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.

BACKGROUND: We previously validated a Bayesian network (BN) model for diagnosing ventilator-associated pneumonia (VAP). Here, we report on the performance of the model to predict microbial causes of VAP and to select antibiotics. METHODS: Pathogens were grouped into seven categories based upon the antibiotic susceptibility and epidemiological characteristics. Colonization of the upper respiratory tract was modelled in the BN and depended--in additional steps--on (i) duration of admission and ventilation, (ii) previous culture results and (iii) previous antibiotic use. A database with 153 VAP episodes and their microbial causes was used as reference standard. Appropriateness of antibiotic prescription, with fixed choices for pathogens predicted, was determined. RESULTS: One hundred and seven VAP episodes were monobacterial and 46 were caused by two pathogens. Using duration of admission and ventilation only, areas under the receiver operating curve (AUC) ranged from 0.511 to 0.772 for different pathogen groups, and model predictions significantly improved when adding information on culture results, but not when adding information on antibiotic use. The best performing model (with all information) had AUC values ranging from 0.859 for Acinetobacter spp. to 0.929 for Streptococcus pneumoniae. With this model, 91 (85%) and 29 (63%) of all pathogen groups were correctly predicted for monobacterial and polymicrobial VAP, respectively. With fixed antibiotic choices linked to pathogen groups, 92% of all episodes would have been treated appropriately. CONCLUSIONS: The BN models' performance to predict pathogens causing VAP improved markedly with information on colonization, resulting in excellent pathogen prediction and antibiotic selection. Prospective external validation is needed.