Abstract
Health-related quality of life (HR-QoL) is a subjective concept, reflecting the overall mental and physical state of the patient, and their own sense of well-being. Estimating current and future QoL has become a major outcome in the evaluation of critically ill patients. The aim of this study is to enhance the inference process of 6 weeks and 6 months prognosis of QoL after intensive care unit (ICU) stay, using the EQ-5D questionnaire. The main outcomes of the study were the EQ-5D five main dimensions: mobility, self-care, usual activities, pain and anxiety/depression. For each outcome, three Bayesian classifiers were built and validated with 10-fold cross-validation. Sixty and 473 patients (6 weeks and 6 months, respectively) were included. Overall, 6 months QoL is higher than 6 weeks, with the probability of absence of problems ranging from 31% (6 weeks mobility) to 72% (6 months self-care). Bayesian models achieved prognosis accuracies of 56% (6 months, anxiety/depression) up to 80% (6 weeks, mobility). The prognosis inference process for an individual patient was enhanced with the visual analysis of the models, showing that women, elderly, or people with longer ICU stay have higher risk of QoL problems at 6 weeks. Likewise, for the 6 months prognosis, a higher APACHE II severity score also leads to a higher risk of problems, except for anxiety/depression where the youngest and active have increased risk. Bayesian networks are competitive with less descriptive strategies, improve the inference process by incorporating domain knowledge and present a more interpretable model. The relationships among different factors extracted by the Bayesian models are in accordance with those collected by previous state-of-the-art literature, hence showing their usability as inference model.

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Abstract
Traditional follow-up of patients with cardiovascular devices is still an activity that, in addition to serving an increasing population, requires a considerable amount of time and specialized human and technical resources. Our aim was to evaluate the applicability of the CareLink (R) (Medtronic, Minneapolis, MN) remote monitoring system as a complementary option to the follow-up of patients with implanted devices, between in-office visits. Evaluated outcomes included both clinical (event detection and time to diagnosis) and nonclinical (patient's satisfaction and economic costs) aspects. An observational, longitudinal, prospective study was conducted with patients from a Portuguese central hospital sampled by convenience during 1 week (43 patients). Data were collected in four moments: two in-office visits and two remote evaluations, reproducing 1 year of clinical follow-up. Data sources included health records, implant reports, initial demographic data collection, follow-up printouts, and a questionnaire. After selection criteria were verified, 15 patients (11 men [73%]) were included, 63.4 +/- 10.8 years old, re-presenting 14.0 +/- 6.3 implant months. Clinically, 15 events were detected (9 by remote monitoring and 6 by patient-initiated activation), of which only 9 were symptomatic. We verified that remote monitoring could detect both symptomatic and asymptomatic events, whereas patient-initiated activation only detected symptomatic ones (p = 0.028). Moreover, the mean diagnosis anticipation in patients with events was approximately 58 days (p < 0.001). In nonclinical terms, we observed high or very high satisfaction (67% and 33%, respectively) with using remote monitoring technology, but still 8 patients (53%) stated they preferred in-office visits. Finally, the introduction of remote monitoring technology has the ability to reduce total follow-up costs for patients by 25%. We conclude that the use of this system constitutes a viable complementary option to the follow-up of patients with implantable devices, between in-office visits.

Abstract
Introduction Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. Materials and methods This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Results Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. Conclusions The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.

Abstract
Background: An asynchronous eLearning system was developed for radiographers in order to promote a better knowledge about senology and mammography. Objectives: to assess the learners' satisfaction. Methods: Target population included radiographers and radiography students, in order to assess eLearning satisfaction according to different experience levels in breast imaging. Satisfaction was measured through a questionnaire developed especially for eLearning systems, using a seven-point Likert scale. Main topics related are content, interface, personalization and learning community. Results: Overall, 85% of learners were satisfied with the course and 87,5% considered that the course is successful. Main areas that were evaluated by most learners in a positive way were interface and content (between six and seven-point); on the other hand, learning community presented a wider distribution of answers. Conclusions: The course provides an overall high degree of learner satisfaction, thus providing more effective knowledge gain on breast imaging for radiographers.

Abstract