30 research outputs found
Course of Participation after Subarachnoid Hemorrhage
Objectives: The study aimed to investigate participation problems in patients with subarachnoid hemorrhage (SAH), and the course of participation between 3 and 12 months post-SAH, and to identify determinants of this course. Design: This is a prospective cohort study. Setting: The study was done in the SAH outpatient clinic at the University Medical Center Utrecht. Subjects: Subjects included patients independent in activities of daily living who visited the SAH outpatient clinic for a routine follow-up visit 3 months after the event. Main Measures: Participation was assessed using the restrictions scale of the Utrecht Scale for Evaluation of Rehabilitation-Participation at 3, 6, and 12 months post-SAH. Repeated measures analysis of variance was conducted to identify possible determinants of participation (demographic and SAH characteristics, mood, and cognition). Results: One hundred patients were included. Three months after SAH, the most commonly reported restrictions concerned work/unpaid work/education (70.5%), housekeeping (50.0%), and going out (45.2%). Twelve months post-SAH, patients felt most restricted in work/unpaid work/education (24.5%), housekeeping (23.5%), and chores in and around the house (16.3%). Participation scores increased significantly between 3 and 6 months, and between 3 and 12 months, post-SAH. The course of participation was associated with mood, cognition, and gender, but was in the multivariate analysis only determined by mood (F [1, 74] = 18.31, P = .000, partial eta squared:.20), showing lower participation scores at each time point for patients with mood disturbance. Conclusions: Participation in functionally independent SAH patients improved over time. However, 1 out of 3 patients (34.9%) still reported one or more participation restrictions 12 months post-SAH. Mood disturbance was negatively associated with the course of participation after SAH
Predicting pathogens causing ventilator-associated pneumonia using a Bayesian network model
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