994 resultados para Injury Prediction.


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BACKGROUND: Recent literature demonstrates hyperglycemia to be common in patients with trauma and associated with poor outcome in patients with traumatic brain injury and critically ill patients. The goal of this study was to analyze the impact of admission blood glucose on the outcome of surviving patients with multiple injuries. METHODS: Patients' charts (age >16) admitted to the emergency room of the University Hospital of Berne, Switzerland, between January 1, 2002, and December 31, 2004, with an Injury Severity Score >or=17 and more than one severely injured organ system were reviewed retrospectively. Outcome measurements included morbidity, intensive care unit, and hospital length of stay. RESULTS: The inclusion criteria were met by 555 patients, of which 108 (19.5%) patients died. After multiple regression analysis, admission blood glucose proved to be an independent predictor of posttraumatic morbidity (p < 0.0001), intensive care unit, and hospital length of stay (p < 0.0001), despite intensified insulin therapy on the intensive care unit. CONCLUSIONS: In this population of patients with multiple injuries, hyperglycemia on admission was strongly associated with increased morbidity, especially infections, prolonged intensive care unit, and hospital length of stay independent of injury severity, gender, age, and various biochemical parameters.

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Serial quantitative and correlative studies of experimental spinal cord injury (SCI) in rats were conducted using three-dimensional magnetic resonance imaging (MRI). Correlative measures included morphological histopathology, neurobehavioral measures of functional deficit, and biochemical assays for N-acetyl-aspartate (NAA), lactate, pyruvate, and ATP. A spinal cord injury device was characterized and provided a reproducible injury severity. Injuries were moderate and consistent to within $\pm$20% (standard deviation). For MRI, a three-dimensional implementation of the single spin-echo FATE (Fast optimum angle, short TE) pulse sequence was used for rapid acquisition, with a 128 x 128 x 32 (x,y,z) matrix size and a 0.21 x 0.21 x 1.5 mm resolution. These serial studies revealed a bimodal characteristic in the evolution in MRI pathology with time. Early and late phases of SCI pathology were clearly visualized in $T\sb2$-weighted MRI, and these corresponded to specific histopathological changes in the spinal cord. Centralized hypointense MRI regions correlated with evidence of hemorrhagic and necrotic tissue, while surrounding hyperintense regions represented edema or myelomalacia. Unexpectedly, $T\sb2$-weighted MRI pathology contrast at 24 hours after injury appeared to subside before peaking at 72 hours after injury. This change is likely attributable to ongoing secondary injury processes, which may alter local $T\sb2$ values or reduce the natural anisotropy of the spinal cord. MRI, functional, and histological measures all indicated that 72 hours after injury was the temporal maximum for quantitative measures of spinal cord pathology. Thereafter, significant improvement was seen only in neurobehavioral scores. Significant correlations were found between quantitated MRI pathology and histopathology. Also, NAA and lactate levels correlated with behavioral measures of the level of function deficit. Asymmetric (rostral/caudal) changes in NAA and lactate due to injury indicate that rostral and caudal segments from the injury site are affected differently by the injury. These studies indicate that volumetric quantitation of MRI pathology from $T\sb2$-weighted images may play an important role in early prediction of neurologic deficit and spinal cord pathology. The loss of $T\sb2$ contrast at 24 hours suggests MR may be able to detect certain delayed mechanisms of secondary injury which are not resolved by histopathology or other radiological modalities. Furthermore, in vivo proton magnetic resonance spectroscopy (MRS) studies of SCI may provide a valuable addition source of information about changes in regional spinal cord lactate and NAA levels, which are indicative of local metabolic and pathological changes. ^

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BACKGROUND Zebrafish is a clinically-relevant model of heart regeneration. Unlike mammals, it has a remarkable heart repair capacity after injury, and promises novel translational applications. Amputation and cryoinjury models are key research tools for understanding injury response and regeneration in vivo. An understanding of the transcriptional responses following injury is needed to identify key players of heart tissue repair, as well as potential targets for boosting this property in humans. RESULTS We investigated amputation and cryoinjury in vivo models of heart damage in the zebrafish through unbiased, integrative analyses of independent molecular datasets. To detect genes with potential biological roles, we derived computational prediction models with microarray data from heart amputation experiments. We focused on a top-ranked set of genes highly activated in the early post-injury stage, whose activity was further verified in independent microarray datasets. Next, we performed independent validations of expression responses with qPCR in a cryoinjury model. Across in vivo models, the top candidates showed highly concordant responses at 1 and 3 days post-injury, which highlights the predictive power of our analysis strategies and the possible biological relevance of these genes. Top candidates are significantly involved in cell fate specification and differentiation, and include heart failure markers such as periostin, as well as potential new targets for heart regeneration. For example, ptgis and ca2 were overexpressed, while usp2a, a regulator of the p53 pathway, was down-regulated in our in vivo models. Interestingly, a high activity of ptgis and ca2 has been previously observed in failing hearts from rats and humans. CONCLUSIONS We identified genes with potential critical roles in the response to cardiac damage in the zebrafish. Their transcriptional activities are reproducible in different in vivo models of cardiac injury.

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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.

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National Highway Traffic Safety Administration, Washington, D.C.

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Mode of access: Internet.

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Predictors of outcome following whiplash injury are limited to socio-demographic and symptomatic factors, which are not readily amenable to secondary and tertiary intervention. This prospective study investigated the predictive capacity of early measures of physical and psychological impairment on pain and disability 6 months following whiplash injury. Motor function (ROM; kinaesthetic sense; activity of the superficial neck flexors (EMG) during cranio-cervical flexion), quantitative sensory testing (pressure, thermal pain thresholds, brachial plexus provocation test), sympathetic vasoconstrictor responses and psychological distress (GHQ-28, TSK, IES) were measured in 76 acute whiplash participants. The outcome measure was Neck Disability Index scores at 6 months. Stepwise regression analysis was used to predict the final NDI score. Logistic regression analyses predicted membership to one of the three groups based on final NDI scores (< 8 recovered, 10-28 mild pain and disability, > 30 moderate/severe pain and disability). Higher initial NDI score (1.007-1.12), older age (1.03-1.23), cold hyperalgesia (1.05-1.58), and acute post-traumatic stress (1.03-1.2) predicted membership to the moderate/severe group. Additional variables associated with higher NDI scores at 6 months on stepwise regression analysis were: ROM loss and diminished sympathetic reactivity. Higher initial NDI score (1.03-1.28), greater psychological distress (GHQ-28) (1.04-1.28) and decreased ROM (1.03-1.25) predicted subjects with persistent milder symptoms from those who fully recovered. These results demonstrate that both physical and psychological factors play a role in recovery or non-recovery from whiplash injury. This may assist in the development of more relevant treatment methods for acute whiplash. (c) 2004 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

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Background: There is a recognized need to move from mortality to morbidity outcome predictions following traumatic injury. However, there are few morbidity outcome prediction scoring methods and these fail to incorporate important comorbidities or cofactors. This study aims to develop and evaluate a method that includes such variables. Methods: This was a consecutive case series registered in the Queensland Trauma Registry that consented to a prospective 12-month telephone conducted follow-up study. A multivariable statistical model was developed relating Trauma Registry data to trichotomized 12-month post-injury outcome (categories: no limitations, minor limitations and major limitations). Cross-validation techniques using successive single hold-out samples were then conducted to evaluate the model's predictive capabilities. Results: In total, 619 participated, with 337 (54%) experiencing no limitations, 101 (16%) experiencing minor limitations and 181 (29%) experiencing major limitations 12 months after injury. The final parsimonious multivariable statistical model included whether the injury was in the lower extremity body region, injury severity, age, length of hospital stay, pulse at admission and whether the participant was admitted to an intensive care unit. This model explained 21% of the variability in post-injury outcome. Predictively, 64% of those with no limitations, 18% of those with minor limitations and 37% of those with major limitations were correctly identified. Conclusion: Although carefully developed, this statistical model lacks the predictive power necessary for its use as a basis of a useful prognostic tool. Further research is required to identify variables other than those routinely used in the Trauma Registry to develop a model with the necessary predictive utility.

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Over 2 million Anterior Cruciate Ligament (ACL) injuries occur annually worldwide resulting in considerable economic and health burdens (e.g., suffering, surgery, loss of function, risk for re-injury, and osteoarthritis). Current screening methods are effective but they generally rely on expensive and time-consuming biomechanical movement analysis, and thus are impractical solutions. In this dissertation, I report on a series of studies that begins to investigate one potentially efficient alternative to biomechanical screening, namely skilled observational risk assessment (e.g., having experts estimate risk based on observations of athletes movements). Specifically, in Study 1 I discovered that ACL injury risk can be accurately and reliably estimated with nearly instantaneous visual inspection when observed by skilled and knowledgeable professionals. Modern psychometric optimization techniques were then used to develop a robust and efficient 5-item test of ACL injury risk prediction skill—i.e., the ACL Injury-Risk-Estimation Quiz or ACL-IQ. Study 2 cross-validated the results from Study 1 in a larger representative sample of both skilled (Exercise Science/Sports Medicine) and un-skilled (General Population) groups. In accord with research on human expertise, quantitative structural and process modeling of risk estimation indicated that superior performance was largely mediated by specific strategies and skills (e.g., ignoring irrelevant information), independent of domain general cognitive abilities (e.g., metal rotation, general decision skill). These cognitive models suggest that ACL-IQ is a trainable skill, providing a foundation for future research and applications in training, decision support, and ultimately clinical screening investigations. Overall, I present the first evidence that observational ACL injury risk prediction is possible including a robust technology for fast, accurate and reliable measurement—i.e., the ACL-IQ. Discussion focuses on applications and outreach including a web platform that was developed to house the test, provide a repository for further data collection, and increase public and professional awareness and outreach (www.ACL-IQ.org). Future directions and general applications of the skilled movement analysis approach are also discussed.