Objectives: Aortic dissection (AD) is a life-threatening condition that requires intensive care and management. This paper explores the role of fluid management in the clinical care of AD patients, which has been unclear despite the substantial existing research that has been conducted on the treatment of AD.
Design: A retrospective case-control study using data for AD patients from public databases.
Setting: Two public intensive care unit (ICU) databases with hospital courses from the USA, Medical Information Mart for Intensive Care (MIMIC)-IV critical care dataset and the eICU Collaborative Research Database, with data from 2008 to 2019.
Participants: A total of 751 adult AD patients with detailed fluid management records from two databases were included.
Interventions: The mean 24-hour intake and output were calculated by dividing the total amount of intake and output by the number of days in the ICU, respectively. The mean 24-hour fluid balance was generated by subtracting the output from the intake.
Outcome Measures: The relationship between the mean 24-hour fluid management and all-cause in-hospital death was assessed through univariate and multivariable regression analyses.
Results: A positive correlation was found between mean 24-hour fluid intake and in-hospital mortality among AD patients (OR 1.029, 95% CI (1.018, 1.041), p<0.001), whereas a negative correlation was revealed between mean 24-hour fluid output and in-hospital mortality (OR 0.941, 95% CI (0.914, 0.968), p<0.001). A similar result was found for mean 24-hour fluid balance (OR 1.030, 95% CI (1.019, 1.042), p<0.001), and the cut-off was selected to be 5.12 dL (AUC=0.778, OR 3.066, 95% CI (1.634, 5.753), p<0.001).
Conclusions: This study stresses the importance of fluid balance in the clinical care of AD patients and provides new insights for optimising fluid management and monitoring strategies beyond the conventional focus on blood pressure and heart rate management.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11808867 | PMC |
http://dx.doi.org/10.1136/bmjopen-2024-083933 | DOI Listing |
Cancer Biol Ther
December 2025
Department of Surgery, Hamad Medical Corporation, Doha, Qatar.
Early and precise diagnosis of cancer is pivotal for effective therapeutic intervention. Traditional diagnostic methods, despite their reliability, often face limitations such as invasiveness, high costs, labor-intensive procedures, extended processing times, and reduced sensitivity for early-stage detection. Electrochemical biosensing is a revolutionary method that provides rapid, cost-effective, and highly sensitive detection of cancer biomarkers.
View Article and Find Full Text PDFSurg Infect (Larchmt)
March 2025
Division of Trauma and Acute Care Surgery, Department of Surgery, Atrium Health Wake Forest Baptist Hospital, Winston-Salem, North Carolina, USA.
Ventilator-associated pneumonia (VAP) is a frequent complication in injured patients. Multiplex polymerase chain reaction (PCR) facilitates rapid identification of many respiratory pathogens prior to formal culture results. Our objective was to evaluate the effect of multiplex PCR implementation in a trauma intensive care unit (TICU) on antibiotic utilization and de-escalation.
View Article and Find Full Text PDFCirc Cardiovasc Imaging
March 2025
Department of Cardiology, Cardiovascular Institute, Thorax Center, Erasmus MC, Rotterdam, The Netherlands (J.J.S., N.v.d.V., D.M., A.H.).
Background: Very preterm-born infants are at risk for developing bronchopulmonary dysplasia (BPD), a chronic lung disease. Nowadays, the majority of these infants reach adulthood. Very preterm-born young adults are at risk for developing pulmonary arterial (PA) hypertension later in life.
View Article and Find Full Text PDFHaematologica
March 2025
Department of Oncology, Hematology and Bone Marrow Transplantation with Section Pneumology, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg.
Not available.
View Article and Find Full Text PDFJMIR Med Inform
March 2025
Department of Critical Care Medicine, Beijing Tiantan Hospital, Capital Medical University, No.119 Nansihuanxi Road, Fengtai District, Beijing, 100070, China, 86 17611757717.
Background: Publicly accessible critical care-related databases contain enormous clinical data, but their utilization often requires advanced programming skills. The growing complexity of large databases and unstructured data presents challenges for clinicians who need programming or data analysis expertise to utilize these systems directly.
Objective: This study aims to simplify critical care-related database deployment and extraction via large language models.
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