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Objective: This retrospective study aimed to explore the association and clinical value of sequential organ failure assessment (SOFA) score on the predictors of adverse events in patients with unruptured abdominal aortic aneurysms (AAA).
Methods: A total of 322 patients from Medical Information Mart for Intensive Care IV database were enrolled. Logistic regression was conducted to explore the association between SOFA and primary outcome (need for surgery, NFS). Receiver operating characteristic (ROC) and nomogram analyses were used to assess its performance for predicting NFS. We also explored the association and clinical value of SOFA on secondary outcomes including hospital length of stay (LOS), ICU-LOS, and in-hospital mortality by linear and logistic regression analyses, generalized additive model, ROC, and decision curve analysis.
Results: Totally 291 patients underwent the surgery. High SOFA score significantly correlated with NFS both in crude and adjusted models (all P<0.05). SOFA had a relatively favorable prediction performance on NFS (AUC = 0.701, 95%CI: 0.596-0.802). After adjusting for related diseases, its prediction performance was increased. When SOFA was combined with lactate and gender, the model showed an AUC of 0.888 (95%CI: 0.759-1.000) and 0.3-0.9 prediction possibility. Further, the SOFA also showed significant relationship with hospital-LOS, ICU-LOS, and in-hospital mortality (all P<0.05), and exerted some value in the prediction of 7-day hospital-LOS (AUC = 0.637, 95%CI: 0.575-0.686) and in-hospital mortality (AUC = 0.637, 95%CI: 0.680-0.845).
Conclusions: SOFA score was related to the NFS and can be regarded as a useful indicator for predicting the NFS in patients with AAA.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11698317 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0314137 | PLOS |
Front Med (Lausanne)
August 2025
Department of Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Background: In critically ill patients with septic shock, adequate oxygenation is crucial and hypoxia should be avoided. However, hyperoxia has been linked to the formation of reactive oxygen species, inflammation, and vasoconstriction, which could potentially harm critically ill intensive care patients. Therefore, this study aimed to examine the association between oxygen exposure and mortality and to define optimal oxygen target ranges for this specific group of patients.
View Article and Find Full Text PDFBackground: Acute kidney injury (AKI) in patients with liver cirrhosis represents a significant clinical challenge with high mortality rates. This study aimed to develop and validate a machine learning-based prediction model for 28-day mortality in AKI patients with liver cirrhosis using the MIMIC-IV database.
Methods: This retrospective study analyzed data from 4,168 AKI patients, including 601 with concurrent liver cirrhosis, from the MIMIC-IV database.
Anesthesiol Res Pract
August 2025
Anesthesiology and Pain Medicine Department, Democritus University of Thrace, Alexandroupoli, Greece.
Nutritional screening is gaining recognition in perioperative medicine, as anesthesiologists need to assess patients' nutritional status to identify malnutrition risks. Poor nutritional status is associated with increased perioperative complications, including postoperative pain. Effective pain management is crucial to prevent acute pain from developing into chronic pain.
View Article and Find Full Text PDFCrit Care Res Pract
August 2025
Clinical Tuberculosis and Epidemiology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Sepsis remains one of the leading causes of morbidity and mortality worldwide, particularly among critically ill patients in intensive care units (ICUs). Traditional diagnostic approaches, such as the Sequential Organ Failure Assessment (SOFA) and systemic inflammatory response syndrome (SIRS) criteria, often detect sepsis after significant organ dysfunction has occurred, limiting the potential for early intervention. In this study, we reviewed how artificial intelligence (AI)-driven methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can aid physicians.
View Article and Find Full Text PDFZhong Nan Da Xue Xue Bao Yi Xue Ban
May 2025
Department of Cardiovascular Medicine, Affiliated Changsha Hospital of Xiangya School of Medicine, Central South University, Changsha 410005.
Objectives: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes.
View Article and Find Full Text PDF