Background: Delirium is common in patients admitted to the surgical trauma intensive care unit (ICU), and the risk factors for these patients differ from medical patients. Given the morbidity and mortality associated with delirium, efforts to prevent it may improve patient outcomes, but previous efforts pharmacologically have been limited by side effects and insignificant results. We hypothesized that scheduled quetiapine could reduce the incidence of delirium in this population.
Methods: The study included 71 adult patients who were at high-risk for the development of delirium (PRE-DELIRIC Score ≥50%, history of dementia, alcohol misuse, or drug abuse). Patients were randomized to receive quetiapine 12.5 mg every 12 h for delirium or no pharmacologic prophylaxis within 48 h of admission to the ICU. The primary end point was the incidence of delirium during admission to the ICU. Secondary end points included time to onset of delirium, ICU and hospital length of stay (LOS), ICU and hospital mortality, duration of mechanical ventilation, and adverse events.
Results: The incidence of delirium during admission to the ICU was 45.5% (10/22) in the quetiapine group and 77.6% (38/49) in the group that did not receive pharmacological prophylaxis. The mean time to onset of delirium was 1.4 days for those who did not receive prophylaxis versus 2.5 days for those who did (p = 0.06). The quetiapine group significantly reduced ventilator duration from 8.2 days to 1.5 days (p = 0.002).
Conclusions: The findings suggested that scheduled, low-dose quetiapine is effective in preventing delirium in high-risk, surgical trauma ICU patients.
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http://dx.doi.org/10.1016/j.surge.2020.02.002 | DOI Listing |
Brain Behav
January 2025
Department of Anesthesiology, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, People's Republic of China.
Background: The occurrence and development of postoperative cognitive dysfunction (POCD) are closely linked to neuroinflammation. This bibliometric analysis aims to provide novel insights into the research trajectory, key research topics, and potential future development trends in the field of neuroinflammation-induced POCD.
Methods: The Web of Science Core Collection (WoSCC) database was searched to identify publications from 2012 to 2023 on neuroinflammation-induced POCD.
J Clin Nurs
January 2025
Department of Neurosurgery, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
Sci Rep
January 2025
Department of Anesthesia, College of Health Sciences, Debre Tabor University, PO. Box: 272, Debre Tabor, Ethiopia.
Postoperative delirium has the potential to impact individuals of all age groups, with a significant emphasis on the elderly population. Its presence leads to an increase in surgical morbidity and mortality rates, as well as a notable prolongation of hospital stays. However, there is a lack of research regarding the prevalence, risk factors, and implications of postoperative delirium in developing nations like Ethiopia, which affects both patients and healthcare institutions.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Diagnostic and Health Sciences, College of Health Professions, University of Tennessee Health Science Center, Memphis, TN, United States of America.
For patients hospitalized with COVID-19, delirium is a serious and under-recognized complication, and people experiencing homelessness (PEH) may be at greater risk. This retrospective cohort study compared delirium-associated risk factors and clinical outcomes between PEH and non-PEH. This study used patient records from 154 hospitals discharged from 2020-2021 from the Texas Inpatient Public Use Data file.
View Article and Find Full Text PDFJMIR Perioper Med
January 2025
Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States.
Background: Postoperative delirium (POD) is a common complication after major surgery and is associated with poor outcomes in older adults. Early identification of patients at high risk of POD can enable targeted prevention efforts. However, existing POD prediction models require inpatient data collected during the hospital stay, which delays predictions and limits scalability.
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