Fall-related emergency department admission: fall environment and settings and related injury patterns in 6357 patients with special emphasis on the elderly

PRINCIPALS Throughout the world, falls are a major public health problem and a socioeconomic burden. Nevertheless there is little knowledge about how the injury types may be related to the aetiology and setting of the fall, especially in the elderly. We have therefore analysed all patients presenting with a fall to our Emergency Department (ED) over the past five years. METHODS Our retrospective data analysis comprised adult patients admitted to our Emergency Department between January 1, 2006, and December 31, 2010, in relation to a fall. RESULTS Of a total of 6357 patients 78% (n = 4957) patients were younger than 75 years. The main setting for falls was patients home (n = 2239, 35.3%). In contrast to the younger patients, the older population was predominantly female (56.3% versus 38.6%; P < 0.0001). Older patients were more likely to fall at home and suffer from medical conditions (all P < 0.0001). Injuries to the head (P < 0.0001) and to the lower extremity (P < 0.019) occurred predominantly in the older population. Age was the sole predictor for recurrent falls (OR 1.2, P < 0.0001). CONCLUSION Falls at home are the main class of falls for all age groups, particularly in the elderly. Fall prevention strategies must therefore target activities of daily living. Even though falls related to sports mostly take place in the younger cohort, a significant percentage of elderly patients present with falls related to sporting activity. Falls due to medical conditions were most likely to result in mild traumatic brain injury.

The matrix metalloproteinase inhibitor RS-130830 attenuates brain injury in experimental pneumococcal meningitis.

BACKGROUND Pneumococcal meningitis (PM) is characterized by high mortality and morbidity including long-term neurofunctional deficits. Neuropathological correlates of these sequelae are apoptosis in the hippocampal dentate gyrus and necrosis in the cortex. Matrix metalloproteinases (MMPs) play a critical role in the pathophysiology of PM. RS-130830 (Ro-1130830, CTS-1027) is a potent partially selective inhibitor of MMPs of a second generation and has been evaluated in clinical trials as an anti-arthritis drug. It inhibits MMPs involved in acute inflammation but has low activity against MMP-1 (interstitial collagenase), MMP-7 (matrilysin) and tumour necrosis factor α converting enzyme (TACE). METHODS A well-established infant rat model of PM was used where live Streptococcus pneumoniae were injected intracisternally and antibiotic treatment with ceftriaxone was initiated 18 h post infection (hpi). Treatment with RS-130830 (75 mg/kg bis in die (bid) i.p., n = 40) was started at 3 hpi while control littermates received the vehicle (succinylated gelatine, n = 42). RESULTS Cortical necrosis was significantly attenuated in animals treated with RS-130830, while the extent of hippocampal apoptosis was not influenced. At 18 hpi, concentrations of interleukin (IL)-1β and IL-10 were significantly lower in the cerebrospinal fluid of treated animals compared to controls. RS-130830 significantly reduced weight loss and leukocyte counts in the cerebrospinal fluid of survivors of PM. CONCLUSION This study identifies MMP inhibition, specifically with RS-130830, as an efficient strategy to attenuate disease severity and cortical brain injury in PM.

Early brain injury linearly correlates with reduction in cerebral perfusion pressure during the hyperacute phase of subarachnoid hemorrhage

BACKGROUND It is unclear how complex pathophysiological mechanisms that result in early brain injury (EBI) after subarachnoid hemorrhage (SAH) are triggered. We investigate how peak intracranial pressure (ICP), amount of subarachnoid blood, and hyperacute depletion of cerebral perfusion pressure (CPP) correlate to the onset of EBI following experimental SAH. METHODS An entire spectrum of various degrees of SAH severities measured as peak ICP was generated and controlled using the blood shunt SAH model in rabbits. Standard cardiovascular monitoring, ICP, CPP, and bilateral regional cerebral blood flow (rCBF) were continuously measured. Cells with DNA damage and neurodegeneration were detected using terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) and Fluoro-jade B (FJB). RESULTS rCBF was significantly correlated to reduction in CPP during the initial 15 min after SAH in a linear regression pattern (r (2) = 0.68, p < 0.001). FJB- and TUNEL-labeled cells were linearly correlated to reduction in CPP during the first 3 min of hemorrhage in the hippocampal regions (FJB: r (2) = 0.50, p < 0.01; TUNEL: r (2) = 0.35, p < 0.05), as well as in the basal cortex (TUNEL: r (2) = 0.58, p < 0.01). EBI occurred in animals with severe (relative CPP depletion >0.4) and moderate (relative CPP depletion >0.25 but <0.4) SAH. Neuronal cell death was equally detected in vulnerable and more resistant brain regions. CONCLUSIONS The degree of EBI in terms of neuronal cell degeneration in both the hippocampal regions and the basal cortex linearly correlates with reduced CPP during hyperacute SAH. Temporary CPP reduction, however, is not solely responsible for EBI but potentially triggers processes that eventually result in early brain damage.

Successful transnasal delivery of stem cells in a rat model of perinatal hypoxic-ischemic brain injury

OBJECTIVE: New routes for cell transplantation into the brain need to be explored as intracerebral or intrathecal applications have a high risk to cause damage to the central nervous system. It has been hypothesized that transnasally administrated cells bypass the blood-brain barrier and migrate along the olfactory neural route into the brain and cerebrospinal fluid. Our goal is to confirm this hypothesis by transnasally administrating Wharton’s Jelly mesenchymal stem cells (WJ-MSC) and neural progenitor cells (NPC) to perinatal rats in a model of hypoxic-ischemic brain injury. STUDY DESIGN: Four-day-old Wistar rat pups, previously brain-damaged by combined hypoxic-ischemic and inflammatory insult, either received WJ-MSC or green fluorescent protein-expressing NPC: The heads of the rat pups were immobilized and 3 ml drops containing the cells (50’000 cells/ml) were placed on one nostril allowing it to be snorted. This procedure was repeated twice, alternating right to left nostril with an interval of one minute between administrations. The rat pups received a total of 600’000 cells. Animals were sacrificed 24h, 48h or 7 days after the application of the cells. Fixed brains were collected, embedded in paraffin and sectioned. RESULTS: Transplanted cells were found in the layers of the olfactory bulb (OB), the cerebral cortex, thalamus and the hippocampus. The amount of cells was highest in the OB. Animals treated with transnasally delivered stem cells showed significantly decreased gliosis compared to untreated animals. CONCLUSION: Our data show that transnasal delivery of WJ-MSC and NPC to the newborn brain after perinatal brain damage is successful. The cells not only migrate the brain, but also decrease scar formation and improve neurogenesis. Therefore, the non-invasive intranasal delivery of stem cells to the brain may be the preferred method for stem cell treatment of perinatal brain damage and should be preferred in future clinical trials.

Role of TRP Channels in Mediating the Calcium Signaling Response of Brain Endothelial Cells to Mechanical Stretch

Traumatic brain injury (TBI) often results in disruption of the blood brain barrier (BBB), which is an integral component to maintaining the central nervous system homeostasis. Recently cytosolic calcium levels ([Ca2+]i), observed to elevate following TBI, have been shown to influence endothelial barrier integrity. However, the mechanism by which TBI-induced calcium signaling alters the endothelial barrier remains unknown. In the present study, an in vitro BBB model was utilized to address this issue. Exposure of cells to biaxial mechanical stretch, in the range expected for TBI, resulted in a rapid cytosolic calcium increase. Modulation of intracellular and extracellular Ca2+ reservoirs indicated that Ca2+ influx is the major contributor for the [Ca2+]i elevation. Application of pharmacological inhibitors was used to identify the calcium-permeable channels involved in the stretch-induced Ca2+ influx. Antagonist of transient receptor potential (TRP) channel subfamilies, TRPC and TRPP, demonstrated a reduction of the stretch-induced Ca2+ influx. RNA silencing directed at individual TRP channel subtypes revealed that TRPC1 and TRPP2 largely mediate the stretch-induced Ca2+ response. In addition, we found that nitric oxide (NO) levels increased as a result of mechanical stretch, and that inhibition of TRPC1 and TRPP2 abolished the elevated NO synthesis. Further, as myosin light chain (MLC) phosphorylation and actin cytoskeleton rearrangement are correlated with endothelial barrier disruption, we investigated the effect mechanical stretch had on the myosin-actin cytoskeleton. We found that phosphorylated MLC was increased significantly by 10 minutes post-stretch, and that inhibition of TRP channel activity or NO synthesis both abolished this effect. In addition, actin stress fibers formation significantly increased 2 minutes post-stretch, and was abolished by treatment with TRP channel inhibitors. These results suggest that, in brain endothelial cells, TRPC1 and TRPP2 are activated by TBI-mechanical stress and initiate actin-myosin contraction, which may lead to disruption of the BBB.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

A computational model coupling mechanics and electrophysiology in spinal cord injury

Traumatic brain injury and spinal cord injury have recently been put under the spotlight as major causes of death and disability in the developed world. Despite the important ongoing experimental and modeling campaigns aimed at understanding the mechanics of tissue and cell damage typically observed in such events, the differenti- ated roles of strain, stress and their corresponding loading rates on the damage level itself remain unclear. More specif- ically, the direct relations between brain and spinal cord tis- sue or cell damage, and electrophysiological functions are still to be unraveled. Whereas mechanical modeling efforts are focusing mainly on stress distribution and mechanistic- based damage criteria, simulated function-based damage cri- teria are still missing. Here, we propose a new multiscale model of myelinated axon associating electrophysiological impairment to structural damage as a function of strain and strain rate. This multiscale approach provides a new framework for damage evaluation directly relating neuron mechanics and electrophysiological properties, thus provid- ing a link between mechanical trauma and subsequent func- tional deficits.

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Neuroanatomic-based detection algorithm for automatic labeling of brain structures in brain injury

The number and grade of injured neuroanatomic structures and the type of injury determine the degree of impairment after a brain injury event and the recovery options of the patient. However, the body of knowledge and clinical intervention guides are basically focused on functional disorder and they still do not take into account the location of injuries. The prognostic value of location information is not known in detail either. This paper proposes a feature-based detection algorithm, named Neuroanatomic-Based Detection Algorithm (NBDA), based on SURF (Speeded Up Robust Feature) to label anatomical brain structures on cortical and sub-cortical areas. Themain goal is to register injured neuroanatomic structures to generate a database containing patient?s structural impairment profile. This kind of information permits to establish a relation with functional disorders and the prognostic evolution during neurorehabilitation procedures.

Clustering techniques for patients suffering acquired brain injury inneuro personal trainer

The study of the effectiveness of the cognitive rehabilitation processes and the identification of cognitive profiles, in order to define comparable populations, is a controversial area, but concurrently it is strongly needed in order to improve therapies. There is limited evidence about cognitive rehabilitation efficacy. Many of the trials conclude that in spite of an apparent clinical good response, differences do not show statistical significance. The common feature in all these trials is heterogeneity among populations. In this situation, observational studies on very well controlled cohort of studies, together with innovative methods in knowledge extraction, could provide methodological insights for the design of more accurate comparative trials. Some correlation studies between neuropsychological tests and patients capacities have been carried out -1---2- and also correlation between tests and morphological changes in the brain -3-. The procedures efficacy depends on three main factors: the affectation profile, the scheduled tasks and the execution results. The relationship between them makes up the cognitive rehabilitation as a discipline, but its structure is not properly defined. In this work we present a clustering method used in Neuro Personal Trainer (NPT) to group patients into cognitive profiles using data mining techniques. The system uses these clusters to personalize treatments, using the patients assigned cluster to select which tasks are more suitable for its concrete needs, by comparing the results obtained in the past by patients with the same profile.

Brain structures identification based on feature descriptor

Traumatic Brain Injury -TBI- -1- is defined as an acute event that causes certain damage to areas of the brain. TBI may result in a significant impairment of an individuals physical, cognitive and psychosocial functioning. The main consequence of TBI is a dramatic change in the individuals daily life involving a profound disruption of the family, a loss of future income capacity and an increase of lifetime cost. One of the main challenges of TBI Neuroimaging is to develop robust automated image analysis methods to detect signatures of TBI, such as: hyper-intensity areas, changes in image contrast and in brain shape. The final goal of this research is to develop a method to identify the altered brain structures by automatically detecting landmarks on the image where signal changes and to provide comprehensive information to the clinician about them. These landmarks identify injured structures by co-registering the patient?s image with an atlas where landmarks have been previously detected. The research work has been initiated by identifying brain structures on healthy subjects to validate the proposed method. Later, this method will be used to identify modified structures on TBI imaging studies.

Intelligent Therapy Assistant (ITA) for cognitive rehabilitation in patients with Acquired Brain Injury

This paper presents the design, development and first evaluation of an algorithm, named Intelligent Therapy Assistant (ITA), which automatically selects, configures and schedules rehabilitation tasks for patients with cognitive impairments after an episode of Acquired Brain Injury. The ITA is integrated in "Guttmann, Neuro Personal Trainer" (GNPT), a cognitive tele-rehabilitation platform that provides neuropsychological services.

Improving brain injury cognitive rehabilitation by personalized telerehabilitation services: Guttmann neuropersonal trainer

Cognitive rehabilitation aims to remediate or alleviate the cognitive deficits appearing after an episode of acquired brain injury (ABI). The purpose of this work is to describe the telerehabilitation platform called Guttmann Neuropersonal Trainer (GNPT) which provides new strategies for cognitive rehabilitation, improving efficiency and access to treatments, and to increase knowledge generation from the process. A cognitive rehabilitation process has been modeled to design and develop the system, which allows neuropsychologists to configure and schedule rehabilitation sessions, consisting of set of personalized computerized cognitive exercises grounded on neuroscience and plasticity principles. It provides remote continuous monitoring of patient's performance, by an asynchronous communication strategy. An automatic knowledge extraction method has been used to implement a decision support system, improving treatment customization. GNPT has been implemented in 27 rehabilitation centers and in 83 patients' homes, facilitating the access to the treatment. In total, 1660 patients have been treated. Usability and cost analysis methodologies have been applied to measure the efficiency in real clinical environments. The usability evaluation reveals a system usability score higher than 70 for all target users. The cost efficiency study results show a relation of 1-20 compared to face-to-face rehabilitation. GNPT enables brain-damaged patients to continue and further extend rehabilitation beyond the hospital, improving the efficiency of the rehabilitation process. It allows customized therapeutic plans, providing information to further development of clinical practice guidelines.

A computational model coupling mechanics and electrophysiology in spinal cord injury

Traumatic brain injury and spinal cord injury have recently been put under the spotlight as major causes of death and disability in the developed world. Despite the important ongoing experimental and modeling campaigns aimed at understanding the mechanics of tissue and cell damage typically observed in such events, the differentiated roles of strain, stress and their corresponding loading rates on the damage level itself remain unclear. More specifically, the direct relations between brain and spinal cord tissue or cell damage, and electrophysiological functions are still to be unraveled. Whereas mechanical modeling efforts are focusing mainly on stress distribution and mechanistic-based damage criteria, simulated function-based damage criteria are still missing. Here, we propose a new multiscale model of myelinated axon associating electrophysiological impairment to structural damage as a function of strain and strain rate. This multiscale approach provides a new framework for damage evaluation directly relating neuron mechanics and electrophysiological properties, thus providing a link between mechanical trauma and subsequent functional deficits