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Garcia-Altes A.,Agencia dInformacio | Perez K.,CIBER ISCIII | Novoa A.,CIBER ISCIII | Bernabeu M.,Institute Guttmann Neurorehabilitation Hospital | And 6 more authors.
Neuroepidemiology | Year: 2012

Background: Among traumatic injuries, spinal cord injuries (SCI) and traumatic brain injuries (TBI) are of major importance because of their epidemiological and economic impact on society. The overall objective of this study was to estimate the economic cost associated with people with SCI and TBI in Spain in 2007. Methods: A cost-of-illness analysis was performed, considering the perspective of society, using a 1-year time horizon. Medical costs, adaptation costs, material costs, administrative costs, and costs of police, firefighters and roadside assistance, productivity losses due to institutionalization and sick leave, as well as an estimate of productivity losses of carers, and productivity losses due to death were included. Results: The economic cost associated with people with SCI is between EUR 92,087,080.97 and 212,496,196.41 (USD 131 million and 302 million) according to the injury mechanism, and between EUR 1,079,223,688.66 and 3,833,752,692.78 (USD 1,536 million and 5,458 million) for people with TBI. Conclusions: There is an urgent need to develop effective interventions known to prevent SCI and TBI, and to evaluate their effectiveness and efficiency. Copyright © 2012 S. Karger AG, Basel.

Opisso E.,Institute Guttmann Neurorehabilitation Hospital | Borau A.,Institute Guttmann Neurorehabilitation Hospital | Rijkhoff N.J.M.,University of Aalborg
Journal of Neural Engineering | Year: 2011

The goal of this study was to investigate whether real-time external urethral sphincter (EUS) EMG-controlled dorsal genital nerve (DGN) stimulation can suppress undesired detrusor bladder contractions in patients with both neurogenic detrusor overactivity (NDO) and detrusor sphincter dyssynergia (DSD). Detrusor pressure (Pdet) and EUS EMG were recorded in 12 neurogenic patients who underwent two filling cystometries. The first one was without stimulation and was intended to confirm the NDO and DSD and to set the EMG detection threshold. The second one was with real-time EMG-controlled stimulation of DGNs. Two detection methods were analyzed to detect bladder contractions. The first method was a Kurtosis-scaled root mean square (RMS) detector and was used on-line. The second was a simple RMS detector and was used off-line. Of 12 patients included, 10 patients showed both NDO and DSD. In nine of these ten patients relevant EMG concomitant to detrusor activity was detected and stimulation could suppress at least one detrusor contraction. The second filling compared to the first one showed an increase of 84% in bladder capacity (p = 0.002) and a decrease of 106% in Pdet (p = 0.002). Nine false-positive detections occurred during the ten fillings with electrical stimulation. The mean increases of both time and Pdet between stimulation and bladder contraction onsets for method 1 were 1.8 s and 4 cmH2O and for method 2 were 0.9 s and 2 cmH2O, respectively. This study shows that EUS EMG can be used in real time to detect the onset of a bladder contraction. In combination with DGN stimulation has been shown to be feasible to suppress undesired bladder contractions and in turn to increase bladder capacity in subjects with both NDO and DSD. © 2011 IOP Publishing Ltd.

Marcano-Cedeno A.,Technical University of Madrid | Chausa P.,Technical University of Madrid | Garcia A.,Institute Guttmann Neurorehabilitation Hospital | Caceres C.,Technical University of Madrid | And 2 more authors.
Expert Systems with Applications | Year: 2013

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. © 2012 Elsevier Ltd. All rights reserved.

Palacios E.M.,August Pi i Sunyer Biomedical Research Institute | Sala-Llonch R.,August Pi i Sunyer Biomedical Research Institute | Sala-Llonch R.,University of Barcelona | Junque C.,University of Barcelona | And 4 more authors.
JAMA Neurology | Year: 2013

Importance The study of brain activity and connectivity at rest provides a unique opportunity for the investigation of the brain substrates of cognitive outcome after traumatic axonal injury. This knowledge may contribute to improve clinical management and rehabilitation programs. OBJECTIVE To study functional magnetic resonance imaging abnormalities in signal amplitude and brain connectivity at rest and their relationship to cognitive outcome in patients with chronic and severe traumatic axonal injury. DESIGN Observational study. SETTING University of Barcelona and Hospital Clinic de Barcelona, Barcelona, and Institut Guttmann-Neurorehabilitation Hospital, Badalona, Spain. PARTICIPANTS Twenty patients with traumatic brain injury (TBI) were studied, along with 17 matched healthy volunteers. INTERVENTIONS Resting-state functional magnetic resonance imaging and diffusion tensor imaging data were acquired. After exploring group differences in amplitude of low-frequency fluctuations (ALFF), we studied functional connectivity within the default mode network (DMN) by means of independent component analysis, followed by a dual regression approach and seed-based connectivity analyses. Finally, we performed probabilistic tractography between the frontal and posterior nodes of the DMN. MAIN OUTCOMES AND MEASURES Signal amplitude and functional connectivity during the resting state, tractography related to DMN, and the association between signal amplitudes and cognitive outcome. RESULTS Patients had greater ALFF in frontal regions, whichwas correlated with cognitive performance. Within the DMN, patients showed increased connectivity in the frontal lobes. Seed-based connectivity analyses revealed augmented connectivity within surrounding areas of the frontal and left parietal nodes of the DMN. Fractional anisotropy of the cingulate tract was correlated with increased connectivity of the frontal node of theDMNin patients with TBI. CONCLUSIONS AND RELEVANCE Increased ALFF is related to better cognitive performance in chronic TBI. The loss of structural connectivity produced by damage to the cingulum tract explained the compensatory increases in functional connectivity within the frontal node of the DMN.

Marcano-Cedeno A.,Complutense University of Madrid | Marcano-Cedeno A.,CIBER ISCIII | Chausa P.,Complutense University of Madrid | Chausa P.,CIBER ISCIII | And 6 more authors.
Artificial Intelligence in Medicine | Year: 2013

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. © 2013 Elsevier B.V.

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