Background And Aims: The critically ill patients with liver disease are vulnerable to infections in both community and hospital settings. The nosocomial infections are often caused by multidrug-resistant (MDR) bacteria. The present observational study was conducted to describe the epidemiology, course, and outcome of MDR bacterial infection and identify the risk factors of such infection in critically ill patients with liver disease.
Materials And Methods: A retrospective observational study was conducted on 106 consecutive critically patients with liver disease admitted in the Intensive Care Unit between March 2015 and February 2017. The MDR and non-MDR (non-MDR) groups were compared and the risk factors identified by multivariate analysis.
Results: Out of the 106 patients enrolled in the study, 23 patients had infections caused by MDR bacteria. The MDR-infected patients had severe liver disease (Child-Pugh score 11 ± 2.3 vs. 7 ± 3.9; = 0.04), longer duration of antibiotic usage (6 ± 2.7 days vs. 2 ± 1.5 days; = 0.04), greater use of total parenteral nutrition (TPN) (73.9% vs. 62.6%; = 0.04), and more concurrent antifungal administration (60.8% vs. 38.5%; = 0.04). The mortality was higher in MDR group (hazard ratio = 1.86; < 0.05). The independent predictors of MDR bacterial infection were Child-Pugh score >10, prior carbapenem use, antibiotic use for more than 10 days, TPN use, and concurrent antifungal administration.
Conclusion: The study demonstrated a high prevalence of MDR bacterial infection in critically ill patients with a higher mortality over non-MDR bacterial infection and also identified the independent predictors of such infections.
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http://dx.doi.org/10.4103/sja.SJA_749_17 | DOI Listing |
EClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
View Article and Find Full Text PDFJ Intensive Care Soc
January 2025
Department of Physiotherapy, Faculty of Medicine, Dentistry and Health Sciences, School of Health Sciences, The University of Melbourne, Melbourne, VIC, Australia.
Digital health refers to the field of using and developing technology to improve health outcomes. Digital health and digital health interventions (DHIs) within the area of intensive care and critical illness survivorship are rapidly evolving. Digital health interventions refer to technologies in clinical interventional format.
View Article and Find Full Text PDFIntroduction The pediatric intensive care unit (PICU) is a specialized area for treating critically ill infants and children. However, some of these children may experience poor outcomes, including death. However, it is necessary to predict the prognosis for critically ill patients as early as possible to commence triage as well as an early and effective intervention to prevent mortality.
View Article and Find Full Text PDFFront Cardiovasc Med
December 2024
Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitaion, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Background: Artificial intelligence (AI) has been widely adopted for the prediction of latent shock occurrence in critically ill patients in intensive care units (ICUs). However, the usefulness of an ICU-based model to predict latent shock risk in an emergency department (ED) setting remains unclear. This study aimed to develop an AI model to predict latent shock risk in patients admitted to EDs.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
December 2024
Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Introduction: The Sequential Organ Failure Assessment (SOFA) score is a widely utilized clinical tool for evaluating the severity of organ failure in critically ill patients and assessing their condition and prognosis in the intensive care unit (ICU). Research has demonstrated that higher SOFA scores are associated with poorer outcomes in these patients. However, the predictive value of the SOFA score for acute kidney injury (AKI), a common complication of diabetic ketoacidosis (DKA), remains uncertain.
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