Search Results (5)

The glomerular filtration rate (GFR), according to which the drug dose for patients with chronic kidney disease (CKD) is adjusted, is computed with estimators (eGFR) that are developed specifically for CKD. These particular types of estimators are also used in population pharmacokinetic (pop PK) modelling in drug development. Similar approaches without scientific validation have been proposed for patients with acute kidney injury (AKI), yet it is uncertain which specific eGFR should be used for drug dosing or in pop PK models in patients with AKI. In our study, we included 34 patients with AKI and vancomycin (VCM) treatment, and we built both individual PK and pop PK (non-linear mixed-effects, one-compartment) models to see which eGFR estimator is the best covariate. In these models different eGFRs (Cockcroft-Gault, MDRD, CKD-EPI 2009, Jelliffe and Jelliffe, Chen et al., and Yashiro et al. 2013) were used. We included six additional patients to validate the final pop PK model. All eGFRs underrate the true renal clearance in the AKI, so we created pop PK models for VCM dosing in AKI with all eGFRs, to discover that the most accurate model was the one with the Cockcroft-Gault estimator. Since the eGFRs underestimate the true renal clearance in AKI, they are inaccurate for clinical drug dosing decisions, with the exception of the Cockcroft-Gault one, which is appropriate for the pop PK models intended for drug development purposes in AKI. Full article

Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most N points where N is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing. Full article

The 2019 novel coronavirus disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread and worsen in many parts of the world. As the pandemic grows, it is especially important to understand how the virus and the pandemic are affecting pregnant women and infants. While early data suggested that being infected with the virus did not increase the risk of adverse pregnancy or infant outcomes, as more information has emerged, it has become clear that risks for some adverse pregnancy and infant outcomes are increased (e.g., preterm birth, cesarean section, respiratory distress, and hospitalization). The Healthy Outcomes of Pregnancy for Everyone in the time of novel coronavirus disease-19 (HOPE COVID-19) study is a multi-year, prospective investigation designed to better understand how the SARS-CoV-2 virus and COVID-19 impact adverse pregnancy and infant outcomes. The study also examines how the pandemic exacerbates existing hardships such as social isolation, economic destabilization, job loss, housing instability, and/or family member sickness or death among minoritized and marginalized communities. Specifically, the study examines how pandemic-related hardships impact clinical outcomes and characterizes the experiences of Black, Latinx and low-income groups compared to those in other race/ethnicity and socioeconomic stratum. The study includes two nested cohorts. The survey only cohort will enroll 7500 women over a two-year period. The survey+testing cohort will enroll 2500 women over this same time period. Participants in both cohorts complete short surveys daily using a mobile phone application about COVID-19-related symptoms (e.g., fever and cough) and complete longer surveys once during each trimester and at 6–8 weeks and 6, 12 and 18 months after delivery that focus on the health and well-being of mothers and, after birth, of infants. Participants in the survey+testing cohort also have testing for SARS-CoV-2 and related antibodies during pregnancy and after birth as well as testing that looks at inflammation and for the presence of other infections like Influenza and Rhinovirus. Study results are expected to be reported on a rolling basis and will include quarterly reporting for participants and public health partners as well as more traditional scientific reporting. Full article

Acute kidney injury (AKI) can potentially lead to the accumulation of antimicrobial drugs with significant renal clearance. Drug dosing adjustments are commonly made using the Cockcroft-Gault estimate of creatinine clearance (CLcr). The Modified Jelliffe equation is significantly better at estimating kidney function than the Cockcroft-Gault equation in the setting of AKI. The objective of this study is to assess the degree of antimicrobial dosing discordance using different glomerular filtration rate (GFR) estimating equations. This is a retrospective evaluation of antimicrobial dosing using different estimating equations for kidney function in AKI and comparison to Cockcroft-Gault estimation as a reference. Considering the Cockcroft-Gault estimate as the criterion standard, antimicrobials were appropriately adjusted at most 80.7% of the time. On average, kidney function changed by 30 mL/min over the course of an AKI episode. The median clearance at the peak serum creatinine was 27.4 (9.3–66.3) mL/min for Cockcroft Gault, 19.8 (9.8–47.0) mL/min/1.73 m² for MDRD and 20.5 (4.9–49.6) mL/min for the Modified Jelliffe equations. The discordance rate for antimicrobial dosing ranged from a minimum of 8.6% to a maximum of 16.4%. In the event of discordance, the dose administered was supra-therapeutic 100% of the time using the Modified Jelliffe equation. Use of estimating equations other than the Cockcroft Gault equation may significantly alter dosing of antimicrobials in AKI. Full article

This study examines the adoption of high-yielding varieties (HYVs) of groundnut by smallholders in eastern Uganda. The primary focus of this work is the analysis of the sustainability of impacts from a regional farmer-led HYV dissemination and multiplication program. Data collected in 2014 is used to determine the lasting impact of the program conducted a decade prior, from 2001 to 2004. The structure of the data, which includes a treatment and 2-part control group, is critical to the identification of project impacts, measured as groundnut land allocation to groundnut HYVs (%). Fractional regression, propensity score matching and instrumental variable techniques are utilized to address potential bias from model specification, selection, and endogeneity. We find that, on average, participating households allocated 21% more of their land in groundnuts to HYVs relative to controls. Diffusion of program benefits through spillover is revealed by statistically significant differences in mean adoption between neighbor and non-neighbor controls, such that benefits are transferred from participants to their neighbors but not to the non-neighbor control group. We also find that, because of seed saving practices, the average yield for HYVs decreased over time to levels below those obtained from landrace varieties. Thus, the program effectively aided in information dissemination and technology transfer within rural communities. However, additional knowledge transfer is critical to the sustainability of food security outcomes among participant farmers. Full article