Stroke: epidemiology and outcomes

Objective: To determine the frequency of mortality, length of stay and nosocomial pneumonia outcomes, as well as their distribution according to predictor variables, in stroke patients treated at the emergency room of a tertiary hospital. Methods: A retrospective cohort study, with a sample of patients attended between January 1 and December 31, 2018. Based on the data collected in the medical records, the sample was characterized. Therefore, the frequency of each outcome was checked, as well as its distribution according to the predictor variables. Results: The sample population consisted of 210 patients. The frequencies observed in death and nosocomial pneumonia were 17.6% and 17.1%, respectively. The general mean length of stay was 13.8 ±12.9 days. Statistically significant differences were observed both in the occurrence of nosocomial pneumonia and atrial fibrillation (AF); days of hospitalization in intensive care unit; total days of hospitalization; orotracheal intubation; use of nasoenteral tube and surgical procedure secondary to stroke. Morever, there was also the relation of total time of hospitalization regarding dyslipidemia; orotracheal intubation; use of nasoenteral tube and surgical procedure secondary to stroke. Conclusion: The results found in the frequency of mortality, nosocomial pneumonia and mean total number of days of hospitalization are comparable with other Brazilian studies. However, it is possible to optimize the time of care provided for patients who arrive in the emergency room. In addition, the decrease of hospitalization days in dyslipidemic patients and the increase of nosocomial pneumonia in AF patients require further studies to verify such findings

A comparison of computational driver models using naturalistic and test-track data from cyclist-overtaking manoeuvres

The improvement of advanced driver assistance systems (ADAS) and their safety assessment rely on the understanding of scenario-dependent driving behaviours, such as steering to avoid collisions. This study compares driver models that predict when a driver starts steering away to overtake a cyclist on rural roads. The comparison is among four models: a threshold model, an accumulator model, and two models inspired by a proportional-integral and proportional-integral-derivative controller. These models were tested and cross-applied using two different datasets: one from a naturalistic driving (ND) study and one from a test-track (TT) experiment. Two perceptual variables, expansion rate (the horizontal angular expansion rate of the image of the lead road user on the driver’s retina) and inverse tau (the ratio between the image’s expansion rate and its horizontal optical size), were tested as input to the models. A linear cost function is proposed that can obtain the optimal parameters of the models by computationally efficient linear programming. The results show that the models based on inverse tau fitted the data better than the models that included expansion rate. In general, the models fitted the ND data reasonably well, but not as well the TT data. For the ND data, the models including an accumulative component outperformed the threshold model. For the TT data, due to the poorer fit of the models, more analysis is required to determine the merit of the models. The models fitted to TT data captured the overall pattern of steering onsets in the ND data rather well, but with a persistent bias, probably due to the drivers employing a more cautious strategy in TT. The models compared in this paper may support the virtual safety assessment of ADAS so that driver behaviour may be considered in the design and evaluation of new safety systems

On the importance of driver models for the development and assessment of active safety: A new collision warning system to make overtaking cyclists safer

The total number of road crashes in Europe is decreasing, but the number of crashes involving cyclists is not decreasing at the same rate. When cars and bicycles share the same lane, cars typically need to overtake them, creating dangerous conflicts—especially on rural roads, where cars travel much faster than cyclists. In order to protect cyclists, advanced driver assistance systems (ADAS) are being developed and introduced to the market. One of them is a forward collision warning (FCW) system that helps prevent rear-end crashes by identifying and alerting drivers of threats ahead. The objective of this study is to assess the relative safety benefit of a behaviour-based (BB) FCW system that protects cyclists in a car–to–cyclist overtaking scenario. Virtual safety assessments were performed on crashes derived from naturalistic driving data. A series of driver response models was used to simulate different driver reactions to the warning. Crash frequency in conjunction with an injury risk model was used to estimate the risk of cyclist injury and fatality. The virtual safety assessment estimated that, compared to no FCW, the BB FCW could reduce cyclists’ fatalities by 53–96% and serious injuries by 43–94%, depending on the driver response model. The shorter the driver’s reaction time and the larger the driver’s deceleration, the greater the benefits of the FCW. The BB FCW also proved to be more effective than a reference FCW based on the Euro NCAP standard test protocol. The findings of this study demonstrate the BB FCW’s great potential to avoid crashes and reduce injuries in car–to–cyclist overtaking scenarios, even when the driver response model did not exceed a comfortable rate of deceleration. The results suggest that a driver behaviour model integrated into ADAS collision threat algorithms can provide substantial safety benefits

Understanding the interaction between cyclists and automated vehicles: Results from a cycling simulator study

Cycling as an active mode of transport is increasing across all Europe [1]. Multiple benefits are coming from cycling both for the single user and the society as a whole. With increasing cycling, we expect more conflicts to happen between cyclists and vehicles, as it is also shown by the increasing cyclists' share of fatalities, contrary to the passenger cars' share [2]. Understanding cyclists' behavioral patterns can help automated vehicles (AVs) to predict cyclist's behavior, and then behave safely and comfortably when they encounter them. As a result, developing reliable predictive models of cyclist behavior will help AVs to interact safely with cyclists

Dysembryoplastic neuroepithelial tumor originally diagnosed as astrocytoma and oligodendroglioma

Dysembryoplastic neuroepithelial tumor (DNT), described in 1988 and introduced in the WHO classification in 1993, affects predominantly children or young adults causing intractable complex partial seizures. Since it is benign and treated with surgical resection, its recognition is important. It has similarities with low-grade gliomas and gangliogliomas, which may recur and become malignant. OBJECTIVES: To investigate whether DNT was previously diagnosed as astrocytoma, oligodendroglioma, or ganglioglioma and to determine its frequency in a series of low-grade glial/glio-neuronal tumors. METHODS: Clinical, radiological, and histological aspects of 58 tumors operated from 1978 to 2008, classified as astrocytomas (32, including 8 pilocytic), oligodendrogliomas (12), gangliogliomas (7), and DNT (7), were reviewed. RESULTS: Four new DNT, one operated before 1993, previously classified as astrocytoma (3) and oligodendroglioma (1), were identified. One DNT diagnosed in 2002 was classified once more as angiocentric glioma. Therefore, 10 DNT (17.2%) were identified. CONCLUSIONS: Clinical-radiological and histopathological correlations have contributed to diagnose the DNT