Background: The area under the concentration-time curve over 24 hours (AUC ) is frequently utilized to monitor tobramycin exposure in children with cystic fibrosis (CF). An understanding of exposure target achievement during clinical implementation of an AUC based approach in children is limited.
Methods: A retrospective chart review was performed in children with CF treated with once daily tobramycin and drug concentration monitoring at a pediatric CF center. During clinical care AUC was estimated using a traditional log-linear regression approach (LLR). AUC was also estimated retrospectively using a pharmacokinetic model-based Bayesian forecasting approach (BF). AUC achievement after both approaches were compared.
Results: In 77 treatment courses (mean age, 12.7 ± 5.0 years), a target AUC 100 to 125 mg h/L was achieved after starting dose in 21 (27%) and after initial dose adjustment in 35 (45%). In the first 7 days of treatment, 24 (32%) required ≥3 dose adjustments, and the mean number of drug concentrations measured was 7.1 ± 3.2. Examination of a BF approach demonstrated adequate prediction of measured tobramycin concentrations (median bias -2.1% [95% CI -3.1 to -1.4]; median precision 7.6% [95% CI, 7.1%-8.2%]). AUC estimates utilizing the BF approach were higher than the LLR approach with a mean difference of 6.4 mg h/L (95% CI, 4.8 to 8.0 mg h/L).
Conclusions: Achievement of a narrow AUC target is challenging during clinical care, and dose individualization is needed in most children with CF. Implementing a BF approach for estimating AUC in children with CF is supported.
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http://dx.doi.org/10.1002/ppul.25037 | DOI Listing |
Palliat Support Care
January 2025
Department of Obstetrics and Gynecology, Inova Fairfax Hospital, Falls Church, VA, USA.
Objectives: To incorporate a longitudinal palliative care curriculum into obstetrics and gynecology (Ob-Gyn) residency that could become standardized to ensure competencies in providing end of life (EOL) care.
Methods: This was a prospective cohort study conducted among 23 Ob-Gyn residents at a tertiary training hospital from 2021 to 2022. A curriculum intervention was provided via lecture and simulation.
JMIR Public Health Surveill
January 2025
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardio-Thoraco-Vascular Sciences and Public Health, University of Padova, Via Loredan 18, Padova, Italy, 39 049 8275384.
Background: As the COVID-19 pandemic has affected populations around the world, there has been substantial interest in wastewater-based epidemiology (WBE) as a tool to monitor the spread of SARS-CoV-2. This study investigates the use of WBE to anticipate COVID-19 trends by analyzing the correlation between viral RNA concentrations in wastewater and reported COVID-19 cases in the Veneto region of Italy.
Objective: We aimed to evaluate the effectiveness of the cumulative sum (CUSUM) control chart method in detecting changes in SARS-CoV-2 concentrations in wastewater and its potential as an early warning system for COVID-19 outbreaks.
J Atten Disord
January 2025
Johns Hopkins Aramco Healthcare, Clinical Psychology and Counseling Services Unit, Saudi Arabia.
Objective: This study investigated the psychometric properties of the Arabic version of the Adult Self-Report Scale-5 (the ASRS-5-AR) within a large sample of adults residing in Saudi Arabia.
Methods: This cross-sectional study applied the ASRS-5-AR to a random sample of 4,299 Saudi and non-Saudi adults, aged 19 to 66 years (31.16 ± 9.
Hypertension
January 2025
Department of Nephrology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany (S.A.P., I.Q., D. Arifaj, M.K., D. Argov, L.C.R., J.S.).
Background: Ciliary neurotrophic factor (CNTF), mainly known for its neuroprotective properties, belongs to the IL-6 (interleukin-6) cytokine family. In contrast to IL-6, the effects of CNTF on the vasculature have not been explored. Here, we examined the role of CNTF in AngII (angiotensin II)-induced hypertension.
View Article and Find Full Text PDFJMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
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