Are some brain injury patients improving more than ohers?

Predicting the evolution of individuals is a rather new mining task with applications in medicine. Medical researchers are interested in the progress of a disease and in the evolution of individuals subjected to treatment. We investigate the evolution of patients on the basis of medical tests before and during treatment after brain trauma: we want to understand how similar patients can become to healthy participants. We face two challenges. First, we have less information on healthy participants than on the patients. Second, the values of the medical tests for patients, even after treatment started, remain well-separated from those of healthy people; this is typical for neurodegenerative diseases, but also for further brain impairments. Our approach encompasses methods for modelling patient evolution and for predicting the health improvement of different patient subpopulations, dealing with the above challenges. We test our approach on a cohort of patients treated after brain trauma and a corresponding cohort of controls

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

Hyperglycemia in bacterial meningitis: a prospective cohort study

ABSTRACT: BACKGROUND: Hyperglycemia has been associated with unfavorable outcome in several disorders, but few data are available in bacterial meningitis. We assessed the incidence and significance of hyperglycemia in adults with bacterial meningitis. METHODS: We collected data prospectively between October 1998 and April 2002, on 696 episodes of community-acquired bacterial meningitis, confirmed by culture of CSF in patients >16 years. Patients were dichotomized according to blood glucose level on admission. A cutoff random non-fasting blood glucose level of 7.8 mmol/L (140 mg/dL) was used to define hyperglycemia, and a cutoff random non-fasting blood glucose level of 11.1 mmol/L (200 mg/dL) was used to define severe hyperglycemia. Unfavorable outcome was defined on the Glasgow outcome scale as a score <5. We also evaluated characteristics of patients with a preadmission diagnosis of diabetes mellitus. RESULTS: 69% of patients were hyperglycemic and 25% severely hyperglycemic on admission. Compared with non-hyperglycemic patients, hyperglycemia was related with advanced age (median, 55 yrs vs. 44 yrs, P<0.0001), preadmission diagnosis of diabetes (9% vs. 3%, P=0.005), and distant focus of infection (37% vs. 28%, P=0.02). They were more often admitted in coma (16% vs. 8%; P=0.004) and with pneumococcal meningitis (55% vs. 42%, P=0.007). These differences remained significant after exclusion of patients with known diabetes. Hyperglycemia was related with unfavorable outcome (in a hockey stick-shaped curve) but this relation did not remain robust in a multivariate analysis. Factors predictive for neurologic compromise were related with higher blood glucose levels, whereas factors predictive for systemic compromise were related with lower blood glucose levels. Only a minority of severely hyperglycemic patients were known diabetics (19%). The vast majority of these known diabetic patients had meningitis due to Streptococcus pneumoniae (67%) or Listeria monocytogenes (13%) and they were at high risk for unfavorable outcome (52%). CONCLUSIONS: The majority of patients with bacterial meningitis have hyperglycemic blood glucose levels on admission. Hyperglycemia can be explained by a physical stress reaction, the central nervous system insult leading to disturbed blood-glucose regulation mechanisms, and preponderance of diabetics for pneumococcal meningitis. Patients with diabetes and bacterial meningitis are at high risk for unfavorable outcom

Stratification of the severity of critically ill patients with classification trees

<p>Abstract</p> <p>Background</p> <p>Development of three classification trees (CT) based on the CART (<it>Classification and Regression Trees</it>), CHAID (<it>Chi-Square Automatic Interaction Detection</it>) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR).</p> <p>Methods</p> <p>Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%).</p> <p>Results</p> <p>CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69-75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)).</p> <p>Conclusion</p> <p>With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.</p

Control of hyperglycaemia in paediatric intensive care (CHiP): study protocol.

BACKGROUND: There is increasing evidence that tight blood glucose (BG) control improves outcomes in critically ill adults. Children show similar hyperglycaemic responses to surgery or critical illness. However it is not known whether tight control will benefit children given maturational differences and different disease spectrum. METHODS/DESIGN: The study is an randomised open trial with two parallel groups to assess whether, for children undergoing intensive care in the UK aged <or= 16 years who are ventilated, have an arterial line in-situ and are receiving vasoactive support following injury, major surgery or in association with critical illness in whom it is anticipated such treatment will be required to continue for at least 12 hours, tight control will increase the numbers of days alive and free of mechanical ventilation at 30 days, and lead to improvement in a range of complications associated with intensive care treatment and be cost effective. Children in the tight control group will receive insulin by intravenous infusion titrated to maintain BG between 4 and 7.0 mmol/l. Children in the control group will be treated according to a standard current approach to BG management. Children will be followed up to determine vital status and healthcare resources usage between discharge and 12 months post-randomisation. Information regarding overall health status, global neurological outcome, attention and behavioural status will be sought from a subgroup with traumatic brain injury (TBI). A difference of 2 days in the number of ventilator-free days within the first 30 days post-randomisation is considered clinically important. Conservatively assuming a standard deviation of a week across both trial arms, a type I error of 1% (2-sided test), and allowing for non-compliance, a total sample size of 1000 patients would have 90% power to detect this difference. To detect effect differences between cardiac and non-cardiac patients, a target sample size of 1500 is required. An economic evaluation will assess whether the costs of achieving tight BG control are justified by subsequent reductions in hospitalisation costs. DISCUSSION: The relevance of tight glycaemic control in this population needs to be assessed formally before being accepted into standard practice

Case Report Primary Clear Cell Chondrosarcoma of the Spine: A Case Report of a Rare Entity and a Review of the Literature

Chondrosarcoma is the third most common primary malignant bone tumor after osteosarcoma and Ewing&apos;s sarcoma. Clear cell chondrosarcoma is a rare subtype variant of chondrosarcoma, most commonly encountered in the proximal part of the femur or humerus. Vertebral involvement is exceedingly rare and shows a predilection for the thoracic spine. We report the case of a woman with clear cell chondrosarcoma of the thoracic spine, which has been surgically excised, and review the pertinent literature (PubMed). Although it has a reasonably benign biological behavior, clear cell chondrosarcoma needs to be treated as a malignancy. The best treatment for spinal chondrosarcoma is surgery. It should be promptly and adequately resected. Gross-total resection should be the ultimate surgical goal. Radiation therapy should also be considered, especially in the case of subtotal resection or inoperable lesions. In conclusion, it is important to keep in mind this entity in the differential diagnosis of spinal tumors, in order to optimize treatment planning. With adequate treatment, local recurrence rates as low as 20% can be achieved