Publications by authors named "S Boussen"
Article Synopsis
- The study investigates the effectiveness of an automated algorithm for measuring intracranial blood volume to predict complications like delayed cerebral ischemia (DCI) and acute hydrocephalus (AH) in patients with aneurysmal subarachnoid hemorrhage (aSAH).
- It compares the algorithm's predictive accuracy using the area under the receiver operating characteristic curve (ROC) against eight established radiological scales.
- Results from 145 patients indicate that the algorithm performs similarly to existing scales in predicting DCI and AH, with significant correlations found, particularly with the Hijdra scale for cisternal blood volume.
Background: Severe trauma is the leading cause of disability and mortality in the patients under 35 years of age. Surgical site infections (SSI) represent a significant complication in this patient population. However, they are often inadequately investigated, potentially impacting the quality of patient outcomes.
View Article and Find Full Text PDFBackground: Cardiac injury caused by a sharp object is a medical and surgical therapeutic challenge. Mortality risk factors have been identified but there are major discrepancies in the literature. The aim of this study was to analyse the management of victims of penetrating cardiac injuries before and after admission to hospital and the anatomical characteristics of these injuries in order to facilitate diagnosis of the most critical patients.
View Article and Find Full Text PDFBackground/objectives: Aneurysmal subarachnoid haemorrhage (aSAH) is a life-threatening event with major complications. Delayed cerebral infarct (DCI) occurs most frequently 7 days after aSAH and can last for a prolonged period. To determine the most predictive radiological scales in grading subarachnoid or ventricular haemorrhage or both for functional outcome at 3 months in a large aSAH population, we conducted a single-centre retrospective study.
View Article and Find Full Text PDFThis study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status.
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