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Gomes E.,Centro Hospitalar do Porto | Antunes R.,Centro Hospitalar do Porto | Dias C.,University of Porto | Arajo R.,Centro Hospitalar do Porto | Costa-Pereira A.,CINTESIS
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine | Year: 2010

Background. Acute kidney injury (AKI) has been hard to assess due to the lack of standard definitions. Recently, the Risk, Injury, Failure, Loss and End-Stage Kidney (RIFLE) classification has been proposed to classify AKI in a number of clinical settings. This study aims to estimate the frequency and levels of severity of AKI and to study its association with patient mortality and length of stay (LOS) in a cohort of trauma patients needing intensive care. Methods. Between August 2001 and September 2007, 436 trauma patients consecutively admitted to a general intensive care unit (ICU), were assessed using the RIFLE criteria. Demographic data, characteristics of injury, and severity of trauma variables were also collected. Results. Half of all ICU trauma admissions had AKI, which corresponded to the group of patients with a significantly higher severity of trauma. Among patients with AKI, RIFLE class R (Risk) comprised 47%, while I (Injury) and F (Failure) were, 36% and 17%, respectively. None of these patients required renal replacement therapy. No significant differences were found among these three AKI classes in relation to patient's age, gender, type and mechanism of injury, severity of trauma or mortality. Nevertheless, increasing severity of acute renal injury was associated with a longer ICU stay. Conclusions. AKI is a common feature among trauma patients requiring intensive care. Although the development of AKI is associated with an increased LOS it does not appear to influence patient mortality. © 2010 Gomes et al; licensee BioMed Central Ltd. Source

Kikuste I.,University of Latvia | Kikuste I.,Digestive Diseases Center | Stirna D.,Digestive Diseases Center | Liepniece-Karele I.,Academic Histology Laboratory | And 4 more authors.
European Journal of Gastroenterology and Hepatology | Year: 2014

BACKGROUND: Targeting biopsies on the basis of visual recognition of mucosal changes in the stomach instead of the currently accepted random biopsy sampling may be attractive. AIM: The aim of this study was to evaluate the accuracy of endoscopic findings using flexible spectral imaging colour enhancement (FICE) for intestinal metaplasia (IM) in the gastric mucosa. METHODS: A consecutive cohort of 126 individuals aged over 50 years (27% men) was subjected to upper endoscopy using FICE. Histological assessment (per patient and per biopsy) was considered the gold standard to accuracy estimates. RESULTS: Histological assessment revealed IM in 50% of the individuals [OLGIM (operative link on gastric intestinal metaplasia assessment) stages I-IV]. Overall, endoscopy presented sensitivities, specificities, positive likelihood ratio, negative likelihood ratio and accuracies per patient of 60% [95% confidence interval (95% CI) 48-72], 87% (95% CI 79-95), 4.7 (95% CI 2.4-93), 0.45 (95% CI 0.33-0.62) and 74% (95% CI 0.66-0.82), respectively, for IM diagnosis and 71% (95% CI 37-100), 87% (95% CI 79-95), 5.6 (95% CI 2.5-12.5), 0.32 (95% CI 0.10-1.0) and 86% (95% CI 77-94), respectively, for selecting individuals with OLGIM (III-IV). The proportions of agreement (and κ values) for IM in the antrum and the corpus were 75% (0.37) and 81% (0.19), respectively. CONCLUSION: FICE endoscopy yielded favourable results in the endoscopic diagnosis of IM of the gastric mucosa, and this is a very practical and easy method to use in daily clinical practice for unselected patients. Our study demonstrated a good specificity for FICE endoscopy to detect IM in the stomach. Further improvement in disseminating and training of this assessment is required to improve the reliability. © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. Source

Dias-Silva D.,CINTESIS | Pimentel-Nunes P.,Portuguese Institute of Porto | Pimentel-Nunes P.,University of Porto | Magalhaes J.,Centro Hospitalar Do Alto Ave | And 8 more authors.
Gastrointestinal Endoscopy | Year: 2014

Background A simplified narrow-band imaging (NBI) endoscopy classification of gastric precancerous and cancerous lesions was derived and validated in a multicenter study. This classification comes with the need for dissemination through adequate training. Objective To address the learning curve of this classification by endoscopists with differing expertise and to assess the feasibility of a YouTube-based learning program to disseminate it. Design Prospective study. Setting Five centers. Participants Six gastroenterologists (3 trainees, 3 fully trained endoscopists [FTs]). Interventions Twenty tests provided through a Web-based program containing 10 randomly ordered NBI videos of gastric mucosa were taken. Feedback was sent 7 days after every test submission. Main Outcome Measurements Measures of accuracy of the NBI classification throughout the time. Results From the first to the last 50 videos, a learning curve was observed with a 10% increase in global accuracy, for both trainees (from 64% to 74%) and FTs (from 56% to 65%). After 200 videos, sensitivity and specificity of 80% and higher for intestinal metaplasia were observed in half the participants, and a specificity for dysplasia greater than 95%, along with a relevant likelihood ratio for a positive result of 7 to 28 and likelihood ratio for a negative result of 0.21 to 0.82, were achieved by all of the participants. No constant learning curve was observed for the identification of Helicobacter pylori gastritis and sensitivity to dysplasia. The trainees had better results in all of the parameters, except specificity for dysplasia, compared with the FTs. Globally, participants agreed that the program's structure was adequate, except on the feedback, which should have consisted of a more detailed explanation of each answer. Limitations No formal sample size estimate. Conclusion A Web-based learning program could be used to teach and disseminate classifications in the endoscopy field. In this study, an NBI classification for gastric mucosal features seems to be easily learned for the identification of gastric preneoplastic lesions. © 2014 by the American Society for Gastrointestinal Endoscopy. Source

Rebuge A.,University of Porto | Lapao L.V.,CINTESIS | Lapao L.V.,New University of Lisbon | Freitas A.,CINTESIS | Cruz-Correia R.,CINTESIS
Proceedings - IEEE Symposium on Computer-Based Medical Systems | Year: 2013

Process mining can be used to extract healthcare processes related information from event logs by performing analysis exploiting the information recorded in it. We report a process mining analysis made to an event log containing traces on user activity recorded by a Virtual Electronic Patient Record (VEPR) system of a Central Hospital. A set of technical analyses were performed. Results from the discovery and characterization of global behavior and from a time series analysis on observed user tasks are reported. Process mining was applied successfully to discover, characterize and analyze user behavior recorded from VEPR. Worth noting the execution of tasks profile observed after log out, revealing significant security problems. © 2013 IEEE. Source

Riaz F.,University of Porto | Areia M.,Instituto Portugues Of Oncologia | Silva F.B.,Karolinska Universitessjukhuse | Dinis-Ribeiro M.,CINTESIS | And 2 more authors.
Proceedings - International Symposium on Biomedical Imaging | Year: 2011

Automatic classification of cancer lesions for gastroenterology imaging scenarios poses novel challenges to computer assisted decision systems, owing to their distinct visual characteristics such as reduced color spaces or natural organic textures. In this paper, we explore the prospects of using Gabor filters in a texton framework for the classification of images from two distinct imaging modalities (chromoendoscopy and narrow-band imaging) into three different groups: normal, precancerous and cancerous. Results show that they produce consistent results for both imaging modalities, hinting on their possible generic use for the classification of in-body images. © 2011 IEEE. Source

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