Intra-abdominal candidiasis (IAC) is one of the most common of invasive candidiasis observed in critically ill patients. It is associated with high mortality, with up to 50% of deaths attributable to delays in source control and/or the introduction of antifungal therapy. Currently, there is no comprehensive guidance on optimising antifungal dosing in the treatment of IAC among the critically ill. However, this form of abdominal sepsis presents specific pharmacokinetic (PK) alterations and pharmacodynamic (PD) challenges that risk suboptimal antifungal exposure at the site of infection in critically ill patients. This review aims to describe the peculiarities of IAC from both PK and PD perspectives, advocating an individualized approach to antifungal dosing. Additionally, all current PK/PD studies relating to IAC are reviewed in terms of strength and limitations, so that core elements for the basis of future research can be provided.
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http://dx.doi.org/10.1186/s13054-023-04742-w | DOI Listing |
Adv Neonatal Care
January 2025
Author Affiliations: Neonatal Intensive Care Unit, Seattle Children's Hospital, Seattle, WA (Mrs LaBella, Ms Kelly, Mrs Carlin, and Dr Walsh); and Seattle Children's Research Institute, Seattle, WA (Mrs Carlin and Dr Walsh).
Background: Finding an accurate and simple method of thermometry in the neonatal intensive care unit is important. The temporal artery thermometer (TAT) has been recommended for all ages by the manufacturer; however, there is insufficient evidence for the use of TAT in infants, especially to detect hypothermia.
Purpose: To assess the accuracy of the TAT in hypothermic neonates in comparison to a rectal thermometer.
Shock
January 2025
Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, FL, 32611, USA.
Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to ICUs of Mayo Clinic Hospitals over eight-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Cardiology, Yonsei University College of Medicine, Seoul, Republic of Korea.
Background: Efficient emergency patient transport systems, which are crucial for delivering timely medical care to individuals in critical situations, face certain challenges. To address this, CONNECT-AI (CONnected Network for EMS Comprehensive Technical-Support using Artificial Intelligence), a novel digital platform, was introduced. This artificial intelligence (AI)-based network provides comprehensive technical support for the real-time sharing of medical information at the prehospital stage.
View Article and Find Full Text PDFAnn Med
December 2025
Department of Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Background: This study was aimed to explore the global burden and trends of Clostridioides difficile infections (CDI) associated diseases.
Methods: Data for this study were obtained from the Global Burden of Disease Study 2021. The burden of CDI was assessed using the age-standardized rates of disability-adjusted life years (ASR-DALYs) and deaths (ASDRs).
Indian J Pediatr
January 2025
Department of Nephrology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, National Children's Regional Medical Center, Hangzhou, China.
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