Understanding the Role of Pharmacometrics-Based Clinical Decision Support Systems in Pediatric Patient Management: A Case Study Using Lyv Software

Pharmacokinetic model-guided enoxaparin dosing in the Neonatal ICU: Retrospective cohort study to plan for prospective feasibility trial

Traditional milligram per kilogram (mg/kg) dosing of enoxaparin in neonates frequently fails to achieve target anti-Xa levels promptly, necessitating repeated laboratory monitoring and dose adjustments. This study investigated whether a personalized dosing strategy based on predicted individual clearance and volume of distribution could improve outcomes, comparing standard-of-care (SOC) mg/kg dosing to pharmacokinetic (PK) model-informed precision dosing (MIPD). A retrospective analysis was conducted on hospitalized neonates treated with enoxaparin at less than 44 weeks postmenstrual age from 2019 to 2022. Data on demographics, drug dosing, PK model covariates, and clinical outcomes were extracted from electronic health records and analyzed using the Pumas-AI Lyv dosing tool. The primary focus was on comparing the initial SOC dose to the MIPD-recommended dose. The secondary outcome measured was the time required to achieve therapeutic anti-Xa levels. The study included 168 neonates with a median postnatal age of 15 days (range 1-149) and a median dosing weight of 3.1 kg (range: 0.82-5.2). MIPD-recommended initial doses were 20%-60% higher than SOC doses in 32% of the cases and over 60% higher in 11% of cases. Neonates who received SOC doses that were much lower than the MIPD recommendation showed the longest delays in reaching therapeutic anti-Xa levels. The results indicate that PK model-informed of enoxaparin dosing leads to higher initial dosages than SOC in neonates, potentially reducing the time to therapeutic anti-Xa levels. These findings are being utilized to define dosing limits for a prospective trial of MIPD in neonatal intensive care settings.

Expanding access to veterinary clinical decision support in resource-limited settings: a scoping review of clinical decision support tools in medicine and antimicrobial stewardship

Introduction

Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided.

Methods

The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature.

Results

A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist.

Discussion

We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.

Bayesian model-guided antimicrobial therapy in pediatrics

Antimicrobials have transformed the practice of medicine, making life-threatening infections treatable, but determining optimal dosing, particularly in pediatric patients, remains a challenge. The lack of pediatric data can largely be traced back to pharmaceutical companies, which, until recently, were not required to perform clinical testing in pediatrics. As a result, most antimicrobial use in pediatrics is off-label. In recent years, a concerted effort (e.g., Pediatric Research Equality Act) has been made to fill these knowledge gaps, but progress is slow and better strategies are needed. Model-based techniques have been used by pharmaceutical companies and regulatory agencies for decades to derive rational individualized dosing guidelines. Historically, these techniques have been unavailable in a clinical setting, but the advent of Bayesian-model-driven, integrated clinical decision support platforms has made model-informed precision dosing more accessible. Unfortunately, the rollout of these systems remains slow despite their increasingly well documented contributions to patient-centered care. The primary goals of this work are to 1) provide a succinct, easy-to-follow description of the challenges associated with designing and implementing dose-optimization strategies; and 2) provide supporting evidence that Bayesian-model informed precision dosing can meet those challenges. There are numerous stakeholders in a hospital setting, and our intention is for this work to serve as a starting point for clinicians who recognize that these techniques are the future of modern pharmacotherapy and wish to become champions of that movement.

Population Pharmacokinetics of Vancomycin in Pregnant Women

Objective: Vancomycin is a glycopeptide antibacterial indicated for serious gram-positive infections. Pharmacokinetics (PK) of vancomycin have not been described in pregnant women. This study aims to characterize the PK disposition of vancomycin in pregnant women based on data acquired from a database of routine hospital care for therapeutic drug monitoring to better inform dosing decisions.

Methods: In this study, plasma drug concentration data from 34 pregnant hospitalized women who were administered intravenous vancomycin was analyzed. A population pharmacokinetic (PPK) model was developed using non-linear mixed effects modeling. Model selection was based on statistical criterion, graphical analysis, and physiologic relevance. Using the final model AUC_0-24 (PK efficacy index of vancomycin) was compared with non-pregnant population.

Results: Vancomycin PK in pregnant women were best described by a two-compartment model with first-order elimination and the following parameters: clearance (inter individual variability) of 7.64 L/hr (32%), central volume of 67.35 L, inter-compartmental clearance of 9.06 L/h, and peripheral volume of 37.5 L in a typical patient with 175 ml/min creatinine clearance (CRCL) and 45 kg fat-free mass (FFM). The calculated geometric mean of AUC_0-24 for the pregnant population was 223 ug.h/ ml and 226 ug.h/ ml for the non-pregnant population.

Conclusion: Our analysis suggests that vancomycin PK in pregnant women is consistent with non-pregnant adults and the dosing regimens used for non-pregnant patients may also be applicable to pregnant patients.