists. This technique uses minimal volumes of samples and solvents, resulting in simple and rapid extraction procedures. Furthermore, it has proved to increase analyte stability, improving storage and transportation. However, DMS presents some limitations: the hematocrit influencing accuracy and inconsistencies regarding the means of spotting samples and adding internal standard on paper. Thus, we provide an overview of analytical methodologies with forensic applications focusing on drugs of abuse and discussing the main particularities, limitations and achievements.