Clinical Elements that Predict Outcome after Traumatic Brain Injury: A Prospective Multicenter Recursive Partitioning (Decision-Tree) Analysis
Department of Physical Medicine and Rehabilitation, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. Journal of Neurotrauma
(Impact Factor: 3.71).
11/2005; 22(10):1040-51. DOI: 10.1089/neu.2005.22.1040
Traumatic brain injury (TBI) often presents clinicians with a complex combination of clinical elements that can confound treatment and make outcome prediction challenging. Predictive models have commonly used acute physiological variables and gross clinical measures to predict mortality and basic outcome endpoints. The primary goal of this study was to consider all clinical elements available concerning a survivor of TBI admitted for inpatient rehabilitation, and identify those factors that predict disability, need for supervision, and productive activity one year after injury. The Traumatic Brain Injury Model Systems (TBIMS) database was used for decision tree analysis using recursive partitioning (n = 3463). Outcome measures included the Functional Independence Measure(), the Disability Rating Scale, the Supervision Rating Scale, and a measure of productive activity. Predictor variables included all physical examination elements, measures of injury severity (initial Glasgow Coma Scale score, duration of post-traumatic amnesia [PTA], length of coma, CT scan pathology), gender, age, and years of education. The duration of PTA, age, and most elements of the physical examination were predictive of early disability. The duration of PTA alone was selected to predict late disability and independent living. The duration of PTA, age, sitting balance, and limb strength were selected to predict productive activity at 1 year. The duration of PTA was the best predictor of outcome selected in this model for all endpoints and elements of the physical examination provided additional predictive value. Valid and reliable measures of PTA and physical impairment after TBI are important for accurate outcome prediction.
Available from: Jerome Maller
- "The Glasgow Coma Scale (GCS) has been used almost exclusively to classify injury severity. Recent studies have suggested that post-traumatic amnesia (PTA) duration may be more strongly associated with cognitive and functional outcome than GCS (Brown et al., 2005). Few of these studies have reported on the association between neuropsychological performance and volumetric loss in specific brain areas. "
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ABSTRACT: There has been limited examination of the effect of brain pathology on subsequent function. The current study examined the relationships between regional variation in grey matter volume, age and cognitive impairment using a semi-automated image analysis tool. This study included 69 individuals with mild-to-severe TBI, 41 of whom also completed neuropsychological tests of attention, working memory, processing speed, memory and executive functions. A widespread reduction in grey matter volume was associated with increasing age. Regional volumes that were affected also related to the severity of injury, whereby the most severe TBI participants displayed the most significant pathology. Poorer retention of newly learned material was associated with reduced cortical volume in frontal, parietal, and occipital brain regions. In addition, poorer working memory and executive control performance was found for individuals with lower cortical volume in temporal, parietal, and occipital regions. These findings are largely in line with previous literature, which suggests that frontal, temporal, and parietal regions are integral for the encoding of memories into long-term storage, memory retrieval, and working memory. The present study suggests that automated image analysis methods may be used to explore the relationships between regional variation in grey matter volume and cognitive function following TBI.
Brain and Cognition 07/2013; 83(1):34-44. DOI:10.1016/j.bandc.2013.06.007 · 2.48 Impact Factor
Available from: PubMed Central
- "The duration of PTA alone was selected to predict late disability and independent living. The duration of PTA was the best predictor of outcome selected in this model for all endpoints with elements of the physical examination being of additional predictive value . "
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ABSTRACT: There are many published studies about loss of consciousness related to general trauma however works on loss of consciousness in respect to orthopedic injuries are scarce.
The aim of this study was to investigate whether loss of consciousness worsens the prognosis of patients with orthopedic injuries.
A retrospective cohort study of orthopedic traumas was performed in the university Hospital of Base in São José do Rio Preto. All accident victims with injuries of the extremities classified as Score 3 or 4 by the Abbreviated Injury Scale (AIS) were included in this observational quantitative study. Patients with minor injuries and injuries that did not involve the extremities were not included. The association of loss of consciousness at the scene of the accident with evolution to death was investigated. The t-test, chi-squared and Fisher exact tests, and relative risk were used for statistical analysis. An alpha error of 5% (p-value ≤ 0.05) was considered statistically significant.
A total of 245 patients with ages between 13 and 98 years old and a mean of 45.4 years had extremity AIS scores of 3 or 4. Of these, significantly more men (170 - p< 0.001) suffered this type of injury than women (71). Thirty-six (14.94%) of these patients lost consciousness compared to 205 (85.06%) who did not lose consciousness. The total death rate in this group of patients was 5.39%; 9 (25%) of the 36 patients who lost consciousness and 4 (1.95%) of the 205 who did not lose consciousness died (Fisher exact test: p-value = 0.0001 and relative risk = 12,813 - 95% confidence index: 4,166 to 39,408).
Loss of consciousness in patients with orthopedic injuries of the extremities is associated to a higher death rate.
The Open Orthopaedics Journal 12/2012; 6:590-2. DOI:10.2174/1874325001206010590
Available from: Alexis Marcano Cedeño
- "This platform has been included in the hospital clinical protocols since 2005 and at the moment of this analysis PREVIRNECÓ database stores 1120 patients, with a total of 183047 rehabilitation tasks executions. Different statistical methodologies and predictive data mining methods have been applied to predict clinical outcomes of rehabilitation of patients with ABI (Rughani et al., 2010; Ji, Smith, Huynh, & Najarian, 2009; Pang et al., 2007; Segal et al., 2006; Brown et al., 2005; Rovlias & Kotsou, 2004; Andrews et al., 2002). Most of these studies are focused in determining survival, predicting disability or the recovery of patients, and looking for the factors that are better at predicting the patient's condition after suffering an ABI. "
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ABSTRACT: a b s t r a c t Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is asso-ciated 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. Perfor-mance 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 treat-ment efficacy in individual patients.
Expert Systems with Applications 09/2012; 40(4). DOI:10.1016/j.eswa.2012.08.034 · 2.24 Impact Factor
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